Autonomous Path Planning for Unmanned Aerial Vehicle(UAV)Based on Improved Heuristic Ant Colony Algorithm

Abstract

Autonomous path planning of UAV is a key technical problem for future UAV operation. In view of the shortcomings of traditional route planning methods,such as low efficiency,poor real-time performance,easy to fall into local optimum,an improved heuristic ant colony algorithm for UAV route planning is proposed. In the early stage of the algorithm,Dijkstra algorithm is used to initialize the track,and heuristic information is introduced to improve the search efficiency. Logistic chaotic map is used to initialize pheromone,so that the diversity of solutions can be increased and the convergence speed of the algorithm can be improved. In the middle and late stage of the algorithm,multi-track selection strategy and simulated annealing mechanism are used to improve the global search ability of the algorithm,which avoid falling into local optimum due to too fast convergence speed solution. The simulation results show that,compared with the basic ant colony algorithm,the improved ant colony algorithm can plan a path from the start to the end effectively in the complex environment with threats and obstacles. It also has higher optimization accuracy and faster convergence speed,which is of applicable value.

Keywords

• uav
• route planning
• dijkstra algorithm
• logistic chaos
• ant colony algorithm
• simulated annealing algorithm