A novel joint path planning method for drones with hopfield neural network and Gaussian sampling

How to ensure the safe flight of unmanned aerial vehicles (UAVs) in complex environments with static and dynamic obstacles can be a big challenge. Therefore, a new joint path planning method with integrated of an improved non-dominated sorting genetic algorithm (NSGA-III) and an improved artificial potential field (APF) is proposed, which exhibits high computation efficiency and excellent global optimum searching capability. Three objectives including flying efficiency, stability and obstacles avoidance are constructed to meet the strict requirements of the complex environment. Greatest novel features of this new method include three aspects, and they are:1) Hopfield neural network is introduced to replace the random strategy to generate the initial population of NSGA-III, which can enhance the iteration efficiency significantly, 2) Gaussian sampling is designed to create a new potential solution space that is adjacent to the local optimum, which can guide the further searching for a better result and 3) virtual path points as well as tracked distances are both developed in APF to help escape from the stuck area during the dynamic obstacle avoidance. A comprehensive compared study is also carried out by use of popular traditional NSGA-III, improved A∗ and RRT∗ algorithms. The joint planning algorithm can ameliorate path length and flying efficiency by an average of 12.6% and 67.5%, respectively. Simulations and experiments approve the validation.