PheroSLAM: A Collaborative and Bioinspired Multi-agent System Based on Monocular Vision

Abstract

Multi-robot applications have been extensively discussed and, recently, they are essential for solving problems in robotics field. Nevertheless, development multi-robot real-time applications is usually a complex task, in which it is necessary to design robust environments to support implementation scenarios. In order to deal with such scenarios, this paper proposes PheroSLAM, a bio-inspired multi-robot system based on monocular camera which adopt an extended version of Ant Colony Optimization approach to coordinate multiple-robot teams in the problem related to localization and mapping simultaneously (SLAM). Moreover, robots launch repulsive articial pheromone around themself, creating a repulsive trail in PheroSLAM system. This pheromone trail must be avoided by the other robots, since it denotes an area that have been recently explored. A vision-based SLAM mechanism is also used to provide visual odometry information and to build a 3D feature-based map, considering that every robot must be able to localize itself in the explored environment. Usually, the SLAM problem is solved by cameras or robots remotely controlled. Therefore, the relevance of the proposal is to extend an SLAM problem for many robots and promote the robots move autonomously in the environment according a bio-inspired coordination strategy. Experimental evidences indicated the dispersibility of the PheroSLAM system, increasing the covered area of an environment. Also, results showed that the coordination strategy is efficient and satisfactory to accomplish the exploration task.