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Control by Gradient Collocation: Applications to optimal obstacle avoidance and minimum torque control
2012
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
We present a new machine learning algorithm for learning optimal feedback control policies to guide a robot to a goal in the presence of obstacles. Our method works by first reducing the problem of obstacle avoidance to a continuous state, action, and time control problem, and then uses efficient collocation methods to solve for an optimal feedback control policy. This formulation of the obstacle avoidance problem improves over standard approaches, such as potential field methods, by being
doi:10.1109/iros.2012.6385556
dblp:conf/iros/RuvoloWM12
fatcat:p6bsiinztne2fodfph3x3ke3im