End-to-end decentralized formation control using a graph neural network-based learning method.

ABSTRACT: Multi-robot cooperative control has been extensively studied using model-based distributed control methods. However, such control methods rely on sensing and perception modules in a sequential pipeline design, and the separation of perception and controls may cause processing latencies and compounding errors that affect control performance. End-to-end learning overcomes this limitation by implementing direct learning from onboard sensing data, with control commands output to the robots. Challenges exist in end-to-end learning for multi-robot cooperative control, and previous results are not scalable. We propose in this article a novel decentralized cooperative control method for multi-robot formations using deep neural networks, in which inter-robot communication is modeled by a graph neur

SUBMITTER: Jiang C 

PROVIDER: S-EPMC10661938 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature