Multi-goal Motion Planning of an Autonomous Robot in Unknown Environments by an Ant Colony Optimization Approach

Luo, Chaomin

An ant colony optimization (ACO) approach is

proposed in this paper for real-time map building and

navigation for multiple goals purpose. In real world

applications such as rescue robots, service robots, mine rescue

robots, and mine searching robots, etc., an autonomous mobile

robot needs to reach multiple goals with a 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 to plan a collision-free path along the trail planned by

the global path planner. A replanning-based algorithm aims to

generate path while a mobile robot explores through a terrain

with map building in unknown environments. In this paper,

simulation and experimental results demonstrate that the

real-time concurrent mapping and multi-goal navigation of an

autonomous robot is successfully performed under unknown

environments.