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This paper describes a new method of path-planning for multiple robots in unknown environments.
In many applications multiple robots in Mobile Ad-hoc Networks are required to collaborate in order to solve a task.
The algorithm developed in this work for path-planning of multiple robots in unknown environments, named Space D*, relates the D* algorithm with the Space Colonization algorithm.
This paper tackles the problem of identification and tracking of multiple robots in an intelligent space using an array of cameras placed in fixed positions within the environment.
In this article we propose a novel approach for utilizing automated advising agents in assisting an operator to better manage a team of multiple robots in complex environments.
The presence of obstacles and the probability of a collision between multiple robots in a workspace are the motivations behind employment of the artificial potential field (APF) approach.
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Das [19] proposed a new methodology to determine the optimal trajectory of the path for multiple robot in a clutter environment using hybridization of improved particle swarm optimization (IPSO) with an improved gravitational search algorithm (IGSA).
The architecture is based on two software components: Saphira, developed by K. Konolige at SRI, and MPSLab, a package for motion planning and simulation of multiples robots in virtual environlnents, developed by one of the authors of this work.
To accelerate the decontamination without decreasing the efficiency, multiple robots working in coordination are employed in the proposed system.
Most of the works of collaborative path-planning for multiple robots located in unknown environments focused on the problem of exploration of the environment.
The use of multiple robots working cooperatively in a redundant way offers new possibilities in the execution of complex tasks in dynamic workspaces.
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