Design Method of Intelligent Ropeway Type Line Changing Robot Based on Lifting Force Control and Synovial Film Controller

Jiazhen Duan; Ruxin Shi; Hongtao Liu; Hailong Rong

doi:10.1155/2022/3640851

Design Method of Intelligent Ropeway Type Line Changing Robot Based on Lifting Force Control and Synovial Film Controller

Duan, Jiazhen;Shi, Ruxin;Liu, Hongtao;Rong, Hailong 2022-04-12 00:00:00 Hindawi Journal of Robotics Volume 2022, Article ID 3640851, 11 pages https://doi.org/10.1155/2022/3640851 Research Article Design Method of Intelligent Ropeway Type Line Changing Robot Based on Lifting Force Control and Synovial Film Controller 1 2 1 3 Jiazhen Duan , Ruxin Shi , Hongtao Liu , and Hailong Rong State Grid Changzhou Power Supply Company, Transmission and Distribution Engineering Company, Changzhou, Jiangsu 213000, China State Grid Changzhou Power Supply Company, Oﬃce, Changzhou, Jiangsu 213000, China School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou, Jiangsu 213164, China Correspondence should be addressed to Hailong Rong; rhle_16@163.com Received 25 February 2022; Revised 25 March 2022; Accepted 30 March 2022; Published 12 April 2022 Academic Editor: Shan Zhong Copyright © 2022 Jiazhen Duan et al. ,is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Aiming at the problems of low eﬃciency, reliability, and safety of manual construction for demolition of old lines, a design method of an intelligent ropeway type line changing robot based on lifting force control and synovial ﬁlm controller is proposed. First, the mechanical model of robot load and line sag is established, and the sag of the overhead line where the robot is located is used to calculate the jacking force that the jacking device needs to provide to the robot. ,en, by introducing the radial basis function (RBF) neural network adaptive algorithm into the synovial controller, an adaptive sliding mode position control algorithm based on the RBF neural network is designed to achieve high-precision motion control of the robot in complex operating environments. Finally, based on the compactness, weight, and reliability of the robot, the optimal design is carried out from four aspects of topology, size, shape and morphology, and the design scheme of the robot for wire removal is proposed, and the robot is produced. ,e developed robot and the other three robots are compared and analyzed under the same conditions through simulation experiments. ,e results show that the maximum operating time, maximum climbing angle, and maximum traveling speed of the robot developed in this study are all optimal, which are 45 min, 10 , and 1 m/s respectively, and the performance is better than the other three comparison algorithms. 1. Introduction 2. Related Work Using robots instead of manual construction of the foot With the continuous growth of the national electricity de- frame and completion of the loosening of wire and guide mand, more and more transmission lines are put into use. At wire back pumping procedures after upping the tower can the same time, there are a large number of old lines. Old lines improve the eﬃciency of demolition construction and re- may aﬀect the safe operation of the power system, personal duce the construction diﬃculty and cost. It plays a signif- safety, and property safety [1–3]. At present, the demolition icant role in improving the overall technical level of power of the old lines of the power grid is basically completed by line construction [9, 10]. dos Santos [11] developed a rope- manual work, and there are many problems such as long climbing robot that can move on distribution lines and the construction time, high construction cost, and wide inﬂu- corresponding motion planning to avoid collisions with ence range [4, 5]. In view of the above problems, using robots insulators and other devices. It designs a geometric motion to replace humans to complete construction work such as planning control method by using the quintic polynomial overhead line demolition and replacement has broad ap- interpolation method, so that the robot articulated sus- plication scenarios and development space and is currently a pension can retract when approaching obstacles and expand research hotspot in the industry [6–8]. 2 Journal of Robotics neural network into the synovial controller, an adaptive after crossing obstacles. However, this method does not analyze the accurate control of robot motion speed. In algorithm which can accurately control the robot motion is proposed. Compared with traditional detection view of the diﬃculty in calculating the drop of power lines, Zengin et al. [12] proposed a new method to accurately methods, the innovations of the proposed method are measure the drop of transmission lines based on the listed: power line inspection robot and by using the sensors (1) A new method is proposed to calculate the lifting carried by the robot to collect data and send it remotely. force of the robot. ,e lifting force required by the However, this method does not study the motion control robot is calculated based on the vertical radian of the method of the robot itself. In view of the problem of poor position of the overhead line where the robot is battery life of the live working robot, Jiang et al. [13] located and the attitude sensor constructed a method to optimize the motion energy (2) ,e RBF neural network is introduced into the sy- consumption of the robot arm. It adapts the genetic al- novial controller, which greatly improves the pre- gorithm and selects the appropriate algorithm parameters cision of robot motion control in complex operating to solve the optimal motion planning of the robot energy environment consumption, which improves the operation eﬃciency of the robot. However, this method only takes the lowest (3) On the basis of not increasing the weight of the energy consumption as the objective function and has robot, the reliability of the robot is improved by limitations. Nguyen et al. [14] developed a robot for optimizing and adjusting the shape, position, and cleaning solar panels and simpliﬁed it. By establishing the quantity of diﬀerent structures control motion equation of the robot during driving and combining with the linear quadratic regulator, a scheme is 3. The Proposed Method proposed to ensure the stable movement of the system and track the desired trajectory. However, this method only 3.1. Automatic Control of Lifting Force Based on Vertical focuses on the control method of the robot motion path Radian of Overhead Line. When the robot moves on the wire, and cannot guarantee the accuracy of speed control. it needs certain climbing ability to ensure that the wheel will Aiming at the diﬃculties and high risks of power grid fault not slide at any time as far as possible, which means that the detection, Zhao et al. [15] proposed a patrol robot that can wheel surface and overhead line are relatively static. ,is detect equipment faults and identify infrared images of requires that the friction force provided by the driving wheel faulty equipment based on infrared imaging technology and the ﬁxed wheel is enough to overcome the inﬂuence of the and support vector machine technology. Although the heavy torque and the pulling torque of the rope, so that the detection eﬃciency is enhanced, the accuracy of the robot can remain relatively static [18]. method using robots for fault identiﬁcation is not sig- Since the load of the robot is closely related to the sag niﬁcantly improved. Xie et al. [16] proposed an integrated of overhead line, the mechanical model of the load and sag 3D printing tube-climbing robot composed of an ordered of the robot should be established ﬁrst. ,e statics analysis new soft bending mechanism. ,e ﬁnite element method of the robot is the same in ascending and descending is used to predict the maximum bending angle of the stages. ,e following is an example of robot climbing. ,e module, and the output torque and recovery torque are static model of the robot when it goes uphill is shown in obtained by building a torque test bench. On this basis, the Figure 1. model of the whole robot is established. However, In Figure 1, M represents the mass of the robot, g the calculation process of this method is complicated, and represents the acceleration of gravity, v represents the the cost is too high to apply in practical engineering. Song speed of the robot, d represents the distance between the et al. [17] proposed an automatic inspection mechanical front and rear wheels of the robot, N and N , respec- f b diagnostic robot based on the fuzzy search method of tively, represent the positive pressure of the front and rear acoustic signals. ,e intelligent control of the inspection wheels of the robot on the line, F and F , respectively, fs bs path of the mechanical diagnostic robot is realized represent the static friction forces of the front and rear through rough sets, fuzzy neural network (FNN), and self- wheels of the robot, M and M , respectively, represent s1 s2 positioning azimuth correction, and it obtains navigation the braking torque applied by the front and rear wheels of and search method by using the extracted fault sound the robot, δ and δ , respectively, represent the static fs bs signal for fuzzy reasoning. However, this method does not friction coeﬃcients of the front and rear wheels of the analyze the control complexity and motion accuracy of robot, and M and M are, respectively, the rolling fs bs the robot itself. friction moments suﬀered by the front and rear wheels of Based on the above analysis, in view of the low eﬃ- the robot, whose values are very small and negligible. It ciency and high cost of the current old line dismantling can be regarded as M � M � 0. fs bs work, a design method of intelligent ropeway type line Combined with the studies [19, 20] on the dynamic changing robot based on lifting force control and synovial modeling of the shock contact phenomenon in a closed-loop ﬁlm controller is proposed. ,e basic idea is as follows: the robot chain and the kinetic models based on kinematic mechanical model of robot load and line sag is established control of ellipsoids and cubic nanoparticles, according to to analyze its force and the calculation method of lifting Figure 1, the static analysis equation of the robot when it is force required by the robot, and by introducing the RBF uphill can be obtained as follows. Journal of Robotics 3 M complementary ﬁltering algorithm or extended Kalman T1 algorithm, and the attitude quaternion characterizing the inclination of the transmission wire is obtained. Finally, the attitude quaternion is transformed into Euler angle (pitch T2 d angle, roll angle, and azimuth angle) to lay a foundation for fs fs the subsequent control of lifting force. ,e torque to be bs overcome by the driving wheel is shown in Figure 2. It can be seen from Figure 2 that the lifting force required bs to be provided by the two driving wheels is diﬀerent. After measuring the inclination of the wire, the friction force needed by the driving wheel and the ﬁxed wheel can be calculated combining with the weight of the robot and the real-time drag force of the tow rope, and the lifting force Mg needed by the robot can be calculated on this basis. ,e speciﬁc design scheme of the lifting force control Figure 1: Statics model of the robot going uphill. system is shown in Figure 3. ,e entire jacking force control system adopts a single closed-loop control