An Improved Spherical Vector and Truncated Mean Stabilization Based Bat Algorithm for UAV Path Planning

• Published in: IEEE access Vol. 11; pp. 2396 - 2409
• Main Authors: Chen, Buqian, Yang, Jin, Zhang, Huizhen, Yang, Meng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Autonomous aerial vehicles; B spline functions; bat algorithm; configuration space; Costs; Feasibility; Flight paths; Flight planning; Fuels; Optimization; Optimization algorithms; Particle swarm optimization; Path planning; Spherical coordinates; truncated mean stabilization strategy; Unmanned aerial vehicle (UAV); Unmanned aerial vehicles
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3234057

Unmanned aerial vehicles have a wide range of applications. An intelligent optimization algorithm based on the traditional bat algorithm (BA) is investigated in this paper for UAV flight path planning in a static complex environment. The primary goal of this work is to develop a safer flight path while considering the feasibility of the UAV and the requirements for safe operation. This research proposes an improved spherical coordinate and truncated average stable strategy-based bat optimization algorithm (TMS-SBA). The algorithm uses the UAV's motion space to encode the operator, and by substituting a new bat for the worst of the old one after each iteration to increase population diversity, the algorithm can converge quickly in a complex environment while maintaining stable operation. In addition, the flight path is smoothly generated by using B-spline curves to make the planned path suitable for UAV. MATLAB simulation experiments show that, compared with other traditional swarm intelligent algorithms, TMS-SBA can successfully generate feasible and effective optimal solutions in complex environments and plan shorter, safer, and more accessible flight paths for UAV.