Task-Sequencing Optimization Using DSSA Algorithm for AUV with Limited Endurance

Abstract

Efficiency is very important for robots when executing multiple tasks, especially for autonomous underwater vehicles (AUVs) with limited endurance. In order to improve efficiency, task-sequencing optimization, which focuses on achieving the optimal execution order of multiple tasks, is one effective way. In this paper, the classic lawnmower traverse task is taken as an example for optimization, and discrete salp swarm algorithm (DSSA), which combines salp swarm algorithm (SSA) and genetic algorithm (GA), is selected and applied to optimize task sequencing for one AUV. In the optimization process, there are two key problems that need to be solved. One is how to encode the individual chromosome and the other is how to perform genetic operations on chromosomes of different lengths. For the former problem, a two-layer encoding method is proposed to generate individuals in the population, in which identity number and entry point of different areas are encoded, respectively. For the latter problem, mutation and crossover operators are modified and extended to deal with individuals of variable length due to the limited endurance of AUVs. Simulation results show that the sequencing optimization can be achieved after finite iteration. Through comprehensive comparison, DSSA is finally implemented and verified in a physical AUV.