method. ,e control system takes the torque calculation result at the ⎧ ⎪ M + M − N d + Mgh sin α + Mgd cos α � 0, contact point between the driving wheel and the power line ⎪ T1 T2 f 0 0 0 as feedback, takes the torque exerted by the driving wheel as ⎪ the reference, and the diﬀerence between the feedback and F + F − Mg sin α � 0, fs bs ⎪ the reference as the input of the controller, and the output of the controller directly controls the brushless DC. ,e output (1) ⎪ N + N − Mg cos α � 0, ⎪ f b of the motor ﬁnally achieves the purpose of indirectly adjusting the force of the driving wheel through the electric ⎪ M � F r , cylinder. ,e two driving wheels share a set of torque cal- T1 fs w ⎪ culation modules, but the control systems are diﬀerent. ,e design of the controller is one of the key points. First, the M � F r . T2 bs w mathematical model of each part of the brushless DC motor In (1), r is the angular velocity of the robot wheel. and electric cylinder is constructed, and then, the mathe- It can be seen from (1) that in the process of wire matical model of the controller is constructed by using the climbing, when the driving torque is constant, the running automatic control theory, and then, the stability of the state of the robot is mainly aﬀected by friction. ,e dip angle controller is analyzed by the Lyapunov method, and other related control theories are used. ,e robustness and of the transmission wire and the support force provided by the lifting device to the driving wheel are two main factors adaptability of the controller are analyzed, and ﬁnally, a robust controller is designed. aﬀecting the friction. ,erefore, the lifting force required by the lifting device can be calculated by detecting the vertical ,e entire lifting force control system adopts a single closed-loop control method. ,e control system takes the radian of the position of the overhead wire where the robot is located. torque calculation result at the contact point between the For the robot that adopts the lifting grasping principle driving wheel and the power line as the feedback and takes with ﬁxed stroke, the friction between the driving wheel and the torque exerted by the driving wheel as the input, and the overhead line is constant and cannot be changed according diﬀerence between the feedback and the input is the input of to the change of load, so the adaptability is poor [21, 22]. the controller. Since the output of the controller can directly When the load of the robot is large, because the friction force control the output of the brushless DC motor, the electric cylinder can indirectly adjust the force applied by the driving is not enough to keep the driving wheel and the wire rel- atively still, it will aﬀect the traveling speed or even cannot wheel. ,e two driving wheels share a set of torque calculation climb the slope. In addition, when downhill or under a small load, excessive friction will lead to waste of power and can modules, and the diﬀerence is that their control systems are diﬀerent. ,e design process of the controller consists of 4 aﬀect the duration of continuous operation of the robot. Because there are certain diﬀerences in the sag of diﬀerent steps: overhead lines, it is necessary to detect the inclination of the (1) Build the mathematical model of each part of the wire before calculating the power required for the robot to brushless DC motor and electric cylinder move and control the pressure output of the lifting mech- (2) Use automatic control theory to build the mathe- anism to adapt to the current load condition. matical model of the controller In order to solve the above problems, the attitude sensor (3) Use the Lyapunov method to analyze the stability of is used to sense the own attitude of robot so as to obtain the the controller inclination of the wire indirectly [23, 24]. ,e outputs of (4) Use other related control theories to analyze the these sensors are calibrated by the low-power processor in the sensor, and then, these outputs are fused by the robustness and adaptability of the controller Robot body 4 Journal of Robotics robot gravity traction Figure 2: ,e torque to be overcome by the driving wheel. Accelerometer controller Geomagnetic Error sensor correction Input voltage of brushless DC motor Gyro Complementary Filtering Electric Algorithm/Extended Kalman cylinder Algorithm Torque exerted by Traction Calculate the force moment of the driving wheel Tension contact between the driving Gauge wheel and the line Figure 3: ,e design scheme of the lifting force control system. 3.2. Adaptive Speed Control Based on Neural Network robot is large, its traveling speed will be very slow, which will Synovial Controller. When the robot moves along the aﬀect the construction eﬃciency [25, 26]. ,erefore, it is overhead line, the vertical radian of the overhead line and the necessary to design a more accurate controller to control the length of the traction rope change all the time. In addition, walking speed of the robot. In this study, the speed of the brushless DC motor is the operating environment is very complicated due to the slippery circuit caused by rain or the swing caused by wind. controlled by using a synovial controller, so as to achieve ,e complex environment will cause the robot load to accurate control of robot walking speed. ,e synovial change constantly. For the robot controller that realizes control method for system parameter variations and external speed control through the PID controller, it cannot achieve disturbances has good robustness and complete adaptability. high-precision speed control in a time-varying environment In practical applications, due to the back-and-forth of load due to its low degree of freedom: when the load of the switching of the control action, the inertia and delay of the robot is small, its traveling speed will be fast, which will aﬀect system, and the measurement error and other inﬂuencing the fastening installation of the hook. When the load of the factors, the structure control will appear high-frequency Journal of Robotics 5 chattering in the sliding mode, which seriously aﬀects the requirements. At this time, the sliding mode surface control performance of the system. It is diﬃcult to solve the switching function is set as above problems only by improving the synovial control s � x + cx , c> 0. (5) 2 1 method. ,ere will be a static error, and the implementation process of the high-order sliding mode control algorithm is T In (5), x � [x , x ] represents the input of the neural 1 2 very complicate. So, it is diﬃcult to apply in practice. network, and c is a constant. ,is study considers the application of adaptive control ,e neural network adaptive sliding mode controller idea. Combining the radial basis function (RBF) neural system structure can be divided into three parts: sliding network adaptive algorithm with sliding mode variable mode variable structure controller, RBF network, and structure control, it designs the corresponding RBF neural adaptive law. network adaptive sliding mode position control algorithm ,e input of the neural network will continuously and ﬁnally realizes the high-precision motion control of the change the size of the weights after learning by the neural robot in the above complex operation environment. ,e network, so that the output function f(x) approximates the RBF neural network is an advanced intelligent control al- 􏽢 ideal nonlinear functionf(x). ,e output f(x) of the RBF gorithm, which has strong self-learning, self-adaptation, and network and the ideal nonlinear function f(x) are shown in self-organization functions, and has a good application the following equations, respectively. prospect in dealing with nonlinear and uncertain problems of control systems. In addition, the RBF neural network has (6) f(x) � W h (x). good approximation ability, simple network structure, and fast learning ability. f(x) � (a + Δa)θ − (a + Δa)x + (z + Δz). (7) d 2 ,e main body of the synovial controller based on the RBF neural network is the synovial controller. By intro- In (6), h (x) is the Gaussian function of the RBF neural ducing the RBF neural network into the synovial controller, network and W represents the weighted vector. the synovial surface switching function of the controller is In order to further improve the chattering problem of the adjusted, and the external load disturbance component is adaptive sliding mode of the RBF network, the reaching law added to the switching function. ,erefore, the synovial is optimized, and the optimized reaching law is shown as controller generated by modifying the switching function _ (8) s � − μs |s|sgns − βs, μ> 0, β> 0. with the neural network becomes an adaptive synovial controller. Once the external disturbance due to the external In (8), the reaching law can be divided into power part environment change, the external disturbance will make a and exponential part. When the distance between the sharp change in the switch function of the synovial surface, moving point of the control system and the sliding mode so that the adaptive synovial controller can respond to the surface is large, the value of s is large. In this case, the external disturbance quickly and adjust the input current of exponential part and the power part work simultaneously the brushless DC motor timely; thus, the speed control and the approaching speed is fast. When s ⟶ 0, the power accuracy is greatly improved. part tends to zero, and only the exponential part plays a role. In order to improve the position control accuracy of the ,e jitter caused by the sign function sgns will diminish with brushless DC motor, the following torque balance equation the decrease of the power part. ,erefore, the design of is given, considering the changes of internal parameters and approach law not only ensures the convergence speed but external loads. also makes the dynamic response of the control system more ω _ � − (a + Δa)ω + (b + Δb)i − (z + Δz). (2) stable. Finally, the control law of the system can be obtained as In (2), a � B/J, b � KT/J, z � TL/J. Δa, Δb, and Δz, € 􏽢 respectively, represent the disturbance variation caused by (9) i � 􏽨θ + f(x) + cx + μs |s|sgns + βs􏽩. the disturbance of the internal parameters of the system and the disturbance of the external load. Here, the gradient descent method is used to learn the In order to make the corresponding angle θ of the RBF neural network. If the learning rate η is set to a ﬁxed position controller track the set angle θ faster, the position 􏽢 value in the process of weight W update, it will cause tracking error of the controller can be expressed as problems such as low learning eﬃciency and slow con- vergence speed. ,erefore, the adaptive learning rate is e(t) � x � θ − θ. (3) 1 d used to adjust the learning rate online, which can speed up the learning rate, while ensuring the stability of the system At this time, the following equation is established. and the stability of the learning process. ,e recursive error _ _ ⎧ ⎪ e(t) � x � θ − θ, 2 d is € € (4) e_(t) � x _ � θ − θ, ⎪ 2 d 1 1 2 2 E(k) � y(k) − y(k − 1) . (10) € _ � θ + (a + Δa)θ − (a + Δa)x − (b + Δb)i + (z + Δz). d d 2 2 2 It can be seen from (4) that there is e_ ⟶ 0 when ,e rules for adjusting the learning rate according to the e ⟶ 0, so the position controller meets the design size of the recursive error are as 6 Journal of Robotics c × η(k − 1), (k)< E(k − 1), ⎧ ⎪ 1 θ ⎪ θ d i SMC BLDCM c × η(k − 1), c × η(k − 1)< E(k), (11) 2 3 η(k − 1), else. f (x) In (11), c , c , and c are the proportional constants. RBF Network 1 2 3 ,us, the speciﬁc design scheme of the controller can be obtained as shown in Figure 4. In Figure 4, SMC represents the synovial controller, Adaptive Law BLDCM represents the brushless DC motor, the RBF net- Figure 4: Design scheme of synovial controller based on the neural work represents the radial basis function neural network, network. and adaptive law represents the law of adaptive adjustment. parameters of robot parts such as thickness of plate and section area of pillar. 