Adaptive Learning-Based Robot Motion Planning

Abstract

This paper presents a novel approach to robot motion planning based on adaptive learning. Traditional robot motion planning methods often struggle to cope with dynamic and uncertain environments. This research leverages reinforcement learning (RL) to develop a system capable of autonomously learning optimal motion strategies and adapting to real-time changes in the environment and task requirements. The core of the system is a dynamically adjusted trajectory generation process, allowing the robot to navigate complex scenarios with greater flexibility and efficiency. The system is trained using an RL algorithm, and the learned policy is then utilized for real-time trajectory optimization. This approach demonstrates the potential for significantly enhancing the adaptability and intelligence of robots in various applications. The system's ability to learn and adapt provides a robust solution for dynamic environments, addressing limitations inherent in traditional planning techniques. ---