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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 neural network (GNN). Our method takes LiDAR sensor data as input, and the control policy is learned from demonstrations that are provided by an expert controller for decentralized formation control. Although it is trained with a fixed number of robots, the learned control policy is scalable. Evaluation in a robot simulator demonstrates the triangular formation behavior of multi-robot teams of different sizes under the learned control policy.
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Stabilization via Distributed Control
We consider the linear plant with multiple pairs of input and output channels, and solve the problem of designing a stabilizing controller consisting of a set of control stations, one for each channel pair, connected with an arbitrarily fixed network topology and communication dynamics. It is shown that such stabilizing distributed controller exists if and only if the fixed modes of the plant, augmented with the network communication, are stable; this leverages and extends the classical fixed mode result for decentralized control. Moreover, a condition is given to reveal exactly what connections are needed between control stations to achieve stabilizability via a distributed control. Thus, the feasibility of optimal distributed control synthesis can readily be checked or enforced before applying computationally expensive optimization methods. Stabilizing distributed controllers, if feasible, can be designed using decentralized control theories.
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- Award ID(s):
- 2113528
- PAR ID:
- 10667037
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Transactions on Automatic Control
- ISSN:
- 0018-9286
- Page Range / eLocation ID:
- 1 to 8
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1109/TAC.2025.3614507