3.3. Design of Line Changing Robot. ,e design of the line Shape optimization: based on the designed robot to- changing robot needs to ensure the following three technical pology, the geometry of diﬀerent parts is optimized to indicators: improve the strength of parts. (1) Compact structure: the uncompact structure of the Morphology optimization: based on the weight of the designed robot, after ﬁxing the weight, adjust the shape, robot aﬀects the ﬂexibility of its movement and reduces the work eﬃciency position, and quantity of diﬀerent concave-convex struc- tures and optimize the stiﬀness and mode of sheet metal (2) Weight size: the weight of the robot will increase the structural parts, so as to eliminate potential weak links and time and energy consumption of the tower up and improve the robot the purpose of reliability. down, and the high energy consumption during the ,e main process of robot design includes software and wiring, and prolong the continuous working time. hardware design. ,e hardware design includes the four (3) Reliability: improving the reliability of robot oper- aspects: ation is very important for safety and maintenance cost reduction in engineering applications (1) Hardware design of robot ontology structure (2) Hardware design of the robot control system (4) Mobility: the mobility of a robot aﬀects its adapt- ability to the operating environment (3) ,e design of the main control module and the peripheral basic hardware circuit (5) Load capacity: in order to ensure the working ability of the robot, the robot also needs to bear the (4) Hardware circuit design maximum load ≥5 kg in addition to carrying its own ,e speciﬁc design content is given in Table 1. weight ,e hardware design drawing and the line changing (6) Speed requirements: the robot should improve its robot obtained are shown in Figures 5 and 6, respectively. maximum moving speed as far as possible under the ,e software design mainly includes the realization of premise of ensuring stability and reliability and re- the robot control system, the display of robot running duce the inﬂuence of external factors such as wind on state, and the realization of robot remote control. ,e work eﬃciency control ﬂowchart of the line changing robot is shown in (7) Self-protection ability: in order to prevent the robot Figure 7. ,e workﬂow of robot mainly includes the from falling, it should have certain safety self-pro- following steps: tection measures (1) First initialize the robot before work, start the Aiming at the above seven indexes, the optimized design subroutine receiving instructions, and connect it is mainly carried out from four aspects of topology opti- with the ground control device mization, size optimization, shape optimization, and mor- (2) ,e robot starts the synovial controller based on the phology optimization. RBF neural network to realize the adaptive speed Topology optimization: for the critical path of load, control at work aluminum alloy polypropylene composite lightweight (3) ,e robot starts the subroutine that collects and laminate is used. For the noncritical path, the polymer plate sends its own state and the subroutine that collects is used to minimize the bodyweight while ensuring the and sends video and sends the above data infor- payload. Designing based on the payload transfer path and mation to the ground receiving device optimal material distribution can improve the overall structure and reduce the overall design cost. (4) ,e robot receives the instruction from the ground and judges the type of the instruction and executes it Dimension optimization: the volume of robot parts is set as the objective function, and the combination of optimal until it stops working after receiving the discon- nected instruction design parameters is calculated based on dimension Journal of Robotics 7 Table 1: ,e design content of the line changing robot. Content Purpose 1 Simplify Reduce structural complexity 2 Redundancy Redundancy of key structural parameters, functional realization 3 Derating Reduce the failure rate of components 4 Components outline Control and manage electronic components and mechanical parts 5 Critical and important Utilize existing resources to improve the reliability of key and important parts Environmental Choose appropriate materials or solutions to requirements of waterproof, electromagnetic protection, protection ambient temperature, and humidity. By adopting N-version programming and implementing software engineering and speciﬁcations to improve 7 Software reliability critical reliability Packaging, shipping, 8 Determine packaging protection measures, meet shipping, storage requirements storage 9 Ergonomics Make the equipment easy to operate and maintain In the process of practical application, a number of Pressure performances of the robot are tested. It measures and an- alyzes the indicators including the operation success rate of wheel wheel the line changing robot, the online moving speed, the speed control accuracy, the climbing angle, the continuous op- eration time, and the environmental adaptation. wheel wheel ,e experimental results are given in Table 2. ,e experimental results show that the robot can move Pressure up to 1 m/s, remote control distance up to 1 km, climbing angle up to 10 , continuous operation time up to 45 minutes, and waterproof grade up to 6 on 220 kV and below overhead line. ,e adaptability of the environment is as follows: it can work normally in the environment, in which the wind is level 5 or less, and the precipitation is 8 ml or less per hour, and the temperature range is − 10–40 C. In addition, the pro- posed intelligent ropeway type line changing robot based on lifting force control and synovial ﬁlm controller does not need to build scaﬀolding and cannot hinder the normal operation of the facilities to be crossed. Compared with on- site construction, the time cost is shortened from days to hours, and the process is fast, eﬃcient, simple, safe, and reliable. It can be reused after a one-time investment, which Figure 5: Hardware design of line changing robot. greatly reduces the cost. 4.2. Performance Comparison Analysis. Since the main ex- ternal factor aﬀecting the high-altitude operation of robot is the wind, the robot developed in this study and the robot developed in [11, 13, 14] are, respectively, compared and analyzed in terms of operation duration, climbing angle, and traveling speed of the robot under diﬀerent winds. ,e relationship between the maximum operating time, maximum climbing angle, and maximum traveling speed of diﬀerent robots and wind power is shown in Figures 9–11, respectively. It can be seen from Figures 9–11 that the developed robot has the largest working time, forward speed, and climbing Figure 6: Actual object of line changing robot. angle compared with other robots, no matter in no wind or in diﬀerent wind levels. It indicates that the developed robot is better able to work outdoors at high altitude. ,e reason is 4. Experiment and Analysis that the sliding mode controller is used to control the robot, 4.1. Practical Application Experiment. Some experiments are and the RBF neural network is introduced in it, which can carried out on the 220 kV overhead transmission line for the self-learn, self-adapt, and self-organize to achieve high- designed line changing robot. It is shown in Figure 8. precision motion control of the robot in the complex 8 Journal of Robotics START Initialize the robot Start the subroutine that receives the command Receive a connection to the ground operating system Start a subroutine that collects and sends robot status Start a subroutine that captures and sends video data Start the robot precise control subroutine Determine the type of instruction and execute it NO Disconnect? YES terminate all subroutines END Figure 7: Control ﬂowchart of line changing robot. operation environment. However, the robot developed in good endurance without considering the external environ- [11] does not take into account the accurate control of the ment, but does not consider the performance of other as- robot movement speed. ,e robot developed in [13] has pects of the robot. ,e robot developed in [14] Journal of Robotics 9 Figure 8: Practical application experiment of line changing robot. Table 2: ,e experimental data of the line changing robot. Number Runtime (min) Success rate (%) Velocity (m/s) Climbing angle Wind Temperature 1 28 100 0.5 5 2 − 3 C 2 30 100 0.6 6 2 − 5 C 3 32 100 0.8 5 2 − 10 C 4 34 100 1.0 7 3 30 C 5 36 100 0.7 8 4 40 C 6 37 100 0.6 8 5 − 7 C 7 40 100 0.8 9 5 − 8 C 8 42 100 1.0 10 4 20 C 9 44 100 1.0 10 5 − 10 C 10 45 100 1.0 10 3 40 C 0 12345 Wind Level Proposed method Ref.[13] Ref.[11] Ref.[14] Figure 9: ,e relationship between the maximum runtime of diﬀerent robots and the wind level. Maximum Runtime / min 10 Journal of Robotics potential weak links and improve the reliability of the robot. In the future, further research will be conducted on the battery module of the line changing robot. ,erefore, in the future research on related robots, the RBF neural network can be introduced into the control module to improve the control stability of the robot, and the reliability of the robot 6 can be improved through reasonable structural adjustment and optimization. On the basis of ensuring the good function of the robot, the endurance time of the robot can be extended as far as possible to enhance the ability of its continuous work. 0 12345 Data Availability Headwind Level ,e data used to support the ﬁndings of this study are Proposed method Ref.[13] available from the corresponding author upon request. Ref.[11] Ref.[14] Figure 10: ,e relationship between the maximum climbing angle Conflicts of Interest of diﬀerent robots and the headwind level. ,e authors declare that they have no conﬂicts of interest. Acknowledgments ,is work was supported by State Grid Jiangsu Electric 0.8 Power Co., Ltd. (Incubate project: Research on the Intelli- gent Cable Method-Based Robot Used for Power Line Re- 0.6 moving and Changing under grant JF2021022). 0.4 References 0.2 [1] J. Yerramsetti, D. S. Paritala, and R. Jayaraman, “Design and implementation of automatic robot for ﬂoating solar panel cleaning system using AI technique,” in Proceedings of the 0 12345 11th International Conference of Computer Communication Headwind Level and Informatics (ICCCI), pp. 105–110, Sri Shakthi Institute of Proposed method Ref.[13] Engineeing and Technology, Coimbatore, India, 2021. Ref.[11] Ref.[14] [2] D. Hruby, ´ D. Marko, M. Olejar, ´ V. Cviklovic, ˇ and D. 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Publisher: Hindawi Publishing Corporation
Copyright: Copyright © 2022 Jiazhen Duan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ISSN: 1687-9600
eISSN: 1687-9619
DOI: 10.1155/2022/3640851
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Abstract

