Cross Synergy Model-Based Accuracy Improvement for Manipulator Trajectory Planning in Dynamic Environments

To address the common problems of degraded trajectory accuracy, insufficient safety margin, and limited real-time response capability in manipulator trajectory planning under dynamic environments, this study focuses on the collaborative optimization requirements of high precision, high safety, and high real-time performance in complex scenarios. This study proposes a Cross Synergy Model empowered manipulator trajectory planning method and constructs a unified trajectory generation and optimization framework for dynamic environments to improve the stability and executability of trajectory planning in variable scenarios. This method introduces conditional diffusion generation, risk-aware correction, and a closed-loop mechanism of generation-optimization-online correction. The experimental results demonstrate that the proposed method effectively improves trajectory accuracy and safety margin while maintaining low planning latency. Therefore, this study has certain reference significance for the field of manipulator trajectory planning in dynamic environments.