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db-CBS: Discontinuity-Bounded Conflict-Based Search for Multi-Robot Kinodynamic Motion Planning

Akmaral Moldagalieva, Joaquim Ortiz-Haro, Wolfgang Hönig

TL;DR

db-CBS presents a novel three-level kinodynamic motion planner for multi-robot systems that integrates discontinuity-bounded single-robot planning with conflict-based high-level constraint resolution and joint-space trajectory optimization. By allowing bounded discontinuities during low-level planning and progressively reducing the bound, it achieves near-optimal, probabilistically complete performance for heterogeneous robot dynamics. The approach demonstrates superior success rates and lower costs compared to state-of-the-art baselines on canonical and real-world drone experiments, validating its practicality for complex, dynamic environments. The work bridges informed discrete search with continuous optimization, offering scalable, real-time capable planning for diverse robotic teams.

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.

db-CBS: Discontinuity-Bounded Conflict-Based Search for Multi-Robot Kinodynamic Motion Planning

TL;DR

db-CBS presents a novel three-level kinodynamic motion planner for multi-robot systems that integrates discontinuity-bounded single-robot planning with conflict-based high-level constraint resolution and joint-space trajectory optimization. By allowing bounded discontinuities during low-level planning and progressively reducing the bound, it achieves near-optimal, probabilistically complete performance for heterogeneous robot dynamics. The approach demonstrates superior success rates and lower costs compared to state-of-the-art baselines on canonical and real-world drone experiments, validating its practicality for complex, dynamic environments. The work bridges informed discrete search with continuous optimization, offering scalable, real-time capable planning for diverse robotic teams.

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.
Paper Structure (19 sections, 2 theorems, 6 equations, 2 figures, 2 tables, 2 algorithms)

This paper contains 19 sections, 2 theorems, 6 equations, 2 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Definition
  4. Approach
  5. Background
  6. db-CBS
  7. db-A* For Dynamic Obstacles
  8. Properties and Extensions
  9. Results
  10. Canonical Examples
  11. Swap
  12. Alcove
  13. At Goal
  14. Scalability
  15. Robot Number
  16. ...and 4 more sections

Key Result

Theorem 1

The db-CBS motion planner in alg:dbcbs is asymptotically optimal, i.e. where $c_n$ is the cost in iteration $n$ and $c^*$ is the optimal cost.

Figures (2)

  • Figure 1: Db-CBS solving multi-robot kinodynamic motion planning. Left: problem instance with a team of 8 heterogeneous robots: unicycle $1^{\text{st}}$ order (rectangle), double integrator (circle), car with trailer (rectangle with box in the end). Right: Real-world experiment challenging each pair of flying robots to pass the narrow window swapping their positions in order to reach their assigned goals.
  • Figure 2: Methods are listed from left to right. Upper row: S2M2, K-CBS, db-CBS with swap. Middle row: SST*, S2M2, db-CBS with alcove. Last row shows instances where only db-CBS is able to return a solution: 4 cars with trailer with swap, 4 unicycles with swap, 8 unicycles with random start and goal states.

Theorems & Definitions (4)

  • Definition 1
  • Theorem 1
  • Theorem 2
  • proof