A Framework for Multi-Agent UAV Exploration and Target-Finding in GPS-Denied and Partially Observable Environments.

Clicks: 274
ID: 110434
2020
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Abstract
The problem of multi-agent remote sensing for the purposes of finding survivors or surveying points of interest in GPS-denied and partially observable environments remains a challenge. This paper presents a framework for multi-agent target-finding using a combination of online POMDP based planning and Deep Reinforcement Learning based control. The framework is implemented considering planning and control as two separate problems. The planning problem is defined as a decentralised multi-agent graph search problem and is solved using a modern online POMDP solver. The control problem is defined as a local continuous-environment exploration problem and is solved using modern Deep Reinforcement Learning techniques. The proposed framework combines the solution to both of these problems and testing shows that it enables multiple agents to find a target within large, simulated test environments in the presence of unknown obstacles and obstructions. The proposed approach could also be extended or adapted to a number of time sensitive remote-sensing problems, from searching for multiple survivors during a disaster to surveying points of interest in a hazardous environment by adjusting the individual model definitions.
Reference Key
walker2020asensors Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Walker, Ory;Vanegas, Fernando;Gonzalez, Felipe;
Journal sensors
Year 2020
DOI
E4739
URL
Keywords
uav search deep reinforcement-learning pomdp multi-agent

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