A-star Ant Colony Fusion Optimisation for Unmanned Ground Vehicle Route Planning

• Published in: Journal of physics. Conference series 2024-12, Vol.2891 (11), p.112001
• Main Authors: Hou, Rong, Zhang, Pengfei
• Format: Article
• Language: English
• Subjects: Algorithms; Ant colony optimization; Collision avoidance; Obstacle avoidance; Robotics; Route planning; Smoothness; Unmanned ground vehicles
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/2891/11/112001

Route planning technology of Unmanned Ground Vehicle (UGV) is a key issue in robotics, with the goal of avoiding obstacles and moving quickly to the target point. Aiming at the problem of easy collision with obstacles when utilizing the conventional A-star algorithm for global route planning, an improved turning mechanism is proposed to avoid collision. Aiming at the problems of longer routes and insufficiently smooth routes introduced by it, an improved A-star ant colony fusion algorithm for UGV route planning is proposed by combining the local planning capability of the ant colony algorithm. The A-star algorithm with an improved turning mechanism is fused with an ant colony algorithm to iteratively optimize the route length and route smoothness progressively. Simulation results show that the improved A-star ant colony fusion algorithm is able to avoid obstacles, reduce the length of the planned route by at least 2.34%, and improve the route smoothness by at least 5.62% compared to the A-star algorithm. It is beneficial for UGV to accomplish the corresponding tasks quickly on the basis of ensuring its obstacle avoidance.