In global path planning (GPP), an autonomous underwater vehicle (AUV) tracks a predefined path. The main objective of GPP is to generate a collision free sub-optimal path with
minimum path cost. The path is defined as a set of segments, passing through selected nodes
known as waypoints. For smooth planar motion, the path cost is a function of the path length,
the threat cost and the cost of diving. Path length is the total distance travelled from start to end
point, threat cost is the penalty of collision with the obstacle and cost of diving is the energy
expanse for diving deeper in ocean. This paper addresses the GPP problem for multiple AUVs in
formation. Here, Grey Wolf Optimization (GWO) algorithm is used to find the suboptimal path
for multiple AUVs in formation. The results obtained are compared to the results of applying
Genetic Algorithm (GA) to the same problem. GA concept is simple to understand, easy to
implement and supports multi-objective optimization. It is robust to local minima and have wide
applications in various fields of science, engineering and commerce. Hence, GA is used for this
comparative study. The performance analysis is based on computational time, length of the path
generated and the total path cost. The resultant path obtained using GWO is found to be better
than GA in terms of path cost and processing time. Thus, GWO is used as the GPP algorithm
for three AUVs in formation. The formation follows leader-follower topography. A sliding mode
controller (SMC) is developed to minimize the tracking error based on local information while
maintaining formation, as mild communication exists. The stability of the sliding surface is verified by Lyapunov stability analysis. With proper path planning, the path cost can be minimized
as AUVs can reach their target in less time with less energy expanses. Thus, lower path cost
leads to less expensive underwater missions.
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