SAI-RRT*: Safety-Aware Informed RRT* for Multi-Joint Manipulator Path Planning in Static Environments

Path planning for multi-joint robotic manipulators within complex industrial settings represents a formidable challenge due to high-dimensional spaces and safety requirements. While the conventional Informed RRT* provides a solid foundation, it frequently encounters bottlenecks such as low initial sampling efficiency and poor adaptability. To overcome these issues, this study proposes SAI-RRT* (Safety-Aware Informed RRT*), a specialized framework designed to improve planning efficiency and geometric collision safety for 6-DOF multi-joint manipulator planning through an optimization strategy. First, we replace traditional global exploration with an adaptive constrained circular area sampling technique to reduce invalid exploration and accelerate initial path discovery. Second, a joint constraint framework combining kinematic verification and local reinforcement learning is applied to adjust joint configurations to avoid node rejection. Third, forward-kinematics-based full-body geometric verification is embedded into the tree expansion process, where the capsule model serves as an efficient link-clearance checking layer. Numerical experiments under static obstacle conditions show that SAI-RRT* improves planning efficiency, geometric collision safety, and path quality.