Grid-based GA path planning with improved cost function for an over-actuated hover-capable AUV

For an AUV to perform a long-range mission with its maximum endurance, its energy consumption during transit must be kept to a minimum. This paper presents an improved cost function for a grid-based genetic algorithm (GA) path planning in 2D static environments. The proposed function consists of energy consumption terms that are estimated according to dynamics of a hover-capable AUV - notably Delphin2 AUV. It seeks for a path that requires least effort for the vehicle to move along. A simulation was written in Matlab and the outcomes of the GA with the improved cost function are compared with the ones of a GA with an optimal distance approach as well as an A* approach. It is found that outcomes of an improved cost function require less energy compared with the other techniques.