Path Planning and Singularity Avoidance for Robotic Manipulators in Vision-Constrained Confined Environments Based on GMM and Improved RRT*

In complex and confined industrial environments—such as aerospace component assembly and narrow cabin maintenance—robotic front-end visual perception systems frequently suffer from severe structural occlusions, localized target feature loss, and field-of-view (FOV) clipping during deep aperture traversal, creating a representative “vision-constrained” operational regime where continuous real-time visual feedback is physically unavailable. Concurrently, robotic manipulators face low search efficiency in highly constrained narrow passages and severe risks of Cartesian kinematic singularities. To resolve these coupled challenges without relying on online visual servoing, this paper proposes an improved RRT* algorithm based on a Gaussian Mixture Model (GMM-IRRT*) integrating a singularity-aware barrier cost function, built upon pre-calibrated 6D target poses acquired from front-end perception outside the passage. Specifically, a continuous probabilistic guidance field is constructed via GMM learning from prior demonstration paths, transforming uniform random exploration into targeted sampling along feasible topological corridors. Furthermore, a nonlinear singularity penalty term based on the Yoshikawa manipulability measure is incorporated into the underlying algorithmic cost function, establishing a physical barrier that compels the planning tree to autonomously select non-singular, dexterous inverse kinematic configuration branches (e.g., an “elbow-up” posture). Finally, quasi-uniform cubic B-spline curves optimize the discrete waypoints to guarantee C2 continuity and bounded joint jerk. Simulation results demonstrate that under constrained benchmark tests, the proposed method achieves a planning success rate exceeding 98.0%, shortens the average planning time by 15.1% in narrow passages and up to 37.3% in complex cluttered environments (with planning time variance reduced by over 66%), and completely eliminates explosive joint velocity spikes. Extensive physical experiments across 20 repeated trials on an AUBO-i5 manipulator platform validate the zero-failure execution, superior trajectory smoothness (tracking RMSE of 1.82 mm), and industrial transferability of the proposed framework.