BoB: an online coverage approach for multi-robot systems

Viet, Hoang Huu; Dang, Viet-Hung; Choi, Seung-yoon; Chung, T. J.

doi:10.1007/s10489-014-0571-8

Cited by 35 publications

(29 citation statements)

References 32 publications

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“…Viet et al [28] presented a BA* method that combines boustrophedon motions with the A* search algorithm to reduce the number of BPs in the BSA-CM method. Furthermore, the BoB [10] method, an MRCPP method based on the BA* method, was extended to a multi-cleaning robots version in [10]. When choosing a BP in the BoB method, the robots select the optimal BP using the greedy A* search (GA*) method.…”

Section: Related Workmentioning

confidence: 99%

“…The process of the previous MRCPP method is summarized in Fig. 1 [9], [10]. The three cleaning robots ( 1 , 2 , and 3 ) perform cleaning along a spiral [11] or zigzag path [12] in Fig.…”

Section: Introductionmentioning

confidence: 99%

“…R m uses two convex corners of the collected corners to decompose as the decomposed edge (dotted line, −−) (1, 5), (10,5) and the decomposed edge (13,5), (13,17) . Consequently, R m collects the information for 12 edges and 10 corners as shown in Fig.…”

mentioning

confidence: 99%

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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: Related Workmentioning

confidence: 99%

“…The process of the previous MRCPP method is summarized in Fig. 1 [9], [10]. The three cleaning robots ( 1 , 2 , and 3 ) perform cleaning along a spiral [11] or zigzag path [12] in Fig.…”

Section: Introductionmentioning

confidence: 99%

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

Lee

Kang

2020

IEEE Access

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“…Enric and Carreras 15 presented a comprehensive literature survey on the coverage route planning problem and reported a comprehensive review of the most relevant works in the problem. Viet et al 16 proposed a greedy A* algorithm for multi-robot systems aiming to achieve complete coverage task. It is an online allocating and optimizing method that the result shows inspirations for global CRO method.…”

Section: Coverage Route Optimizingmentioning

confidence: 99%

Route optimizing and following for autonomous underwater vehicle ladder surveys

Jiang

et al. 2018

International Journal of Advanced Robotic Systems

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An autonomous underwater vehicle is able to conduct coverage detections, such as sea terrain mapping and submerged objects detection, using sonar. This work addresses the task of both optimizing and following routes that present a ladder shape. First, a planning method to determine a nearly optimal coverage route is designed. The track spacing is optimized considering the seabed type and the sonar range for the purpose of increasing detection probability. It also adds adaptability to confined water, such as harbors, by decomposing the geometrically concave mission region during the processing of the environmental data. Next, a decoupled and two-layered structure is adopted to design the following controller. The route is followed in the form of sequenced-lines tracking. A proportion-integral-derivative algorithm with fuzzy parameters adjustment is employed to calculate a reference heading angle according to the transverse position deviation in designing the guidance controller. An adaptive nonlinear S-surface law is adopted to design the yaw control. The route following the method is demonstrated with sonar (including side scanning sonar and multi-beam echo sounder) imagery collected in terrain mapping and object detection through sea trials.

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“…Well-known coverage strategies include the boustrophedon decomposition coverage with the back-and-forth motion [4], [11], spiral path coverage [8], and spanning-tree based coverage [6]. The boustrophedon coverage and spiral path coverage can be adapted to perform in an online fashion by tracing the uncovered area [14] [3]. The objective of these problems is to completely cover the environment.…”

Section: Introductionmentioning

confidence: 99%

Coverage Path Planning Under the Energy Constraint

Wei

Isler

2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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In coverage applications, a common assumption is that the robot can fully cover the environment without recharging. However, in reality most mobile robot systems operate under battery limitations. To incorporate this constraint, we consider the problem when the working environment is large and the robot needs to recharge multiple times to fully cover the environment. We focus on a geometric version where the environment is represented as a polygonal grid with a single charging station. Energy consumption throughout the environment is assumed to be uniform and proportional to the distance traveled. We first present a constant-factor approximation algorithm for contour-connected environments. We then extend the algorithm for general environments. We also validate the results in experiments performed with an aerial robot.

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BoB: an online coverage approach for multi-robot systems

Cited by 35 publications

References 32 publications

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

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

Route optimizing and following for autonomous underwater vehicle ladder surveys

Coverage Path Planning Under the Energy Constraint

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