分散モデル予測制御によるマルチロボット軌道生成
Online Trajectory Generation with Distributed Model Predictive Control for Multi-Robot Motion Planning
複数ロボットの衝突回避軌道をリアルタイム生成する分散MPC手法を提案し、イベントトリガー再計画で外乱に対応。シミュレーションと最大20機のドローン群で実証した。
著者: Carlos E. Luis, Marijan Vukosavljev, Angela P. Schoellig
分類: cs.RO
原文アブストラクト
We present a distributed model predictive control (DMPC) algorithm to generate trajectories in real-time for multiple robots. We adopted the \textit{on-demand collision avoidance} method presented in previous work to efficiently compute non-colliding trajectories in transition tasks. An event-triggered replanning strategy is proposed to account for disturbances. Our simulation results show that the proposed collision avoidance method can reduce, on average, around 50% of the travel time required to complete a multi-agent point-to-point transition when compared to the well-studied Buffered Voronoi Cells (BVC) approach. Additionally, it shows a higher success rate in transition tasks with a high density of agents, with more than 90% success rate with 30 palm-sized quadrotor agents in a 18 m^3 arena. The approach was experimentally validated with a swarm of up to 20 drones flying in close proximity.
