A General Approach to Path Planning within C-Space Safe Corridors
Riccardo Abbati, Marco Riboli, Michel Rosselli, Alessandro Tasora, Marco Silvestri
AI summary
Problem
Existing path planners for robotic manipulators struggle to efficiently balance computational speed, trajectory smoothness, and safety margins in cluttered environments, often relying on conservative sampling or computationally heavy optimization.
Approach
The method first builds a safe corridor by asymmetrically expanding axis-aligned bounding boxes from the start and goal configurations, then optimizes smooth splines within this corridor to minimize acceleration and maximize obstacle clearance.
Key results
- Goal-Biased Probabilistic Foam Cube-connect algorithm for efficient C-space corridor generation
- Spline-based channel problem solved via linearly constrained quadratic programming for C3-continuous trajectories
- Superior computational performance and trajectory smoothness over baseline planners in simulation
- Successful real-world validation on a UR5e manipulator with open-source implementation
Why it matters
Enables reliable, high-speed motion planning for industrial robotic manipulators in complex environments, with direct applicability to automation and robotics research.
Abstract
This letter presents a novel strategy for collision-free path planning in robotic manipulators. The method operates in two stages: first, a sampling-based exploration of the configura- tion space is performed to construct a safe corridor composed of axis-aligned bounding boxes. Within this corridor, an optimisation- based trajectory generation phase addresses the channel problem by computing smooth joint trajectories as non-rational splines. A multi-objective cost function is minimised to reduce geometric acceleration along the path, while also maximising the distance from obstacles to improve safety margins. The proposed algorithm is general and applicable to a wide range of kinematic structures, and supports user-defined path degree and geometric continuity. Simulation results demonstrate superior performance compared to existing methods, and experimental validation further confirms its practical effectiveness. Our implementation is open-sourced and available on Github.