Multi-MAV Autonomous Full Coverage Search in Cluttered Forest Environments

Xu, Xin; Marelli, Damián; Meng, Wei; Zhang, Fumin; Cai, Qianqian; Fu, Minyue

doi:10.1007/s10846-022-01723-z

Cited by 2 publications

(1 citation statement)

References 41 publications

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“…More recently, the authors in [26] have proposed a deep reinforcement learning based 3D area coverage approach with a swarm of UAV agents, whereas in [27] a multi-robot coverage approach is proposed based on spatial graph neural networks. Moreover, in [28] the authors investigate the problem of full coverage search with multiple agents in cluttered environments, and finally, the work in [29] proposes a distributed sweep coverage algorithm for multiagent systems in uncertain environments.…”

Section: Related Workmentioning

confidence: 99%

Distributed Search Planning in 3-D Environments With a Dynamically Varying Number of Agents

Papaioannou

Kolios

Theocharides

et al. 2023

IEEE Trans. Syst. Man Cybern, Syst.

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In this work, a novel distributed search-planning framework is proposed, where a dynamically varying team of autonomous agents cooperate in order to search multiple objects of interest in 3D. It is assumed that the agents can enter and exit the mission space at any point in time, and as a result the number of agents that actively participate in the mission varies over time. The proposed distributed search-planning framework takes into account the agent dynamical and sensing model, and the dynamically varying number of agents, and utilizes model predictive control (MPC) to generate cooperative search trajectories over a finite rolling planning horizon. This enables the agents to adapt their decisions on-line while considering the plans of their peers, maximizing their search planning performance, and reducing the duplication of work.

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