Online Trajectory Generation With Distributed Model Predictive Control for Multi-Robot Motion Planning

We present a distributed model predictive control (DMPC) algorithm to generate trajectories in real-time for multiple robots. We adopted the on-demand collision avoidance method presented in previous work to efficiently compute non-colliding trajectories in transition tasks. An event-triggered repla...

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Vydáno v:IEEE robotics and automation letters Ročník 5; číslo 2; s. 604 - 611
Hlavní autoři: Luis, Carlos E., Vukosavljev, Marijan, Schoellig, Angela P.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.04.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Collision avoidance
Computer simulation
distributed robot systems
model predictive control
Motion and path planning
Motion planning
Multiagent systems
Multiple robots
Planning
Predictive control
Real-time systems
Robot control
Robot dynamics
Robots
Task analysis
Trajectories
Trajectory
Travel time
ISSN:2377-3766, 2377-3766
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Shrnutí:We present a distributed model predictive control (DMPC) algorithm to generate trajectories in real-time for multiple robots. We adopted the 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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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2020.2964159