  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.
