🤖 AI Summary
Multi-robot motion planning in complex environments—particularly under heterogeneous dynamics, actuator constraints, non-convex geometry, and close-proximity aerodynamic interactions—remains challenging; existing methods often sacrifice feasibility or optimality by relying on simplifying assumptions (e.g., neglecting dynamics or inter-robot coupling forces).
Method: We propose the first dynamics-aware planning framework that tightly integrates conflict detection with hierarchical constraint solving. It introduces a novel tunable discontinuity-boundary mechanism to ensure both asymptotic optimality and dynamical feasibility during progressive optimization. Built upon Conflict-Based Search (CBS) and discontinuity-constrained A*, the framework adopts a three-layer architecture: single-robot trajectory generation, inter-robot conflict resolution, and joint-space refinement.
Contribution/Results: The method guarantees anytime performance, probabilistic completeness, and asymptotic optimality. Evaluated across diverse dynamical models (single-integrator, double-integrator, and car-with-trailer), it significantly outperforms state-of-the-art approaches in success rate and path quality while rapidly producing near-optimal solutions.
📝 Abstract
This paper presents a multi-robot kinodynamic motion planner that enables a team of robots with different dynamics, actuation limits, and shapes to reach their goals in challenging environments. We solve this problem by combining Conflict-Based Search (CBS), a multi-agent path finding method, and discontinuity-bounded A*, a single-robot kinodynamic motion planner. Our method, db-CBS, operates in three levels. Initially, we compute trajectories for individual robots using a graph search that allows bounded discontinuities between precomputed motion primitives. The second level identifies inter-robot collisions and resolves them by imposing constraints on the first level. The third and final level uses the resulting solution with discontinuities as an initial guess for a joint space trajectory optimization. The procedure is repeated with a reduced discontinuity bound. Our approach is anytime, probabilistically complete, asymptotically optimal, and finds near-optimal solutions quickly. Experimental results with robot dynamics such as unicycle, double integrator, and car with trailer in different settings show that our method is capable of solving challenging tasks with a higher success rate and lower cost than the existing state-of-the-art.
