Deep Reinforcement Learning for Adaptive Motion Planning in Dynamic Environment

Abstract:

Motion planning as the process of finding a sequence of movements for a robot to perform a specific task in its application to several aspects of discipline like computer, engineering, sciences etc is evolving with Deep Reinforcement Learning (DRL).DRL to adaptive motion planning in dynamic environments provide a comprehensive overview of DRL, including its history, key concepts, and applications in this paper. This paper discuss on the application of DRL to motion planning, highlighting the potential of this approach to adapt to changing environments and learn from experience. We present a case study on dynamic motion planning for an industrial manipulator, demonstrating the effectiveness of DRL in learning complex tasks and adapting to dynamic environments. The results show that the DRL approach is able to learn the task and perform it efficiently, adapting to the changing environment and ensuring accurate placement of objects. This paper contributes to the development of DRL-based motion planning solutions for complex robotic systems, highlighting the potential of this approach for industrial applications.

Keywords: Deep Reinforcement Learning, Adaptive Motion Planning, Dynamic Environments, Industrial Manipulator, Robot.