Fast-Spanning Ant Colony Optimisation (FaSACO) for Mobile Robot Coverage Path Planning

TL;DR

This paper introduces FaSACO, a novel ant colony optimization variant that speeds up coverage path planning for mobile robots by allowing ants to explore at different velocities, resulting in faster and more efficient coverage.

Contribution

FaSACO enhances traditional ACO by enabling ants to explore with varying velocities, improving efficiency and reducing coverage time in robot path planning.

Findings

FaSACO is 19.3-32.3% faster than ACO in CPU time.

FaSACO covers 6.9-12.5% fewer cells than ACO.

FaSACO is suitable for real-time, energy-limited applications.

Abstract

Coverage Path Planning (CPP) aims at finding an optimal path that covers the whole given space. Due to the NP-hard nature, CPP remains a challenging problem. Bio-inspired algorithms such as Ant Colony Optimisation (ACO) have been exploited to solve the problem because they can utilise heuristic information to mitigate the path planning complexity. This paper proposes the Fast-Spanning Ant Colony Optimisation (FaSACO), where ants can explore the environment with various velocities. By doing so, ants with higher velocities can find destinations or obstacles faster and keep lower velocity ants informed by communicating such information via pheromone trails on the path. This mechanism ensures that the (sub-)~optimal path is found while reducing the overall path planning time. Experimental results show that FaSACO is $19.3-32.3\%$ more efficient than ACO in terms of CPU time, and re-covers $6.9-12.5\%$ less cells than ACO. This makes FaSACO appealing in real-time and energy-limited applications.