Constructing spanning trees for efficient multi-robot coverage

Agmon, Noa; Hazon, Noam; Kaminka, Gal A.

doi:10.1109/robot.2006.1641951

Cited by 84 publications

(68 citation statements)

References 8 publications

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“…Then, we build a spanning tree for G using any spanning-tree construction algorithm. We can affect the shape of the covering path by adding weights to the edges and building a minimum spanning tree [1,22]. This can be used, for instance, to reduce the number of turns, by assigning horizontal edges greater weights than those of vertical edges [8].…”

Section: Dmentioning

confidence: 99%

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On redundancy, efficiency, and robustness in coverage for multiple robots

Hazon

Kaminka

2008

Robotics and Autonomous Systems

113

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Section: Dmentioning

confidence: 99%

“…The function Solution uses only a constant number of check so its running time complexity is O (1). So, the overall running time complexity…”

Section: Algorithmmentioning

confidence: 99%

On redundancy, efficiency, and robustness in coverage for multiple robots

Hazon

Kaminka

2008

Robotics and Autonomous Systems

113

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“…The process of constructing the k subtrees is done while spreading each tree away from the other trees. The distance measure used to determine how distant the trees are was initially simply the Manhattan distance ( [2]). In this work, we have used three different distance measures: Manhattan distance, Euclidean distance and Shortest paths (following Floyd's all-pairs shortest paths algorithm [7]).…”

Section: Using Different Distance Measuresmentioning

confidence: 99%

The giving tree: constructing trees for efficient offline and online multi-robot coverage

Agmon

Hazon

Kaminka

2008

Ann Math Artif Intell

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This paper discusses the problem of building efficient coverage paths for a team of robots. An efficient multi-robot coverage algorithm should result in a coverage path for every robot, such that the union of all paths generates a full coverage of the terrain and the total coverage time is minimized. A method underlying several coverage algorithms, suggests the use of spanning trees as base for creating coverage paths. However, overall performance of the coverage is heavily dependent on the given spanning tree. This paper focuses on the challenge of constructing a coverage spanning tree for both online and offline coverage that minimizes the time to complete coverage. Our general approach involves building a spanning tree by growing sub-trees from the initial location of the robots. This paper first describes a polynomial time tree-construction algorithm for offline coverage. The use of this algorithm is shown by extensive simulations to significantly improve the coverage time of the terrain even when used as a basis for a simple, inefficient, coverage algorithm. Second, this paper provides an algorithm for online coverage of a finite terrain based on spanning-trees, that is complete and guarantees linear time coverage with no redundancy in the coverage. In addition, the solutions proposed by this paper guarantee robustness to failing robots: the offline trees are used as base for robust multi-robot coverage algorithms, and the online algorithm is proven to be robust.

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“…For instance, there exist coverage algorithms that only require bumper sensors [6], and others that use long range sensors for decomposing the environment [3], [7]. Also, literature distinguishes between approaches that plan the robots' trajectories off-line [2], [8], and those that perform coverage on-line, in which case the environment needs not to be known in advance [3], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Robust Distributed Coverage using a Swarm of Miniature Robots

Correll

Martinoli

2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

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Abstract-For the multi-robot coverage problem deterministic deliberative as well as probabilistic approaches have been proposed. Whereas deterministic approaches usually provide provable completeness and promise good performance under perfect conditions, probabilistic approaches are more robust to sensor and actuator noise, but completion cannot be guaranteed and performance is sub-optimal in terms of time to completion. In reality, however, almost all deterministic algorithms for robot coordination can be considered probabilistic when considering the unpredictability of real world factors. This paper investigates experimentally and analytically how probabilistic and deterministic algorithms can be combined for maintaining the robustness of probabilistic approaches, and explicitly model the reliability of a robotic platform. Using realistic simulation and data from real robot experiments, we study system performance of a swarm-robotic inspection system at different levels of noise (wheel-slip). The prediction error of a purely deterministic model increases when the assumption of perfect sensors and actuators is violated, whereas a combination of probabilistic and deterministic models provides a better match with experimental data.

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Constructing spanning trees for efficient multi-robot coverage

Cited by 84 publications

References 8 publications

On redundancy, efficiency, and robustness in coverage for multiple robots

On redundancy, efficiency, and robustness in coverage for multiple robots

The giving tree: constructing trees for efficient offline and online multi-robot coverage

Robust Distributed Coverage using a Swarm of Miniature Robots

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