Distributed Safe Cooperative Vector Field for Trajectory Curvature Constrained Multi-Robot Systems

• URL: http://arxiv.org/abs/2609.15266v1
• Date: Mon, 14 Sep 2026 09:26:39 GMT
• Title: Distributed Safe Cooperative Vector Field for Trajectory Curvature Constrained Multi-Robot Systems
• Abstract summary: Trajectory curvature constraints are inherent in practical multi-robot systems.<n>Without properly accounting for these constraints, robots may fail to accomplish assigned tasks.<n>This paper proposes a distributed safe cooperative vector field approach for multi-robot systems.
• Score: 33.11742595438937
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Trajectory curvature constraints are inherent in practical multi-robot systems due to the limited turning capabilities of the robots. Without properly accounting for these constraints, robots may fail to accomplish assigned tasks, and their trajectories may diverge from the intended paths. This paper proposes a distributed safe cooperative vector field approach for multi-robot systems subject to trajectory curvature constraints. The proposed approach is composed of a cooperative vector field and a safety-oriented collision avoidance vector field, aiming to address the problems of cooperative motion and safe collision avoidance in multi-robot path-following tasks. A safety-oriented collision avoidance vector field with adaptively adjustable reactive boundary is developed to accommodate the kinematic curvature constraints of robots, thereby ensuring the physical feasibility of collision avoidance maneuvers. The proposed vector field requires only a single virtual variable from each neighboring robot to achieve cooperative motion and ensure both obstacle avoidance and inter-robot collision avoidance. The effectiveness of the proposed approach is validated through both simulations and real-world experiments on an actual multi-robot platform.

Related papers

• Embodying Multi-Hand Manipulation Policies by Searching the Assignment and Null Spaces [19.655642356469748]
  Learned manipulation policies increasingly predict motions for abstract "hands"<n>Executing these trajectories on multi-arm robots is not trivial.<n>In the absence of algorithms that directly address this problem, practitioners typically extend single-arm inverse-kinematics pipelines in an ad hoc way.
  arXiv  Detail & Related papers  (2026-07-24T06:37:52Z)
• Distributed Motion Planning with Safety Guarantees for Self-Reconfiguring Robotic Boats [76.98547580312116]
  This paper proposes a hybrid framework that combines distributed Model Predictive Control (MPC) with Control Barrier Functions (CBFs)<n>To ensure safety in real time, distributed CBF-based filters are applied to enforce inter-agent avoidance.
  arXiv  Detail & Related papers  (2026-07-22T16:37:05Z)
• Real-Time and Accurate Collision-Free Teleoperation via Differentiable Constraint-Based Trajectory Planning [1.4699455652461726]
  A common strategy to mitigate this issue is to enhance the operator's input using optimal-control-based trajectory planning.<n>We adapt a recent formulation of differentiable collision-avoidance constraints, based on duality in convex optimization, to the teleoperation setting.<n>Results show that our approach achieves lower computation times while enabling more accurate obstacle modeling.
  arXiv  Detail & Related papers  (2026-06-07T16:39:56Z)
• 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)
• Sliding Mode Control for Safe Trajectory Tracking with Moving Obstacles Avoidance: Experimental Validation on Planar Robots [1.7044368816068716]
  This paper presents a unified control framework for robust trajectory tracking and moving obstacle avoidance.<n>The proposed architecture guarantees tracking in the presence of external disturbances while strictly enforcing collision avoidance constraints.<n>The efficacy and versatility of the approach are validated through numerical simulations and extensive real-world experiments.
  arXiv  Detail & Related papers  (2026-04-27T14:20:10Z)
• Capability-Aware Heterogeneous Control Barrier Functions for Decentralized Multi-Robot Safe Navigation [19.794822517328654]
  Existing decentralized methods assume robot homogeneity, making shared safety requirements non-uniformly interpreted across heterogeneous agents.<n>We propose Capability-Aware Heterogeneous Control Barrier Function (CA-HCBF), a decentralized framework for consistent safety enforcement and capability-aware coordination.
  arXiv  Detail & Related papers  (2026-04-14T19:21:46Z)
• Discrete-Guided Diffusion for Scalable and Safe Multi-Robot Motion Planning [56.240199425429445]
  Multi-Robot Motion Planning (MPMP) involves generating trajectories for multiple robots operating in a shared continuous workspace.<n>While discrete multi-agent finding (MAPF) methods are broadly adopted due to their scalability, their coarse discretization trajectory quality.<n>This paper tackles limitations of two approaches by introducing discrete MAPF solvers with constrained generative diffusion models.
  arXiv  Detail & Related papers  (2025-08-27T17:59:36Z)
• 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)
• SERA: Safe and Efficient Reactive Obstacle Avoidance for Collaborative Robotic Planning in Unstructured Environments [1.5229257192293197]
  We propose a novel methodology for reactive whole-body obstacle avoidance. Our approach allows a robotic arm to proactively avoid obstacles of arbitrary 3D shapes without direct contact. Our methodology provides a robust and effective solution for safe human-robot collaboration in non-stationary environments.
  arXiv  Detail & Related papers  (2022-03-24T21:11:43Z)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)
• Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction [55.569050872780224]
  We present an online framework for safe crowd-robot interaction based on risk-sensitive optimal control, wherein the risk is modeled by the entropic risk measure. Our modular approach decouples the crowd-robot interaction into learning-based prediction and model-based control. A simulation study and a real-world experiment show that the proposed framework can accomplish safe and efficient navigation while avoiding collisions with more than 50 humans in the scene.
  arXiv  Detail & Related papers  (2020-09-12T02:02:52Z)

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.