SR-Planner: Sampling-Based Path Planning With Feasibility-Aware Focus Regions and Robust Trajectory Optimization for Mobile Manipulator

Abstract

High-quality motion planning for mobile manipulators remains a challenging task due to the high dimensionality and complex constraints involved. While existing methods perform well in specific scenarios, their efficiency and the quality of the resulting paths and trajectories often degrade in dense or irregular environments. In this paper, we propose a unified motion planning framework for mobile manipulators. It consists of two parts: a sampling-based path planner with feasibility-aware focus regions for efficient path generation, and a WA-QN optimizer that adaptively filters and aggregates curvature information to enhance convergence in high-dimensional spaces. Comprehensive experiments in simulated and real-world scenarios demonstrate that our method achieves faster planning, higher success rates, and superior path and trajectory quality, while exhibiting strong generalization across diverse environments.