Multi-goal motion planning of an autonomous robot in unknown environments by an ant colony optimization approach

Abstract

An ant colony optimization (ACO) approach is proposed in this paper for real-time concurrent map building and navigation for multiple goals purpose. In real world applications such as rescue robots, and service robots, an autonomous mobile robot needs to reach multiple goals with the shortest path that, in this paper, is capable of being implemented by an ACO method with minimized overall distance. Once a global path is planned, a foraging-enabled trail is created to guide the robot to the multiple goals. A histogram-based local navigation algorithm is employed locally for obstacle avoidance along the trail planned by the global path planner. A re-planning-based algorithm aims to generate path while a mobile robot explores through a terrain with map building in unknown environments. In this paper, simulation results demonstrate that the real-time concurrent mapping and multi-goal navigation of an autonomous robot is successfully performed under unknown environments.