BA*: an online complete coverage algorithm for cleaning robots

Viet, Hoang Huu; Dang, Viet-Hung; Laskar, Nasir Uddin; Chung, T. J.

doi:10.1007/s10489-012-0406-4

Cited by 100 publications

(70 citation statements)

References 18 publications

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“…Viet et al [7] utilize a commercial round shaped vacuum cleaning robot by iRobot to experimentally demonstrate a coverage path planning algorithm, in which critical points are used as backtracking points for a new bousdrophedon motion.…”

Section: Related Workmentioning

confidence: 99%

Autonomous robotic SLAM-based indoor navigation for high resolution sampling with complete coverage

Wieser

Ruiz

Frassl

et al. 2014

2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014

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Recent work has shown the feasibility of pedestrian and robotic indoor localization based only on maps of the magnetic field. To obtain a complete representation of the magnetic field without initial knowledge of the environment or any existing infrastructure, we consider an autonomous robotic platform to reduce limitations of economic or operational feasibility. Therefore, we present a novel robotic system that autonomously samples any measurable physical processes at high spatial resolution in buildings without any prior knowledge of the buildings' structure. In particular we focus on adaptable robotic shapes, kinematics and sensor placements to both achieve complete coverage in hardly accessible areas and not be limited to round shaped robots. We propose a grid based representation of the robot's configuration space and graph search algorithms, such as Best-First-Search and an adaption of Dijkstra's algorithm, to guarantee complete path coverage. In combination with an optical simultaneous localization and mapping (SLAM) algorithm, we present experimental results by sampling the magnetic field in an a priori unknown office with a robotic platform autonomously and completely.

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Section: Related Workmentioning

confidence: 99%

Autonomous robotic SLAM-based indoor navigation for high resolution sampling with complete coverage

Wieser

Ruiz

Frassl

et al. 2014

2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014

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“…Such a use-case motion planning is called offline decision making or static motion planning. When the obstacles are not static (that is, they are moving or dynamic obstacles), motion planning is termed as on-line or dynamic motion planning [1,2,[5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, algorithms have been developed using an on-line coverage-based approach, the 'boustrophedon division' algorithm, typically the BA* algorithm [9][10][11][12]. The BA* algorithm performs a search using a backtracking mechanism to ensure that all regions are found, and the workspace of the robot is covered completely.…”

Section: Introductionmentioning

confidence: 99%

“…In a known environment, the cleaning robot applies to model a graph-based representation, based on the known environmental information; The BA* algorithm then uses the method of graph traversal for coverage path planning. In an unknown environment, the learning robot employs a sensing system to explore and identify the environment [9][10][11][12]. The approach uses modeling, and generates a graph based on the identified environmental information in the coverage path planning.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Spiral STC-Hedge Algebras Model in Knowledge Reasonings for Robot Coverage Path Planning and Its Applications

et al. 2019

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Robotics is a highly developed field in industry, and there is a large research effort in terms of humanoid robotics, including the development of multi-functional empathetic robots as human companions. An important function of a robot is to find an optimal coverage path planning, with obstacle avoidance in dynamic environments for cleaning and monitoring robotics. This paper proposes a novel approach to enable robotic path planning. The proposed approach combines robot reasoning with knowledge reasoning techniques, hedge algebra, and the Spiral Spanning Tree Coverage (STC) algorithm, for a cleaning and monitoring robot with optimal decisions. This approach is used to apply knowledge inference and hedge algebra with the Spiral STC algorithm to enable autonomous robot control in the optimal coverage path planning, with minimum obstacle avoidance. The results of experiments show that the proposed approach in the optimal robot path planning avoids tangible and intangible obstacles for the monitoring and cleaning robot. Experimental results are compared with current methods under the same conditions. The proposed model using knowledge reasoning techniques in the optimal coverage path performs better than the conventional algorithms in terms of high robot coverage and low repetition rates. Experiments are done with real robots for cleaning in dynamic environments.

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“…Recently Viet et al combined the boustrophedon cell decomposition with an A* path planner for finding shortest paths to the next target location [21].…”

Section: Introductionmentioning

confidence: 99%

Systematic floor coverage of unknown environments using rectangular regions and localization certainty

Goel

Case

Tamino

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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We address the problem of systematically covering all accessible floor space in an unknown environment by a mobile robot. Our approach uses rectangular regions that are swept across the environment. In the first stage, the robot covers each region using the classic boustrophedon pattern and planning paths to uncovered areas within the region while keeping track of its position uncertainty. The region is then moved sideways to cover the next part of the environment until all accessible space has been visited. In the second stage, the robot revisits the perimeter around the obstacles. We compare our method in terms of total trajectory length to 5 off-line methods including the distance transformation by Zelinsky ET AL.[1] in a standard test environment as well as in multi-room homes. The presented method has been employed in our Mint cleaning robot [2] for autonomously sweeping and mopping the floors.

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BA*: an online complete coverage algorithm for cleaning robots

Cited by 100 publications

References 18 publications

Autonomous robotic SLAM-based indoor navigation for high resolution sampling with complete coverage

Autonomous robotic SLAM-based indoor navigation for high resolution sampling with complete coverage

Hybrid Spiral STC-Hedge Algebras Model in Knowledge Reasonings for Robot Coverage Path Planning and Its Applications

Systematic floor coverage of unknown environments using rectangular regions and localization certainty

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