Source-seeking multi-robot team simulator as container of nature-inspired metaheuristic algorithms and Astar algorithm.

One driving application for multi-robots is source seeking, especially in the hazardous environment. It consists of two essential subtasks: source location and path search. Nature inspired meta-heuristic is preferable in addressing the source location subtask which is an inversion problem, while the Astar algorithm and its variants are widely used for the path search subtask. In this paper, we present a multi-robot team simulator as a container which contains both algorithms as components. The simulator takes the constraints into consideration, including the size and the speed bound of each robot, the obstacle and collision avoidance. We provide a python implementation and example problems for research and test purposes. The well-structured code with object-oriented design can be conveniently upgraded by adding new excellent nature inspired metaheuristics, or extended to other source seeking problems in various field applications. The python code can be downloaded from the website: https://github.com/buctlab/source-seeking-multi-robot-team-simulator. • We developed a source-seeking multi-robot simulator containing many metaheuristics. • We implemented the multimodal version of metaheuristics in the simulator. • The simulator closely combines the metaheuristics with the Astar algorithm. [ABSTRACT FROM AUTHOR]