Concurrent-Learning-Based Adaptive Critic Formation for Multirobots Under Safety Constraints

This article presents a concurrent learning-based adaptive critic formation for multirobots under safety constraints, which comprises of an initial formation consensus item and a collision-free adaptive critic policy. First, based on directed graph communication, an initial formation consensus item...

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Vydáno v:IEEE internet of things journal Ročník 12; číslo 6; s. 7610 - 7621
Hlavní autoři: Cheng, Yunjie, Shao, Xingling, Li, Jiangmiao, Liu, Jun, Zhang, Qingzhen
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 15.03.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptive critic formation
Artificial neural networks
Automation
Collision avoidance
concurrent learning
Constraints
Convergence
Dynamic programming
Formation control
Graph theory
Instruments
Internet of Things
Learning
Moving obstacles
Multiple robots
multirobots
Optimal control
Penalty function
Real time
Robot kinematics
Safety
safety constraints
Safety critical
Vectors
ISSN:2327-4662, 2327-4662
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Shrnutí:This article presents a concurrent learning-based adaptive critic formation for multirobots under safety constraints, which comprises of an initial formation consensus item and a collision-free adaptive critic policy. First, based on directed graph communication, an initial formation consensus item is designed to maintain the velocity agreement under a leader-follower setting. Particularly, a collision-free adaptive critic policy is developed that enables robots to preserve formation configuration with the minimum cost while excluding collisions caused by inter-robots and static/moving obstacles, wherein safety constraints encoded by an elegantly devised penalty function are enforced by converting constrained optimal control into unconstrained optimal control issue. Furthermore, by revisiting real-time and historical information, a concurrent weight learning rule is elaborated under a critic-only adaptive dynamic programming, improving the weight convergence without demanding the persistence excitation conditions. The remarkable benefits outperforming existing outcomes are safety-critical coordination with energy-saving performances is assured under a computationally efficient optimal learning paradigm. Involved errors are theoretically proved to be convergent. Finally, the values and superiorities are verified through extensive simulations on 2-D and 3-D multirobots.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2024.3497979