Pc-dbCBS: Kinodynamic Motion Planning of Physically-Coupled Robot Teams
Khaled Wahba, Wolfgang Hoenig
AI summary
Problem
Planning agile, feasible motions for physically-coupled multi-robot teams in cluttered environments is hindered by high dimensionality, numerical instabilities, and a lack of planners that offer both theoretical guarantees and computational efficiency.
Approach
The method extends Conflict-Based Search with a tri-level conflict resolution framework that alternates between stacked and minimal state representations to enforce rigid coupling constraints while relying on precomputed single-robot motion primitives.
Key results
- Solves up to 90% more planning instances than state-of-the-art baselines
- Reduces planning time by up to 60% with faster trajectory generation
- Achieves lower path cost and energy consumption across simulated and real-world tests
- Guarantees probabilistic completeness and asymptotic optimality for rigidly-coupled systems
Why it matters
Provides a theoretically sound, computationally efficient planning foundation for agile multi-robot coordination in complex real-world tasks like aerial payload transport and linked ground vehicle teams.
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 90% more instances than a state-of-the-art baseline, planning trajectories up to 60% faster with significantly reduced planning time.