Gradient-Based Online Regular Virtual Tube Generation for UAV Swarms in Dynamic Fire Scenarios
Kai Rao, Huaicheng Yan, Runfeng Zhang, Zhihao Huang, Penghui Yang
Abstract
This article proposes a novel method for generating the regular virtual tube for safe task execution by UAV swarms in fire dynamic propagation scenarios with static obstacles. Cellular automata is utilized to simulate the dynamic propagation characteristics of fire. We employ the empirical Wang Zhengfei model, taking into account environmental vegetation characteristics, initial wind speed, and wind correction coefficient, to define the state transition function for the cellular automaton. The generation framework for regular virtual tube is divided into two parts: front-end search and back-end optimization. In the front-end, a collision-free trajectory is computed using a search-based approach. In the back-end, a reference curve generation method with soft constraints is employed, and it is solved using gradient information. Subsequently, an optimization method for the radius of the regular virtual tube is provided based on the reference curve. Simulation results demonstrate that the real-time generated regular virtual tube can adapt to dynamic fire scenarios and are more robust in complex obstacle scenarios compared to existing state-of-the-art approaches.