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

• URL: http://arxiv.org/abs/2512.06796v2
• Date: Tue, 09 Dec 2025 11:50:21 GMT
• Title: db-LaCAM: Fast and Scalable Multi-Robot Kinodynamic Motion Planning with Discontinuity-Bounded Search and Lightweight MAPF
• Abstract summary: State-of-the-art multi-robot kinodynamic motion planners struggle to handle more than a few robots due to high computational burden.<n>We combine the scalability and speed of modern multi-agent path finding algorithms with the dynamic-awareness of kinodynamic planners to address these limitations.
• Score: 19.27447010190815
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch [66.7936140772745]
  We present GigaWorld-Policy-0.5, an action-centered WAM designed for more efficient robot control.<n>For efficient inference, GigaWorld-Policy-0.5 introduces a Mixture-of-Transformers architecture that separates visual dynamics modeling and action generation into specialized experts.<n>Experiments and ablations show that GigaWorld-Policy-0.5 preserves the training benefits of future visual dynamics while improving inference efficiency for robot control.
  arXiv  Detail & Related papers  (2026-07-15T15:46:42Z)
• CR-Solver: GPU-Accelerated Kinematics Solver for Tendon-driven Continuum Robots [18.541022260976348]
  We present a two-stage, optimization-based solver for the motion generation of tendon-driven continuum robots.<n>Our method unifies inverse kinematics, path following, and trajectory planning within a single constrained nonlinear optimization framework.<n>We validate our approach on three tasks, demonstrating significant speedups over traditional CPU-based solvers while achieving a consistently high success rate above 95% and millimeter-level accuracy.
  arXiv  Detail & Related papers  (2026-07-13T10:02:36Z)
• Simulation-Informed Diffusion for Decentralized Multi-robot Motion Planning [56.240199425429445]
  Multi-robot motion planning requires each robot to generate collision-free trajectories from local observations.<n>Most existing planners generate trajectories from a static snapshot of the local observation.<n>This paper introduces Simulation-Informed Diffusion (SID), a decentralized framework built on constraint-aware diffusion models.
  arXiv  Detail & Related papers  (2026-05-26T21:17:53Z)
• Dynamic Whole-Body Dancing with Humanoid Robots -- A Model-Based Control Approach [21.477482121501897]
  The framework operates in two stages: offline motion generation and online motion execution.<n>We validate our framework on the full-size humanoid robot Kuavo 4Pro.
  arXiv  Detail & Related papers  (2026-04-05T07:17:33Z)
• Ultrafast Sampling-based Kinodynamic Planning via Differential Flatness [15.37007491684187]
  AkinoPDF is a fast parallelized sampling-based kinodynamic motion planning technique for a broad class of differentially flat robot systems.<n>Our method is fast, exact, and compatible with any sampling-based motion planner.
  arXiv  Detail & Related papers  (2026-03-17T01:53:10Z)
• Spatial-Temporal Graph Diffusion Policy with Kinematic Modeling for Bimanual Robotic Manipulation [88.83749146867665]
  Existing approaches learn a policy to predict a distant next-best end-effector pose.<n>They then compute the corresponding joint rotation angles for motion using inverse kinematics.<n>We propose Kinematics enhanced Spatial-TemporAl gRaph diffuser.
  arXiv  Detail & Related papers  (2025-03-13T17:48:35Z)
• Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models [57.45019514036948]
  Simultaneous MRMP Diffusion (SMD) is a novel approach integrating constrained optimization into the diffusion sampling process to produce collision-free, kinematically feasible trajectories.<n>The paper introduces a comprehensive MRMP benchmark to evaluate trajectory planning algorithms across scenarios with varying robot densities, obstacle complexities, and motion constraints.
  arXiv  Detail & Related papers  (2025-02-05T20:51:28Z)
• Monte Carlo Tree Search with Velocity Obstacles for safe and efficient motion planning in dynamic environments [49.30744329170107]
  We propose a novel approach for optimal online motion planning with minimal information about dynamic obstacles.<n>The proposed methodology combines Monte Carlo Tree Search (MCTS), for online optimal planning via model simulations, with Velocity Obstacles (VO), for obstacle avoidance.<n>We show the superiority of our methodology with respect to state-of-the-art planners, including Non-linear Model Predictive Control (NMPC), in terms of improved collision rate, computational and task performance.
  arXiv  Detail & Related papers  (2025-01-16T16:45:08Z)
• Multi-Agent Motion Planning For Differential Drive Robots Through Stationary State Search [5.9176395108304805]
  Multi-Agent Motion Planning (MAMP) finds various applications in fields such as traffic management, airport operations, and warehouse automation.<n>This paper introduces a three-level framework called MASS to address these challenges.<n> MASS combines MAPF-based methods with our proposed stationary state search planner to generate high-quality kinodynamically-feasible plans.
  arXiv  Detail & Related papers  (2024-12-17T22:17:42Z)
• Unified Control Framework for Real-Time Interception and Obstacle Avoidance of Fast-Moving Objects with Diffusion Variational Autoencoder [2.5642257132861923]
  Real-time interception of fast-moving objects by robotic arms in dynamic environments poses a formidable challenge. This paper introduces a unified control framework to address the challenge by simultaneously intercepting dynamic objects and avoiding moving obstacles.
  arXiv  Detail & Related papers  (2022-09-27T18:46:52Z)
• Simultaneous Contact-Rich Grasping and Locomotion via Distributed Optimization Enabling Free-Climbing for Multi-Limbed Robots [60.06216976204385]
  We present an efficient motion planning framework for simultaneously solving locomotion, grasping, and contact problems. We demonstrate our proposed framework in the hardware experiments, showing that the multi-limbed robot is able to realize various motions including free-climbing at a slope angle 45deg with a much shorter planning time.
  arXiv  Detail & Related papers  (2022-07-04T13:52:10Z)
• An advantage actor-critic algorithm for robotic motion planning in dense and dynamic scenarios [0.8594140167290099]
  In this paper, we modify existing advantage actor-critic algorithm and suit it to complex motion planning. It achieves higher success rate in motion planning with lesser processing time for robot to reach its goal.
  arXiv  Detail & Related papers  (2021-02-05T12:30:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.