Analysis of advanced modified tuna swarm optimization technique for path planning of underwater vehicle

Purpose Path planning with obstacle avoidance is crucial for navigating an autonomous underwater vehicle (AUV) in an unknown and obstacle-rich three-dimensional space. This paper aims to develop an optimized obstacle-free path in a three-dimensional environment using an Advanced Modified Tuna Swarm Optimization (AMTSO) technique. Design/methodology/approach Three-dimensional simulations of underwater environments with different obstacles are created using MATLAB to perform tests such as obstacle avoidance, wall following, avoiding dead ends and path planning in a cluttered environment. There are four techniques in AMTSO for search; each is selected based on the requirement and is independent of the other. Different tests were conducted for different scenarios in underwater navigation to evaluate the controller’s ability to address unforeseen conditions. Findings The path planning ability of the controller, which includes short, obstacle-free and smooth path generation, tested under simulated conditions, is compared with conventional techniques, where the proposed technique shows better navigational parameters. The proposed technique is also tested to determine the robustness and effectiveness of the controller, and the navigational data are compared with a recently developed technique used in AUV navigation based on improved ant colony optimization. In this test, the proposed controller has a shorter path than recently developed techniques. Originality/value In this research, the TSO is extensively modified; an extra handling feature is introduced, and changes are made in the spiral foraging phase to fulfil the objective of the path planning as the traditional TSO is susceptible to falling into local minima and premature convergence.