Autonomous Path Planning via Sand Cat Swarm Optimization With Multistrategy Mechanism for Unmanned Aerial Vehicles in Dynamic Environment

Unmanned aerial vehicles (UAVs) face critical challenges in path planning in dynamic environment, requiring optimized flight paths that account for constraints such as obstacle avoidance, energy efficiency, and altitude limits. Sand cat swarm optimization (SCSO) algorithm has demonstrated promise in addressing complex optimization challenges. However, SCSO is limited by slow convergence, susceptibility to local optima, and insufficient adaptability. To overcome these shortcomings, an enhanced SCSO with the spiral search, Lévy flight, tent chaotic mapping, and adaptive sparrow alert mechanism, namely TSLS-SCSO is developed to propose an autonomous path planning method for UAVs in Dynamic Environment. In TSLS-SCSO, a new population initialization strategy with tent chaotic mapping is designed to achieve a large dynamic range and coverage capability. For the expanding the search range, a new spiral search strategy is designed to broaden the search range in the search phase. For the increasing running efficiency and improving solution, a new Lévy flight strategy is employed to enrich the diversity of population in the attacking prey phase. A new sparrow alert mechanism with integrating the sand cat group with the sparrow alert is designed to obtain faster convergence speed and accuracy. The experiment results on CEC 2017 and CEC 2022 show that the TSLS-SCSO obtains higher accuracy and more stable solutions and exhibits better competitiveness. Furthermore, the proposed UAV path planning method successfully found effective paths with an obstacle avoidance effectiveness of 95.5%. The obtained results validate the effectiveness and competitiveness of TSLS-SCSO in UAV path planning in the dynamic environment.