Hindawi Journal of Robotics Volume 2022, Article ID 3640851, 11 pages https://doi.org/10.1155/2022/3640851 Research Article Design Method of Intelligent Ropeway Type Line Changing Robot Based on Lifting Force Control and Synovial Film Controller 1 2 1 3 Jiazhen Duan , Ruxin Shi , Hongtao Liu , and Hailong Rong State Grid Changzhou Power Supply Company, Transmission and Distribution Engineering Company, Changzhou, Jiangsu 213000, China State Grid Changzhou Power Supply Company, Oﬃce, Changzhou, Jiangsu 213000, China School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou, Jiangsu 213164, China Correspondence should be addressed to Hailong Rong; rhle_16@163.com Received 25 February 2022; Revised 25 March 2022; Accepted 30 March 2022; Published 12 April 2022 Academic Editor: Shan Zhong Copyright © 2022 Jiazhen Duan et al. ,is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Aiming at the problems of low eﬃciency, reliability, and safety of manual construction for demolition of old lines, a design method of an intelligent ropeway type line changing robot based on lifting force control and synovial ﬁlm controller is proposed. First, the mechanical model of robot load and line sag is established, and the sag of the overhead line where the robot is located is used to calculate the jacking force that the jacking device needs to provide to the robot. ,en, by introducing the radial basis function (RBF) neural network adaptive algorithm into the synovial controller, an adaptive sliding mode position control algorithm based on the RBF neural network is designed to achieve high-precision motion control of the robot in complex operating environments. Finally, based on the compactness, weight, and reliability of the robot, the optimal design is carried out from four aspects of topology, size, shape and morphology, and the design scheme of the robot for wire removal is proposed, and the robot is produced. ,e developed robot and the other three robots are compared and analyzed under the same conditions through simulation experiments. ,e results show that the maximum operating time, maximum climbing angle, and maximum traveling speed of the robot developed in this study are all optimal, which are 45 min, 10 , and 1 m/s respectively, and the performance is better than the other three comparison algorithms. 1. Introduction 2. Related Work Using robots instead of manual construction of the foot With the continuous growth of the national electricity de- frame and completion of the loosening of wire and guide mand, more and more transmission lines are put into use. At wire back pumping procedures after upping the tower can the same time, there are a large number of old lines. Old lines improve the eﬃciency of demolition construction and re- may aﬀect the safe operation of the power system, personal duce the construction diﬃculty and cost. It plays a signif- safety, and property safety [1–3]. At present, the demolition icant role in improving the overall technical level of power of the old lines of the power grid is basically completed by line construction [9, 10]. dos Santos [11] developed a rope- manual work, and there are many problems such as long climbing robot that can move on distribution lines and the construction time, high construction cost, and wide inﬂu- corresponding motion planning to avoid collisions with ence range [4, 5]. In view of the above problems, using robots insulators and other devices. It designs a geometric motion to replace humans to complete construction work such as planning control method by using the quintic polynomial overhead line demolition and replacement has broad ap- interpolation method, so that the robot articulated sus- plication scenarios and development space and is currently a pension can retract when approaching obstacles and expand research hotspot in the industry [6–8]. 2 Journal of Robotics neural network into the synovial controller, an adaptive after crossing obstacles. However, this method does not analyze the accurate control of robot motion speed. In algorithm which can accurately control the robot motion is proposed. Compared with traditional detection view of the diﬃculty in calculating the drop of power lines, Zengin et al. [12] proposed a new method to accurately methods, the innovations of the proposed method are measure the drop of transmission lines based on the listed: power line inspection robot and by using the sensors (1) A new method is proposed to calculate the lifting carried by the robot to collect data and send it remotely. force of the robot. ,e lifting force required by the However, this method does not study the motion control robot is calculated based on the vertical radian of the method of the robot itself. In view of the problem of poor position of the overhead line where the robot is battery life of the live working robot, Jiang et al. [13] located and the attitude sensor constructed a method to optimize the motion energy (2) ,e RBF neural network is introduced into the sy- consumption of the robot arm. It adapts the genetic al- novial controller, which greatly improves the pre- gorithm and selects the appropriate algorithm parameters cision of robot motion control in complex operating to solve the optimal motion planning of the robot energy environment consumption, which improves the operation eﬃciency of the robot. However, this method only takes the lowest (3) On the basis of not increasing the weight of the energy consumption as the objective function and has robot, the reliability of the robot is improved by limitations. Nguyen et al. [14] developed a robot for optimizing and adjusting the shape, position, and cleaning solar panels and simpliﬁed it. By establishing the quantity of diﬀerent structures control motion equation of the robot during driving and combining with the linear quadratic regulator, a scheme is 3. The Proposed Method proposed to ensure the stable movement of the system and track the desired trajectory. However, this method only 3.1. Automatic Control of Lifting Force Based on Vertical focuses on the control method of the robot motion path Radian of Overhead Line. When the robot moves on the wire, and cannot guarantee the accuracy of speed control. it needs certain climbing ability to ensure that the wheel will Aiming at the diﬃculties and high risks of power grid fault not slide at any time as far as possible, which means that the detection, Zhao et al. [15] proposed a patrol robot that can wheel surface and overhead line are relatively static. ,is detect equipment faults and identify infrared images of requires that the friction force provided by the driving wheel faulty equipment based on infrared imaging technology and the ﬁxed wheel is enough to overcome the inﬂuence of the and support vector machine technology. Although the heavy torque and the pulling torque of the rope, so that the detection eﬃciency is enhanced, the accuracy of the robot can remain relatively static [18]. method using robots for fault identiﬁcation is not sig- Since the load of the robot is closely related to the sag niﬁcantly improved. Xie et al. [16] proposed an integrated of overhead line, the mechanical model of the load and sag 3D printing tube-climbing robot composed of an ordered of the robot should be established ﬁrst. ,e statics analysis new soft bending mechanism. ,e ﬁnite element method of the robot is the same in ascending and descending is used to predict the maximum bending angle of the stages. ,e following is an example of robot climbing. ,e module, and the output torque and recovery torque are static model of the robot when it goes uphill is shown in obtained by building a torque test bench. On this basis, the Figure 1. model of the whole robot is established. However, In Figure 1, M represents the mass of the robot, g the calculation process of this method is complicated, and represents the acceleration of gravity, v represents the the cost is too high to apply in practical engineering. Song speed of the robot, d represents the distance between the et al. [17] proposed an automatic inspection mechanical front and rear wheels of the robot, N and N , respec- f b diagnostic robot based on the fuzzy search method of tively, represent the positive pressure of the front and rear acoustic signals. ,e intelligent control of the inspection wheels of the robot on the line, F and F , respectively, fs bs path of the mechanical diagnostic robot is realized represent the static friction forces of the front and rear through rough sets, fuzzy neural network (FNN), and self- wheels of the robot, M and M , respectively, represent s1 s2 positioning azimuth correction, and it obtains navigation the braking torque applied by the front and rear wheels of and search method by using the extracted fault sound the robot, δ and δ , respectively, represent the static fs bs signal for fuzzy reasoning. However, this method does not friction coeﬃcients of the front and rear wheels of the analyze the control complexity and motion