弹道
避碰
沃罗诺图
计算机科学
机器人
模型预测控制
控制理论(社会学)
点(几何)
无人机
事件(粒子物理)
跟踪(教育)
控制(管理)
模拟
数学优化
实时计算
碰撞
人工智能
数学
几何学
计算机安全
心理学
物理
教育学
生物
量子力学
天文
遗传学
作者
Carlos E. Luis,Marijan Vukosavljev,Angela P. Schoellig
出处
期刊:IEEE robotics and automation letters
日期:2020-04-01
卷期号:5 (2): 604-611
被引量:110
标识
DOI:10.1109/lra.2020.2964159
摘要
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.
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