Assistive Humanoid Robot Arm Motion Generation in Dynamic Environment Based on Neural Networks

Assistive humanoid robots operating in everyday life environments have to autonomously navigate and perform several tasks. In this paper we propose a neural network based humanoid robot navigation and arm trajectory generation. The robotic system, which is equipped with a visual sensor, laser range finders, navigates in the environment. The neural controllers generate the robot arm motion in dynamic environments where obstacles of different shapes and positions are present. We have implemented the proposed algorithms in the hardware of a mobile humanoid robot. The robot can navigate in the environment reach the table and pick the target object. The motion generated yield good results in both simulation and experimental environments.

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