accuracy of robot, and M and M are, respectively, the rolling fs bs the robot itself. friction moments suﬀered by the front and rear wheels of Based on the above analysis, in view of the low eﬃ- the robot, whose values are very small and negligible. It ciency and high cost of the current old line dismantling can be regarded as M � M � 0. fs bs work, a design method of intelligent ropeway type line Combined with the studies [19, 20] on the dynamic changing robot based on lifting force control and synovial modeling of the shock contact phenomenon in a closed-loop ﬁlm controller is proposed. ,e basic idea is as follows: the robot chain and the kinetic models based on kinematic mechanical model of robot load and line sag is established control of ellipsoids and cubic nanoparticles, according to to analyze its force and the calculation method of lifting Figure 1, the static analysis equation of the robot when it is force required by the robot, and by introducing the RBF uphill can be obtained as follows. Journal of Robotics 3 M complementary ﬁltering algorithm or extended Kalman T1 algorithm, and the attitude quaternion characterizing the inclination of the transmission wire is obtained. Finally, the attitude quaternion is transformed into Euler angle (pitch T2 d angle, roll angle, and azimuth angle) to lay a foundation for fs fs the subsequent control of lifting force. ,e torque to be bs overcome by the driving wheel is shown in Figure 2. It can be seen from Figure 2 that the lifting force required bs to be provided by the two driving wheels is diﬀerent. After measuring the inclination of the wire, the friction force needed by the driving wheel and the ﬁxed wheel can be calculated combining with the weight of the robot and the real-time drag force of the tow rope, and the lifting force Mg needed by the robot can be calculated on this basis. ,e speciﬁc design scheme of the lifting force control Figure 1: Statics model of the robot going uphill. system is shown in Figure 3. ,e entire jacking force control system adopts a single closed-loop control method. ,e control system takes the torque calculation result at the ⎧ ⎪ M + M − N d + Mgh sin α + Mgd cos α � 0, contact point between the driving wheel and the power line ⎪ T1 T2 f 0 0 0 as feedback, takes the torque exerted by the driving wheel as ⎪ the reference, and the diﬀerence between the feedback and F + F − Mg sin α � 0, fs bs ⎪ the reference as the input of the controller, and the output of the controller directly controls the brushless DC. ,e output (1) ⎪ N + N − Mg cos α � 0, ⎪ f b of the motor ﬁnally achieves the purpose of indirectly adjusting the force of the driving wheel through the electric ⎪ M � F r , cylinder. ,e two driving wheels share a set of torque cal- T1 fs w ⎪ culation modules, but the control systems are diﬀerent. ,e design of the controller is one of the key points. First, the M � F r . T2 bs w mathematical model of each part of the brushless DC motor In (1), r is the angular velocity of the robot wheel. and electric cylinder is constructed, and then, the mathe- It can be seen from (1) that in the process of wire matical model of the controller is constructed by using the climbing, when the driving torque is constant, the running automatic control theory, and then, the stability of the state of the robot is mainly aﬀected by friction. ,e dip angle controller is analyzed by the Lyapunov method, and other related control theories are used. ,e robustness and of the transmission wire and the support force provided by the lifting device to the driving wheel are two main factors adaptability of the controller are analyzed, and ﬁnally, a robust controller is designed. aﬀecting the friction. ,erefore, the lifting force required by the lifting device can be calculated by detecting the vertical ,e entire lifting force control system adopts a single closed-loop control method. ,e control system takes the radian of the position of the overhead wire where the robot is located. torque calculation result at the contact point between the For the robot that adopts the lifting grasping principle driving wheel and the power line as the feedback and takes with ﬁxed stroke, the friction between the driving wheel and the torque exerted by the driving wheel as the input, and the overhead line is constant and cannot be changed according diﬀerence between the feedback and the input is the input of to the change of load, so the adaptability is poor [21, 22]. the controller. Since the output of the controller can directly When the load of the robot is large, because the friction force control the output of the brushless DC motor, the electric cylinder can indirectly adjust the force applied by the driving is not enough to keep the driving wheel and the wire rel- atively still, it will aﬀect the traveling speed or even cannot wheel. ,e two driving wheels share a set of torque calculation climb the slope. In addition, when downhill or under a small load, excessive friction will lead to waste of power and can modules, and the diﬀerence is that their control systems are diﬀerent. ,e design process of the controller consists of 4 aﬀect the duration of continuous operation of the robot. Because there are certain diﬀerences in the sag of diﬀerent steps: overhead lines, it is necessary to detect the inclination of the (1) Build the mathematical model of each part of the wire before calculating the power required for the robot to brushless DC motor and electric cylinder move and control the pressure output of the lifting mech- (2) Use automatic control theory to build the mathe- anism to adapt to the current load condition. matical model of the controller In order to solve the above problems, the attitude sensor (3) Use the Lyapunov method to analyze the stability of is used to sense the own attitude of robot so as to obtain the the controller inclination of the wire indirectly [23, 24]. ,e outputs of (4) Use other related control theories to analyze the these sensors are calibrated by the low-power processor in the sensor, and then, these outputs are fused by the robustness and adaptability of the controller Robot body 4 Journal of Robotics robot gravity traction Figure 2: ,e torque to be overcome by the driving wheel. Accelerometer controller Geomagnetic Error sensor correction Input voltage of brushless DC motor Gyro Complementary Filtering Electric Algorithm/Extended Kalman cylinder Algorithm Torque exerted by Traction Calculate the force moment of the driving wheel Tension contact between the driving Gauge wheel and the line Figure 3: ,e design scheme of the lifting force control system. 3.2. Adaptive Speed Control Based on Neural Network robot is large, its traveling speed will be very slow, which will Synovial Controller. When the robot moves along the aﬀect the construction eﬃciency [25, 26]. ,erefore, it is overhead line, the vertical radian of the overhead line and the necessary to design a more accurate controller to control the length of the traction rope change all the time. In addition, walking speed of the robot. In this study, the speed of the brushless DC motor is the operating environment is very complicated due to the slippery circuit caused by rain or the swing caused by wind. controlled by using a synovial controller, so as to achieve ,e complex environment will cause the robot load to accurate control of robot walking speed. ,e synovial change constantly. For the robot controller that realizes control method for system parameter variations and external speed control through the PID controller, it cannot achieve disturbances has good robustness and complete adaptability. high-precision speed control in a time-varying environment In practical applications, due to the back-and-forth of load due to its low degree of freedom: when the load of the switching of the control action, the inertia and delay of the robot is small, its traveling speed will be fast, which will aﬀect system, and the measurement error and other inﬂuencing the fastening installation of the hook. When the load of the factors, the structure control will appear high-frequency Journal of Robotics 5 chattering in the sliding mode, which seriously aﬀects the requirements. At this time, the sliding mode surface control performance of the system. It is diﬃcult to solve the switching function is set as above problems only by improving the synovial control s � x + cx , c> 0. (5) 2 1 method. ,ere will be a static error, and the implementation process of the high-order sliding mode control algorithm is T In (5), x � [x , x ] represents the input of the neural 1 2 very complicate. So, it is diﬃcult to apply in practice. network, and c is a constant. ,is study considers the application of adaptive control ,e neural network adaptive sliding mode controller idea. Combining the radial basis function (RBF) neural system structure can be divided into three parts: sliding network adaptive algorithm with sliding mode variable mode variable structure controller, RBF network, and