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Planning coverage path for multiple robots in a decentralized way enhancesrobustness to coverage tasks handling uncertain malfunctions. To achieve highefficiency in a distributed manner for each single robot, a comprehensiveunderstanding of both the complicated environments and cooperative agentsintent is crucial. Unfortunately, existing works commonly consider only part ofthese factors, resulting in imbalanced subareas or unnecessary overlaps. Totackle this issue, we introduce a Decentralized reinforcement learningframework with dual guidance to train each agent to solve the decentralizedmultiple coverage path planning problem straightly through the environmentstates. As distributed robots require others intentions to perform bettercoverage efficiency, we utilize two guidance methods, artificial potentialfields and heuristic guidance, to include and integrate others intentions intoobservations for each robot. With our constructed framework, results have shownour agents successfully learn to determine their own subareas while achievingfull coverage, balanced subareas and low overlap rates. We then implementspanning tree cover within those subareas to construct actual routes for eachrobot and complete given coverage tasks. Our performance is also compared withthe state of the art decentralized method showing at most 10 percent loweroverlap rates while performing high efficiency in similar environments.

Decentralized Coverage Path Planning with Reinforcement Learning and Dual Guidance