db-LaCAM: Fast and Scalable Multi-Robot Kinodynamic Motion Planning with Discontinuity-Bounded Search and Lightweight MAPF

2025-12-07

AI Summary

Existing dynamics-aware motion planners suffer from high computational complexity, hindering scalability to tens of robots. This paper proposes db-LaCAM, a framework that bridges the efficiency of multi-agent path finding (MAPF) with dynamics awareness. It introduces a search mechanism supporting user-defined discontinuities, ensuring resolution completeness while accommodating arbitrary robot dynamics models. By integrating a precomputed library of motion primitives with a lightweight MAPF solver, db-LaCAM generates finite-horizon, dynamically feasible trajectories. The method supports diverse robot models—including unicycles, 3D double integrators, car-trailer systems, and quadrotors—in both 2D and 3D environments. Experiments demonstrate a 10× speedup over state-of-the-art planners in 50-robot scenarios, with comparable trajectory quality. Safety and feasibility are further validated on real-world flying robots and car-trailer systems.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

State-of-the-art multi-robot kinodynamic motion planners struggle to handle more than a few robots due to high computational burden, which limits their scalability and results in slow planning time. In this work, we combine the scalability and speed of modern multi-agent path finding (MAPF) algorithms with the dynamic-awareness of kinodynamic planners to address these limitations. To this end, we propose discontinuity-Bounded LaCAM (db-LaCAM), a planner that utilizes a precomputed set of motion primitives that respect robot dynamics to generate horizon-length motion sequences, while allowing a user-defined discontinuity between successive motions. The planner db-LaCAM is resolution-complete with respect to motion primitives and supports arbitrary robot dynamics. Extensive experiments demonstrate that db-LaCAM scales efficiently to scenarios with up to 50 robots, achieving up to ten times faster runtime compared to state-of-the-art planners, while maintaining comparable solution quality. The approach is validated in both 2D and 3D environments with dynamics such as the unicycle and 3D double integrator. We demonstrate the safe execution of trajectories planned with db-LaCAM in two distinct physical experiments involving teams of flying robots and car-with-trailer robots.

Problem

• Addresses scalability and speed limitations in multi-robot kinodynamic motion planning
• Combines multi-agent path finding efficiency with dynamic-aware motion primitives
• Enables safe, fast planning for up to 50 robots in 2D/3D environments

Innovation

• Combines scalable MAPF with kinodynamic motion planning
• Uses precomputed motion primitives with user-defined discontinuity
• Efficiently scales to 50 robots with tenfold speed improvement