db-CBS: Discontinuity-Bounded Conflict-Based Search for Multi-Robot Kinodynamic Motion Planning
About
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.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Multi-Robot Motion Planning | Unicycle (UC) Swap (N=4, 5, 8, 10, 15, 20, 25, 30) | Success Rate100 | 16 | |
| Kinodynamic Multi-agent Path Planning | Narrow Corridor UC N=3 | Success Rate (%)100 | 14 | |
| Multi-Robot Motion Planning | Unicycle (UC) Small Cluttered (N=3, 4, 5, 6, 7, 8, 10, 12, 15, 18, 20) | Success Rate100 | 8 | |
| Kinodynamic Multi-agent Path Planning | Large Cluttered UC N=5 | SR100 | 7 | |
| Multi-Robot Motion Planning | Unicycle (UC) Large Cluttered (N=4, 5, 8, 10, 15, 20, 25, 30) | Success Rate100 | 7 | |
| Kinodynamic Multi-agent Path Planning | Small Cluttered SOC N=4 | Success Rate (SR)0.00e+0 | 7 | |
| Kinodynamic Multi-agent Path Planning | Small Cluttered SOC N=8 | Success Rate (SR)0.00e+0 | 7 | |
| Kinodynamic Multi-agent Path Planning | Small Cluttered SOC N=15 | Success Rate0.00e+0 | 7 | |
| Kinodynamic Multi-agent Path Planning | Large Cluttered UC N=20 | Success Rate0.00e+0 | 7 | |
| Kinodynamic Multi-agent Path Planning | Large Cluttered UC N=30 | Success Rate (SR)0.00e+0 | 7 |