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Nakisa proposes a method based on the multi-swarm particle swarm optimization (PSO) with local search on the multiple robot search system to find a given target in a complex environment that contains static obstacles [16].
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Darvishzadeh and Bhanu [14] present a framework to use a modified PSO MPSOO) algorithm in a multiple robot system for search task in real-world environments.
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).
Formulation of the problem for multiple robot path planning has been elaborated in "Problem formulation for multiple robot navigation" section.
The concept of multiple robot systems (MRS) began in the 1990s, in particular in works regrouping mobile robots, gathering objects [1] and robot colonies [2, 3].
Multiple robot models are used to approach multiple strategies in human standing balance.
The problem of multiple robot motion planning focuses on computation of paths of different robots such that each robot has an optimal path, and so the overall path of all the robots combined is optimal.
Luan Silveira, Renan Q. Maffei, Silvia S.C. Botelho, Paulo L. Drews and Alessandro de L. Bicho introduce a method for planning paths for multiple robots in unknown environments, which is based on an incremental graph search and on the Space Colonization algorithm.
For example, the Robocop urban search and rescue (USAR) aims to promote research in collaborative problem solving by multiple robots in a city scale arenas (Visser et al. 2015).
The robot searched the two-story yellow house room by room, but detected no one.
We transplanted distributed control form multiple robots to one robot in CLM control mode.
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