structure control, it designs the corresponding RBF neural adaptive law. network adaptive sliding mode position control algorithm ,e input of the neural network will continuously and ﬁnally realizes the high-precision motion control of the change the size of the weights after learning by the neural robot in the above complex operation environment. ,e network, so that the output function f(x) approximates the RBF neural network is an advanced intelligent control al- 􏽢 ideal nonlinear functionf(x). ,e output f(x) of the RBF gorithm, which has strong self-learning, self-adaptation, and network and the ideal nonlinear function f(x) are shown in self-organization functions, and has a good application the following equations, respectively. prospect in dealing with nonlinear and uncertain problems of control systems. In addition, the RBF neural network has (6) f(x) � W h (x). good approximation ability, simple network structure, and fast learning ability. f(x) � (a + Δa)θ − (a + Δa)x + (z + Δz). (7) d 2 ,e main body of the synovial controller based on the RBF neural network is the synovial controller. By intro- In (6), h (x) is the Gaussian function of the RBF neural ducing the RBF neural network into the synovial controller, network and W represents the weighted vector. the synovial surface switching function of the controller is In order to further improve the chattering problem of the adjusted, and the external load disturbance component is adaptive sliding mode of the RBF network, the reaching law added to the switching function. ,erefore, the synovial is optimized, and the optimized reaching law is shown as controller generated by modifying the switching function _ (8) s � − μs |s|sgns − βs, μ> 0, β> 0. with the neural network becomes an adaptive synovial controller. Once the external disturbance due to the external In (8), the reaching law can be divided into power part environment change, the external disturbance will make a and exponential part. When the distance between the sharp change in the switch function of the synovial surface, moving point of the control system and the sliding mode so that the adaptive synovial controller can respond to the surface is large, the value of s is large. In this case, the external disturbance quickly and adjust the input current of exponential part and the power part work simultaneously the brushless DC motor timely; thus, the speed control and the approaching speed is fast. When s ⟶ 0, the power accuracy is greatly improved. part tends to zero, and only the exponential part plays a role. In order to improve the position control accuracy of the ,e jitter caused by the sign function sgns will diminish with brushless DC motor, the following torque balance equation the decrease of the power part. ,erefore, the design of is given, considering the changes of internal parameters and approach law not only ensures the convergence speed but external loads. also makes the dynamic response of the control system more ω _ � − (a + Δa)ω + (b + Δb)i − (z + Δz). (2) stable. Finally, the control law of the system can be obtained as In (2), a � B/J, b � KT/J, z � TL/J. Δa, Δb, and Δz, € 􏽢 respectively, represent the disturbance variation caused by (9) i � 􏽨θ + f(x) + cx + μs |s|sgns + βs􏽩. the disturbance of the internal parameters of the system and the disturbance of the external load. Here, the gradient descent method is used to learn the In order to make the corresponding angle θ of the RBF neural network. If the learning rate η is set to a ﬁxed position controller track the set angle θ faster, the position 􏽢 value in the process of weight W update, it will cause tracking error of the controller can be expressed as problems such as low learning eﬃciency and slow con- vergence speed. ,erefore, the adaptive learning rate is e(t) � x � θ − θ. (3) 1 d used to adjust the learning rate online, which can speed up the learning rate, while ensuring the stability of the system At this time, the following equation is established. and the stability of the learning process. ,e recursive error _ _ ⎧ ⎪ e(t) � x � θ − θ, 2 d is € € (4) e_(t) � x _ � θ − θ, ⎪ 2 d 1 1 2 2 E(k) � y(k) − y(k − 1) . (10) € _ � θ + (a + Δa)θ − (a + Δa)x − (b + Δb)i + (z + Δz). d d 2 2 2 It can be seen from (4) that there is e_ ⟶ 0 when ,e rules for adjusting the learning rate according to the e ⟶ 0, so the position controller meets the design size of the recursive error are as 6 Journal of Robotics c × η(k − 1), (k)< E(k − 1), ⎧ ⎪ 1 θ ⎪ θ d i SMC BLDCM c × η(k − 1), c × η(k − 1)< E(k), (11) 2 3 η(k − 1), else. f (x) In (11), c , c , and c are the proportional constants. RBF Network 1 2 3 ,us, the speciﬁc design scheme of the controller can be obtained as shown in Figure 4. In Figure 4, SMC represents the synovial controller, Adaptive Law BLDCM represents the brushless DC motor, the RBF net- Figure 4: Design scheme of synovial controller based on the neural work represents the radial basis function neural network, network. and adaptive law represents the law of adaptive adjustment. parameters of robot parts such as thickness of plate and section area of pillar. 3.3. Design of Line Changing Robot. ,e design of the line Shape optimization: based on the designed robot to- changing robot needs to ensure the following three technical pology, the geometry of diﬀerent parts is optimized to indicators: improve the strength of parts. (1) Compact structure: the uncompact structure of the Morphology optimization: based on the weight of the designed robot, after ﬁxing the weight, adjust the shape, robot aﬀects the ﬂexibility of its movement and reduces the work eﬃciency position, and quantity of diﬀerent concave-convex struc- tures and optimize the stiﬀness and mode of sheet metal (2) Weight size: the weight of the robot will increase the structural parts, so as to eliminate potential weak links and time and energy consumption of the tower up and improve the robot the purpose of reliability. down, and the high energy consumption during the ,e main process of robot design includes software and wiring, and prolong the continuous working time. hardware design. ,e hardware design includes the four (3) Reliability: improving the reliability of robot oper- aspects: ation is very important for safety and maintenance cost reduction in engineering applications (1) Hardware design of robot ontology structure (2) Hardware design of the robot control system (4) Mobility: the mobility of a robot aﬀects its adapt- ability to the operating environment (3) ,e design of the main control module and the peripheral basic hardware circuit (5) Load capacity: in order to ensure the working ability of the robot, the robot also needs to bear the (4) Hardware circuit design maximum load ≥5 kg in addition to carrying its own ,e speciﬁc design content is given in Table 1. weight ,e hardware design drawing and the line changing (6) Speed requirements: the robot should improve its robot obtained are shown in Figures 5 and 6, respectively. maximum moving speed as far as possible under the ,e software design mainly includes the realization of premise of ensuring stability and reliability and re- the robot control system, the display of robot running duce the inﬂuence of external factors such as wind on state, and the realization of robot remote control. ,e work eﬃciency control ﬂowchart of the line changing robot is shown in (7) Self-protection ability: in order to prevent the robot Figure 7. ,e workﬂow of robot mainly includes the from falling, it should have certain safety self-pro- following steps: tection measures (1) First initialize the robot before work, start the Aiming at the above seven indexes, the optimized design subroutine receiving instructions, and connect it is mainly carried out from four aspects of topology opti- with the ground control device mization, size optimization, shape optimization, and mor- (2) ,e robot starts the synovial controller based on the phology optimization. RBF neural network to realize the adaptive speed Topology optimization: for the critical path of load, control at work aluminum alloy polypropylene composite lightweight (3) ,e robot starts the subroutine that collects and laminate is used. For the noncritical path, the polymer plate sends its own state and the subroutine that collects is used to minimize the bodyweight while ensuring the and sends video and sends the above data infor- payload. Designing based on the payload transfer path and mation to the ground receiving device optimal material distribution can improve the overall structure and reduce the overall design cost. (4) ,e robot receives the instruction from the ground and judges the type of the instruction and executes it Dimension optimization: the volume of robot parts is set as the objective function, and the combination of optimal until it stops working after receiving the discon- nected instruction design parameters is calculated based on dimension Journal of Robotics 7 Table 1: ,e design content of the line changing