Multirobot Cooperative Model Applied to Coverage of Unknown Regions

Gerlein, Eduardo A.; González, Enríque

doi:10.5772/13241

Cited by 8 publications

(1 citation statement)

References 20 publications

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“…The number of multi-cleaning robots was set to 2-4. The performance of the BSA-CM [9], [48], the BoB method [10], and the proposed MRCPP method was compared using a set number of multi-cleaning robots.…”

Section: Resultsmentioning

confidence: 99%

Multi-Cleaning Robots Using Cleaning Distribution Method Based on Map Decomposition in Large Environments

Lee

Kang

2020

IEEE Access

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Most cleaning robots have a good cleaning performance for small environments such as houses but require a longer cleaning time due to problems such as slow cleaning progress and low battery capacity, making the robots unsuitable for large environments such as libraries and airports. Cleaning large environments with multiple robots is faster than cleaning them with a single robot. Multi-cleaning robots can utilize several robots to simultaneously clean and share the task of cleaning among the robots. However, as the number of robots increases and the effective distribution of cleaning is not efficient during the cleaning process, the cleaning time will consequently be longer due to the frequent collisions between the robots. Therefore, to shorten the cleaning time, multi-cleaning robots require a coverage path planning that uses an effective cleaning distribution method in the cleaning process. In this paper, a coverage path planning using a cleaning distribution method based on map decomposition is proposed to reduce the cleaning time of multi-cleaning robots. The experimental results demonstrated that the proposed multi-cleaning robots' coverage path planning could be used in large environments in the presence of several types of obstacles. Furthermore, the cleaning time was found to be shorter than that of the previous methods in the case of multi-cleaning robots. INDEX TERMS Coverage path planning, cleaning robot, computational efficiency, multi-robots system, robot path planning.

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Section: Resultsmentioning

confidence: 99%