Trajectory Planning and Obstacle Avoidance Method based on Informed RRT* and Dynamic Movement Primitives

Abstract

Addressing the limitations of traditional Dynamic Movement Primitives (DMP) obstacle avoidance methods-which rely on dynamic coupling terms leading to complex algorithmic structures, sluggish responses, and difficulties in autonomous obstacle avoidance-this study innovatively proposes a robotic arm obstacle avoidance trajectory planning framework that integrates DMP with the Informed RRT* algorithm. This framework uses the Informed RRT algorithm to do local trajectory replanning in areas near obstacles. It keeps the main dynamic features of DMP and creates obstacle-avoidance trajectories that are then smoothly added to the original DMP trajectory. This approach eliminates the need for complex dynamic coupling terms, significantly reducing algorithmic complexity. Its local replanning mechanism not only enables autonomous obstacle avoidance and effectively suppresses trajectory oscillations but also substantially enhances real-time response speed. The main contributions of this study include 1) proposing a decoupled DMP obstacle avoidance method based on local trajectory replanning combined with Informed RRT*, simplifying the algorithm structure; 2) the adopted local replanning strategy significantly shortens response time and improves obstacle avoidance efficiency. The effectiveness of the proposed algorithm was validated through two sets of comparative experiments (against the RRT method) conducted on a collaborative robot platform. Experimental results demonstrate that the algorithm reduces response time for obstacle avoidance by 33.2% and increases success rate by 19.5%, fully proving its potential and applicability in specific industrial scenarios.