Chance-Constrained MPC for Voronoi-based Multi-Agent System Deployment

Chevet, Thomas; Maniu, Cristina Stoica; Vlad, Cristina; Zhang, Youmin; Camacho, Eduardo F.

doi:10.1016/j.ifacol.2020.12.417

Cited by 1 publication

(1 citation statement)

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“…Cortes et al [15] introduced distributed control laws using non-smooth analysis and computational geometry, which have applications in network robot coordination problems. Thomas Chevet [16] drove a multi-intelligent system into a static Chebyshev structure by proposing a new chance-constrained model predictive control algorithm-based on-state estimation, where each agent is located at the Chebyshev center of its Voronoi cell, and the noise problem in the system model is effectively solved. Ref.…”

Section: Introductionmentioning

confidence: 99%

Distributed Weighted Coverage for Multi-Robot Systems in Non-Convex Environment

et al. 2023

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Multi-robot coverage systems are widely used in operations such as environmental monitoring, disaster rescue, and pollution prevention. This study considers inherent positioning errors in positioning systems and ground mobile robots with limited communication distance and poor quality in practice. A centroidal Voronoi tessellation algorithm-based formation control technology for multi-robots is optimized. First, by constructing buffered Voronoi cells (BUVCs) for each robot, the collision avoidance ability of the multi-robot formation movement is improved. Next, the formation control problem of multi-robots in a limited communication range and non-convex environment is realized via discrete Voronoi partitioning, a communication distance constraint, and an obstacle avoidance strategy. Simulation and experiment results demonstrate that the proposed method can effectively solve the position generation problem of multi-robot coverage systems in a non-convex environment with actual sizes of the robots and positioning system errors and can further improve the collision avoidance performance of robots and the robustness of BUVC algorithms.

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Section: Introductionmentioning

confidence: 99%