A Connectionist System for Learning Robot Manipulator Obstacle-Avoidance Capabilities in Path Planning
نویسندگان
چکیده
Real time path planning for a robot manipulator could be achieved if we had a method that relies on sensory data and maps any possible perceived situation with suitable actions. In a step forward in this way, we propose a connectionist reactive system that uses reinforcement learning to obtain the reflexes needed for a 2 dof robot manipulator to avoid obstacles while moving to the target point in a 2D environment. A codification scheme for representing the state of the environment is developed in order to achieve that the system could acquire environmentand goal-independent reflexes.
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