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Distributed Multi-Agent Coverage Path Planning Over Graphs With Relaxed Priority Rule
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AI
DOI：10.1109/TVT.2024.3408165.png)
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摘要
En 中文
This paper addresses the distributed coverage path planning problem over graphs with relaxed priority rule considering heterogeneity of the agents involved. Since the underlying problem is NP-hard, its convex relaxation is formulated to give an approximated optimal path. It is shown that the convex relaxation problem is of the form of a distributed optimization with coupled constraints. A fully parallel distributed (offline) algorithm is proposed to find an approximated optimal path of each agent under synchronous protocol. Since the algorithm is fully parallel, the time per iteration of each agent is significantly reduced. The theoretical results are demonstrated by an application to monitor and collect data in important areas of a large field. Finally, two numerical examples are provided in order to demonstrate the efficacy of the proposed method and its advantages.
Keyword:
Costs
Optimization
Robots
Vectors
Path planning
Task analysis
Approximation algorithms
Distributed coverage path planning
multi-agent systems
graphs
relaxed priority rule
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
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