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Learning is essential for cognitive robots as humans to gain experience and to adapt to the real world. We propose an experiential learning method for robots to build their experience online and to transfer knowledge among appropriate contexts. Experience gained through learning is used as a...
Robot operating environments and the status of robots are complex and varying, so it is practically impossible for a robotics designer to anticipate all system configurations to successfully complete a task prior to deployment. Therefore, a mechanism for dynamic decision making and configuration...
Model predictive control (MPC) is a common approach to the control of trajectory-following systems. For nonlinear plants such as car-like robots, methods for path planning and following have the advantage of concurrently solving problems of obstacle avoidance, feasible trajectory selection, and...
This paper approaches from an optimal control perspective the problem of fixed-time detection of mobile radioactive sources in transit by means of a collection of mobile sensors. Under simplifying assumptions on the motion and geometry of the source, the sensors, and the surrounding environment,...
This paper presents a Markov chain based approach for the probabilistic density control of a large number, swarm, of autonomous agents. The proposed approach specifies the time evolution of the probabilistic density distribution by using a Markov chain, which guides the swarm to a desired...
Existing approaches to constrained dynamic programming are limited to formulations where the constraints share the same additive structure of the objective function (that is, they can be represented as an expectation of the summation of one-stage costs). As such, these formulations cannot handle...
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