Multi-Objective Optimal Trajectory Planning for Robotic Arms Using Deep Reinforcement Learning

Abstract

This study investigated the trajectory-planning problem of a six-axis robotic arm based on deep reinforcement learning. Taking into account several characteristics of robot motion, a multi-objective optimization approach is proposed, which was based on the motivations of deep reinforcement learning and optimal planning. The optimal trajectory was considered with respect to multiple objectives, aiming to minimize factors such as accuracy, energy consumption, and smoothness. The multiple objectives were integrated into the reinforcement learning environment to achieve the desired trajectory. Based on forward and inverse kinematics, the joint angles and Cartesian coordinates were used as the input parameters, while the joint angle estimation served as the output. To enable the environment to rapidly find more-efficient solutions, the decaying episode mechanism was employed throughout the training process. The distribution of the trajectory points was improved in terms of uniformity and smoothness, which greatly contributed to the optimization of the robotic arm’s trajectory. The proposed method demonstrated its effectiveness in comparison with the RRT algorithm, as evidenced by the simulations and physical experiments.

A Novel Sampling-Based Optimal Motion Planning Algorithm for Energy-Efficient Robotic Pick and Place

“…The proposed route planner utilizes the RRT-based planning approach to generate a point-topoint path in the joint space that satisfies the camera's field of vision requirements while ensuring obstruction-free and conflict-free constraints. Recently, Zhang et al [25] proposed a multi-objective optimization approach using deep reinforcement learning for the trajectory-planning problem of a six-axis robotic arm, aiming to minimize factors such as accuracy, energy consumption, and smoothness, and demonstrates its effectiveness compared to the RRT algorithm through simulations and physical experiments.…”