robot. Content Purpose 1 Simplify Reduce structural complexity 2 Redundancy Redundancy of key structural parameters, functional realization 3 Derating Reduce the failure rate of components 4 Components outline Control and manage electronic components and mechanical parts 5 Critical and important Utilize existing resources to improve the reliability of key and important parts Environmental Choose appropriate materials or solutions to requirements of waterproof, electromagnetic protection, protection ambient temperature, and humidity. By adopting N-version programming and implementing software engineering and speciﬁcations to improve 7 Software reliability critical reliability Packaging, shipping, 8 Determine packaging protection measures, meet shipping, storage requirements storage 9 Ergonomics Make the equipment easy to operate and maintain In the process of practical application, a number of Pressure performances of the robot are tested. It measures and an- alyzes the indicators including the operation success rate of wheel wheel the line changing robot, the online moving speed, the speed control accuracy, the climbing angle, the continuous op- eration time, and the environmental adaptation. wheel wheel ,e experimental results are given in Table 2. ,e experimental results show that the robot can move Pressure up to 1 m/s, remote control distance up to 1 km, climbing angle up to 10 , continuous operation time up to 45 minutes, and waterproof grade up to 6 on 220 kV and below overhead line. ,e adaptability of the environment is as follows: it can work normally in the environment, in which the wind is level 5 or less, and the precipitation is 8 ml or less per hour, and the temperature range is − 10–40 C. In addition, the pro- posed intelligent ropeway type line changing robot based on lifting force control and synovial ﬁlm controller does not need to build scaﬀolding and cannot hinder the normal operation of the facilities to be crossed. Compared with on- site construction, the time cost is shortened from days to hours, and the process is fast, eﬃcient, simple, safe, and reliable. It can be reused after a one-time investment, which Figure 5: Hardware design of line changing robot. greatly reduces the cost. 4.2. Performance Comparison Analysis. Since the main ex- ternal factor aﬀecting the high-altitude operation of robot is the wind, the robot developed in this study and the robot developed in [11, 13, 14] are, respectively, compared and analyzed in terms of operation duration, climbing angle, and traveling speed of the robot under diﬀerent winds. ,e relationship between the maximum operating time, maximum climbing angle, and maximum traveling speed of diﬀerent robots and wind power is shown in Figures 9–11, respectively. It can be seen from Figures 9–11 that the developed robot has the largest working time, forward speed, and climbing Figure 6: Actual object of line changing robot. angle compared with other robots, no matter in no wind or in diﬀerent wind levels. It indicates that the developed robot is better able to work outdoors at high altitude. ,e reason is 4. Experiment and Analysis that the sliding mode controller is used to control the robot, 4.1. Practical Application Experiment. Some experiments are and the RBF neural network is introduced in it, which can carried out on the 220 kV overhead transmission line for the self-learn, self-adapt, and self-organize to achieve high- designed line changing robot. It is shown in Figure 8. precision motion control of the robot in the complex 8 Journal of Robotics START Initialize the robot Start the subroutine that receives the command Receive a connection to the ground operating system Start a subroutine that collects and sends robot status Start a subroutine that captures and sends video data Start the robot precise control subroutine Determine the type of instruction and execute it NO Disconnect? YES terminate all subroutines END Figure 7: Control ﬂowchart of line changing robot. operation environment. However, the robot developed in good endurance without considering the external environ- [11] does not take into account the accurate control of the ment, but does not consider the performance of other as- robot movement speed. ,e robot developed in [13] has pects of the robot. ,e robot developed in [14] Journal of Robotics 9 Figure 8: Practical application experiment of line changing robot. Table 2: ,e experimental data of the line changing robot. Number Runtime (min) Success rate (%) Velocity (m/s) Climbing angle Wind Temperature 1 28 100 0.5 5 2 − 3 C 2 30 100 0.6 6 2 − 5 C 3 32 100 0.8 5 2 − 10 C 4 34 100 1.0 7 3 30 C 5 36 100 0.7 8 4 40 C 6 37 100 0.6 8 5 − 7 C 7 40 100 0.8 9 5 − 8 C 8 42 100 1.0 10 4 20 C 9 44 100 1.0 10 5 − 10 C 10 45 100 1.0 10 3 40 C 0 12345 Wind Level Proposed method Ref.[13] Ref.[11] Ref.[14] Figure 9: ,e relationship between the maximum runtime of diﬀerent robots and the wind level. Maximum Runtime / min 10 Journal of Robotics potential weak links and improve the reliability of the robot. In the future, further research will be conducted on the battery module of the line changing robot. ,erefore, in the future research on related robots, the RBF neural network can be introduced into the control module to improve the control stability of the robot, and the reliability of the robot 6 can be improved through reasonable structural adjustment and optimization. On the basis of ensuring the good function of the robot, the endurance time of the robot can be extended as far as possible to enhance the ability of its continuous work. 0 12345 Data Availability Headwind Level ,e data used to support the ﬁndings of this study are Proposed method Ref.[13] available from the corresponding author upon request. Ref.[11] Ref.[14] Figure 10: ,e relationship between the maximum climbing angle Conflicts of Interest of diﬀerent robots and the headwind level. ,e authors declare that they have no conﬂicts of interest. Acknowledgments ,is work was supported by State Grid Jiangsu Electric 0.8 Power Co., Ltd. (Incubate project: Research on the Intelli- gent Cable Method-Based Robot Used for Power Line Re- 0.6 moving and Changing under grant JF2021022). 0.4 References 0.2 [1] J. Yerramsetti, D. S. Paritala, and R. Jayaraman, “Design and implementation of automatic robot for ﬂoating solar panel cleaning system using AI technique,” in Proceedings of the 0 12345 11th International Conference of Computer Communication Headwind Level and Informatics (ICCCI), pp. 105–110, Sri Shakthi Institute of Proposed method Ref.[13] Engineeing and Technology, Coimbatore, India, 2021. Ref.[11] Ref.[14] [2] D. Hruby, ´ D. Marko, M. Olejar, ´ V. Cviklovic, ˇ and D. Horn ˇak, ´ “Comparison of control algorithms by simulating power Figure 11: ,e relationship between the maximum velocity of consumption of diﬀerential drive mobile robot motion diﬀerent robots and the headwind level. control in vineyard row,” Acta Technologica Agriculturae, vol. 24, no. 4, pp. 195–201, 2021. [3] W. Liao, K. Nagai, and J. Wang, “An evaluation method of overemphasizes the importance of path control while ne- electromagnetic interference on bio-sensor used for wearable glects the optimization of the climbing ability of robot speed. robot control,” IEEE Transactions on Electromagnetic Com- patibility, vol. 62, no. 1, pp. 36–42, 2020. 5. Conclusion [4] W. Q. Zou, X. Shu, Q. Q. Tang, and S. 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Materials and Power Equipment (ICEMPE), pp. 478–481, Guangzhou, China, April 2019. [9] J. Katrasnik, F. Pernus, and B. Likar, “A survey of mobile robots for distribution power line inspection,” IEEE Trans- [24] M. U. Sumagayan, C. Premachandra, R. B. Mangorsi, C. J. Salaan, H. W. H. Premachandra, and H. Kawanaka, actions on Power Delivery, vol. 25, no. 1, pp. 485–493, 2010. [10] A. Papadimitrio, G. Andrikopoulos, and G. Nikolakopoulos, “Detecting power lines using point Instance network for distribution line Inspection,” IEEE Access, vol. 9, no. 4, “On path following evaluation for a tethered climbing robot,” pp. 107998–108008, 2021. in Proceedings of the 46th Annual Conference of the IEEE- [25] Z. Pu, Y. Xiong, H. Wang et al., “Design and construction of a Industrial-Electronics-Society (IECON), pp. 656–661, ELECTR new insulator detection robot for application in 500 kV Network, Singapore, October 2020. strings: electric ﬁeld analysis and ﬁeld testing,” Electric Power [11] C. H. 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Liu, “Pylon-climber: a novel climbing assistive robot for pylon maintenance,” Industrial Robot: An International Journal, vol. 44, no. 1, pp. 38–48, [22] A. T. Zengin, G. Erdemir, T. C. Akinci, F. A. Selcuk, M. N. Erduran, and S. S. N. Seker, “ROSETLineBot: one- wheel-drive low-cost power line Inspection robot design and control,” Journal of Electrical Systems, vol. 15, no. 4, pp. 626–634, 2019. [23] T. Q. Mao, K. Huang, X. W. Zeng et al., “Development of power transmission line defects diagnosis system for UAV

