Abstract
Abstract This paper describes a new method of path-planning for multiple robots in unknown environments. The method, called Space D*, is based on two algorithms: the D*, which is an incremental graph search algorithm, and the Space Colonization algorithm, previously used to simulate crowd behaviors. The path-planning is achieved through the exchange of information between the robots. So decentralized, each robot performs its path-planning, which provides an obstacle-free path with the least number of robots around. The major contribution of the proposed method is that it generates paths in spacious environments facilitating the control of robots and thus presenting itself in a viable way for using in areas populated with multiple robots. The results obtained validate the approach and show the advantages in comparison with using only the D* method.
Highlights
Planning collision-free motions for autonomous robots located in environments with obstacles is one of the main problems of robotics [9, 15, 16].This is a revised and extended version of a paper that appeared at ENIA 2011 (VIII Encontro Nacional de Inteligência Artificial) and has been recommended to JBCS.One widely studied area of path-planning is planning in environments with multiple independent robots
The Space D* is based on two algorithms that are discussed briefly below: the first is the D*, widely used for path-planning in unknown environments, and the Space Colonization algorithm, which was developed for modeling plant growth [26] and used for the simulation of crowd behaviors [1]
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
Summary
Planning collision-free motions for autonomous robots located in environments with obstacles is one of the main problems of robotics [9, 15, 16]. One widely studied area of path-planning is planning in environments with multiple independent robots For this type of problems, two approaches can be made, centralized and decoupled. Viable alternatives are distributed decoupled techniques, which take in account the usability of distributed processing techniques to implement such approaches In these techniques, each robot plans its path based on local knowledge and in interactions with other robots. The current work proposes an extension of the D* algorithm for multi-robot environments, aiming to provide a collaborative path-planning approach for multi-robot world populated.
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