An adaptive environmental sampling path planning technique for autonomous underwater vehicles based on ant colony optimization algorithm

Path planning, navigation, and localization are still challenging for Autonomous Underwater Vehicles (AUVs). This paper proposes an adaptive environmental sampling technique for autonomous underwater vehicles based on an enhanced ant colony optimization algorithm to achieve fast and efficient path planning for AUVs operating in a known or partially known undersea environment. Three strategies for improving the conventional ant colony algorithm are presented. To begin, the state transition rule is used to control the degree of new paths exploration so that the ant's search activities are concentrated in the spatial vicinity of the optimal solution. Second, the global update rule is only applied to the optimal ant path to raise the pheromone difference between the optimal and worst ant paths. Finally, the local pheromone update rule forces the ant to update the path locally while constructing it and then globally after each cycle. Simulations are used to verify each strategy. Then, it is compared to the A* algorithm and the classic ant colony algorithm. The results show that the adaptive environmental sampling for autonomous underwater vehicles based on our proposed ant colony optimization algorithm is optimal and efficient.