Journal

Journal of Robotics – Hindawi Publishing Corporation

Published: Apr 12, 2022

References

Design and implementation of automatic robot for floating solar panel cleaning system using AI technique,

J. Yerramsetti, D. S. Paritala, and R. Jayaraman
Comparison of control algorithms by simulating power consumption of differential drive mobile robot motion control in vineyard row,

D. Hrubý, D. Marko, M. Olejár, V. Cviklovič, and D. Horňák
An evaluation method of electromagnetic interference on bio-sensor used for wearable robot control,

W. Liao, K. Nagai, and J. Wang
A survey of the application of robots in power system operation and maintenance management,

W. Q. Zou, X. Shu, Q. Q. Tang, and S. Lu
iCrawl: an inchworm-inspired crawling robot,

M. B. Khan, T. Chuthong, C. D. Do et al.
Design and analysis of FCSTAR, a hybrid flying and climbing sprawl tuned robot,

N. B. David and D. Zarrouk
Research of multi-rotor UAVs detailed autonomous Inspection Technology of Transmission lines based on route planning,

T. He, Y. Zeng, and Z. Hu
Design and implementation of a cable inspection robot for cable-stayed bridges,

Z. P. Wang, B. He, Y. M. Zhou, K. Liu, and C. Zhang
A survey of mobile robots for distribution power line inspection,

J. Katrasnik, F. Pernus, and B. Likar
On path following evaluation for a tethered climbing robot,

A. Papadimitrio, G. Andrikopoulos, and G. Nikolakopoulos
Geometrical motion planning for cable-climbing robots applied to distribution power lines inspection,

C. H. F. dos Santos, M. H. Abdali, D. Martins, and C. B. Aníbal Alexandre
Measurement of power line sagging using sensor data of a power line inspection robot,

A. T. Zengin, G. Erdemir, T. C. Akinci, and S. Seker
Dynamic model based energy consumption optimal motion planning for high-voltage transmission line mobile robot manipulator,

W. Jiang, G. C. Ye, D. Zou et al.
Modeling and practical implementation of motion controller for stable movement in a robotic solar panel dust-removal system,

T. P. Nguyen, H. Nguyen, V. H. Phan, and H. Q. Thinh Ngo
Substation electric power equipment detection based on patrol robots,

X. Zhao, Z. Peng, and S. Zhao
Fully 3D-printed modular pipe-climbing robot,

D. Xie, J. Liu, R. Kang, and S. Zuo
Automatic patrol and Inspection method for machinery diagnosis robot-sound signal-based fuzzy search approach,

L. Song, H. Wang, and P. Chen
Kinematics and singularity analysis of an electricity pylon climbing robot,

X. Liu, X. Lu, and S. Zhao
Dynamic modeling of closed-chain robotic manipulators in the presence of frictional dynamic forces: a planar case,

M. Ahmadizadeh, A. M. Shafei, and M. Fooladi
Estimation of critical force and time required to control the kinematics and friction of rough ellipsoidal and cubic nanoparticles using mechanics of contact surfaces,

M. H. Korayem and H. Khaksar
Pylon-climber: a novel climbing assistive robot for pylon maintenance,

X. Lu, S. Zhao, D. Yu, and X. Liu
ROSETLineBot: one-wheel-drive low-cost power line Inspection robot design and control,

A. T. Zengin, G. Erdemir, T. C. Akinci, F. A. Selcuk, M. N. Erduran, and S. S. N. Seker
Development of power transmission line defects diagnosis system for UAV inspection based on binocular depth imaging technology,

T. Q. Mao, K. Huang, X. W. Zeng et al.
Detecting power lines using point Instance network for distribution line Inspection,

M. U. Sumagayan, C. Premachandra, R. B. Mangorsi, C. J. Salaan, H. W. H. Premachandra, and H. Kawanaka
Design and construction of a new insulator detection robot for application in 500 kV strings: electric field analysis and field testing,

Z. Pu, Y. Xiong, H. Wang et al.
Effect of patrol robot on electric field distribution of human body in 500 kV substation,

Y. Li, S. He, L. Xu, X. Wang, and A. Zou

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Design Method of Intelligent Ropeway Type Line Changing Robot Based on Lifting Force Control and Synovial Film Controller

Design Method of Intelligent Ropeway Type Line Changing Robot Based on Lifting Force Control and Synovial Film Controller

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Design Method of Intelligent Ropeway Type Line Changing Robot Based on Lifting Force Control and Synovial Film Controller

Design Method of Intelligent Ropeway Type Line Changing Robot Based on Lifting Force Control and Synovial Film Controller

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