Frontier-Graph Exploration for Multi-robot Systems in an Unknown Indoor Environment

Abstract-Task assignment is paramount for the efficiency of multi-robot missions. In the context of multi-robot exploration of unknown environments, tasks can be identified as targets to reach in order to expand knowledge of the environment. These targets can be frontiers between known reachable area and unknown areas or a best view configuration to observe each frontiers. Once these tasks are identified, they are assigned to robots. Standard approaches work in a two step process where the costs of reaching targets are computed before running a task allocation algorithm. In this article, we combine these two steps to propose an efficient task allocation algorithm. This algorithm can take into account different assignment criteria such as the rank of a robot in a list of robots ordered by cost.

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“…Gossage et al [6] and Wurm et al [7] use a Voronoï discretization. The nodes of the built topological map are associated with the Voronoï cells.…”

Section: A Target Identificationmentioning

confidence: 99%

SyWaP: Synchronized wavefront propagation for multi-robot assignment of spatially-situated tasks

Bautin

Simonin

Charpillet

2013

2013 16th International Conference on Advanced Robotics (ICAR)

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“…The effective, robust, and complete multi-robot coverage is implemented. Gossage et al [34] combined the advantages, in order to obtain robust cooperation, of local Frontier-based approach and global graph-based representation of unknown environments for the cooperation of multirobot. Most recently, Zavlanos and Pappas [45] combined a distributed multi-destination potential field approach and a dynamic assignment algorithm for coverage motion planning of multiple robots.…”

Section: Introductionmentioning

confidence: 99%

Biologically Inspired Intelligence with Applications on Robot Navigation

Luo¹,

Jan²,

Chu³

et al. 2018

Artificial Intelligence - Emerging Trends and Applications

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Biologically inspired intelligence technique, an important embranchment of series on computational intelligence, plays a crucial role for robotics. The autonomous robot and vehicle industry has had an immense impact on our economy and society and this trend will continue with biologically inspired neural network techniques. In this chapter, multiple robots cooperate to achieve a common coverage goal efficiently, which can improve the work capacity, share the coverage tasks, and reduce the completion time by a biologically inspired intelligence technique, is addressed. In many real-world applications, the coverage task has to be completed without any prior knowledge of the environment. In this chapter, a neural dynamics approach is proposed for complete area coverage by multiple robots. A bio-inspired neural network is designed to model the dynamic environment and to guide a team of robots for the coverage task. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting neural equation. Each mobile robot treats the other robots as moving obstacles. Each robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot position. The proposed model algorithm is computationally simple. The feasibility is validated by four simulation studies.

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Frontier-Graph Exploration for Multi-robot Systems in an Unknown Indoor Environment

Cited by 2 publications

References 6 publications

SyWaP: Synchronized wavefront propagation for multi-robot assignment of spatially-situated tasks

SyWaP: Synchronized wavefront propagation for multi-robot assignment of spatially-situated tasks

Biologically Inspired Intelligence with Applications on Robot Navigation

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