Review on Motion Planning of Robotic Manipulator in Dynamic Environments

Abstract

In response to the growing demands for faster production and cost‐efficiency, collaborative and autonomous robots are playing increasingly important roles in various industries. However, ensuring safe interactions between robots and humans in shared workspaces continues to pose significant challenges. This paper provides a detailed review of motion planning algorithms designed for robotic manipulators working in dynamic environments, with a focus on efficiently adapting to changing conditions while ensuring human operator safety. The algorithms covered in this survey—identified through a comprehensive review of the literature and validated experimentally—include sampling‐based methods, artificial potential field (APF) methods, optimization‐based approaches, and learning‐based techniques. Experimental evaluations offer a practical assessment of these algorithms’ real‐time performance. By comparing and analysing the experimental results, this article highlights the relative efficiency of the algorithms and suggests strategies for improving motion planning in dynamic environments.