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pc-dbCBS: Kinodynamic Motion Planning of Physically-Coupled Robot Teams

Khaled Wahba, Wolfgang Hönig

TL;DR

pc-dbCBS tackles kinodynamic motion planning for physically-coupled robot teams in cluttered spaces by extending db-CBS with a tri-level conflict framework and alternating state representations. The method leverages stacked discrete search with single-robot motion primitives and minimal-representation trajectory optimization to maintain probabilistic completeness and asymptotic optimality. Empirical results across two platforms, including cable-suspended multirotors and rod-connected unicycles, show substantial improvements in success rate, trajectory cost, and planning time compared to a state-of-the-art baseline, with real-world experiments validating practical performance. The work advances scalable, provably sound planning for complex, physically-interacting robotic systems and suggests further work on scalability and tighter control–planning integration.

Abstract

Motion planning problems for physically-coupled multi-robot systems in cluttered environments are challenging due to their high dimensionality. Existing methods combining sampling-based planners with trajectory optimization produce suboptimal results and lack theoretical guarantees. We propose Physically-coupled discontinuity-bounded Conflict-Based Search (pc-dbCBS), an anytime kinodynamic motion planner, that extends discontinuity-bounded CBS to rigidly-coupled systems. Our approach proposes a tri-level conflict detection and resolution framework that includes the physical coupling between the robots. Moreover, pc-dbCBS alternates iteratively between state space representations, thereby preserving probabilistic completeness and asymptotic optimality while relying only on single-robot motion primitives. Across 25 simulated and six real-world problems involving multirotors carrying a cable-suspended payload and differential-drive robots linked by rigid rods, pc-dbCBS solves up to 92% more instances than a state-of-the-art baseline and plans trajectories that are 50-60% faster while reducing planning time by an order of magnitude.

pc-dbCBS: Kinodynamic Motion Planning of Physically-Coupled Robot Teams

TL;DR

pc-dbCBS tackles kinodynamic motion planning for physically-coupled robot teams in cluttered spaces by extending db-CBS with a tri-level conflict framework and alternating state representations. The method leverages stacked discrete search with single-robot motion primitives and minimal-representation trajectory optimization to maintain probabilistic completeness and asymptotic optimality. Empirical results across two platforms, including cable-suspended multirotors and rod-connected unicycles, show substantial improvements in success rate, trajectory cost, and planning time compared to a state-of-the-art baseline, with real-world experiments validating practical performance. The work advances scalable, provably sound planning for complex, physically-interacting robotic systems and suggests further work on scalability and tighter control–planning integration.

Abstract

Motion planning problems for physically-coupled multi-robot systems in cluttered environments are challenging due to their high dimensionality. Existing methods combining sampling-based planners with trajectory optimization produce suboptimal results and lack theoretical guarantees. We propose Physically-coupled discontinuity-bounded Conflict-Based Search (pc-dbCBS), an anytime kinodynamic motion planner, that extends discontinuity-bounded CBS to rigidly-coupled systems. Our approach proposes a tri-level conflict detection and resolution framework that includes the physical coupling between the robots. Moreover, pc-dbCBS alternates iteratively between state space representations, thereby preserving probabilistic completeness and asymptotic optimality while relying only on single-robot motion primitives. Across 25 simulated and six real-world problems involving multirotors carrying a cable-suspended payload and differential-drive robots linked by rigid rods, pc-dbCBS solves up to 92% more instances than a state-of-the-art baseline and plans trajectories that are 50-60% faster while reducing planning time by an order of magnitude.
Paper Structure (29 sections, 26 equations, 3 figures, 3 tables, 1 algorithm)

This paper contains 29 sections, 26 equations, 3 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Formulation
  4. Unicycles with Rigid Rods (UR)
  5. Multirotors Transporting a Payload (MP)
  6. Approach
  7. pc-dbCBS
  8. Single Robot Planning
  9. Conflict Resolution
  10. Stacked to Constrained Systems
  11. Trajectory Optimization
  12. Anytime Planning
  13. Case Study: Unicycles with Rigid Rods (UR)
  14. Constrained Dynamics
  15. Conflicts
  16. ...and 14 more sections

Figures (3)

  • Figure 1: Real experiments validation scenarios. Left: Three multirotors transporting a cable-suspended payload in a forest-like environment. Right: Three differential drive robots connected with rigid rods collecting items while avoiding obstacles.
  • Figure 2: Simulation environments from left to right: window (5 multirotors), forest (4 multirotors), wall (3 unicycles), window (4 unicycles). Note that the forest environment is the same for both robot types.
  • Figure 3: Anytime planning of pc-dbCBS for three different example scenarios for multirotors with payload (MP).