Data structures and network algorithms
Data structures and network algorithms
Efficiently searching a graph by a smell-oriented vertex process
Annals of Mathematics and Artificial Intelligence
Spanning-tree based coverage of continuous areas by a mobile robot
Annals of Mathematics and Artificial Intelligence
Coverage for robotics – A survey of recent results
Annals of Mathematics and Artificial Intelligence
Exhaustive Geographic Search with Mobile Robots Along Space-Filling Curves
CRW '98 Proceedings of the First International Workshop on Collective Robotics
The Pemex-B autonomous demining robot: perception and navigation strategies
IROS '95 Proceedings of the International Conference on Intelligent Robots and Systems-Volume 1 - Volume 1
Building Terrain-Covering Ant Robots: A Feasibility Study
Autonomous Robots
Multi-robot exploration of an unknown environment, efficiently reducing the odometry error
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
The giving tree: constructing trees for efficient offline and online multi-robot coverage
Annals of Mathematics and Artificial Intelligence
Modeling floor-cleaning coverage performances of some domestic mobile robots in a reduced scenario
Robotics and Autonomous Systems
Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning
Artificial Intelligence Review
Information Sciences: an International Journal
Multi-robot area patrol under frequency constraints
Annals of Mathematics and Artificial Intelligence
Evolving robotic path with genetically optimised fuzzy planner
International Journal of Computational Vision and Robotics
Weighted voting game based multi-robot team formation for distributed area coverage
Proceedings of the 3rd International Symposium on Practical Cognitive Agents and Robots
Multi-agent coalition formation for distributed area coverage
CARE@AI'09/CARE@IAT'10 Proceedings of the CARE@AI 2009 and CARE@IAT 2010 international conference on Collaborative agents - research and development
Boundary patrolling by mobile agents with distinct maximal speeds
ESA'11 Proceedings of the 19th European conference on Algorithms
Multi-robot exploration and terrain coverage in an unknown environment
Robotics and Autonomous Systems
Adaptive multi-robot team reconfiguration using a policy-reuse reinforcement learning approach
AAMAS'11 Proceedings of the 10th international conference on Advanced Agent Technology
Wireless Communication in Mobile Robotics a Case for Standardization
Wireless Personal Communications: An International Journal
Multi-robot repeated area coverage
Autonomous Robots
Optimal patrolling of fragmented boundaries
Proceedings of the twenty-fifth annual ACM symposium on Parallelism in algorithms and architectures
Journal of Intelligent and Robotic Systems
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Motivated by potential efficiency and robustness gains, there is growing interest in the use of multiple robots for coverage. In coverage, robots visit every point in a target area, at least once. Previous investigations of multi-robot coverage focus on completeness of the coverage, and on eliminating redundancy, but do not formally address robustness. Moreover, a common assumption is that elimination of redundancy leads to improved efficiency (coverage time). We address robustness and efficiency in a novel family of multi-robot coverage algorithms, based on spanning-tree coverage of approximate cell decomposition of the work-area. We analytically show that the algorithms are robust, in that as long as a single robot is able to move, the coverage will be completed. We also show that non-redundant (non-backtracking) versions of the algorithms have a worst-case coverage time virtually identical to that of a single robot-thus no performance gain is guaranteed in non-redundant coverage. Surprisingly, however, redundant coverage algorithms lead to guaranteed performance which halves the coverage time even in the worst case. We present a polynomial-time redundant coverage algorithm, whose coverage time is optimal, and which is able to address robots heterogeneous in speed and fuel. We compare the performance of all algorithms empirically and show that the use of the optimal algorithm leads to significant improvements in coverage time.