A joint global and local path planning optimization for UAV task scheduling towards crowd air monitoring

Abstract Large-scale crowd management systems are used to monitor and manage crowds in various industries aspects by employing relevant technological innovations.In order to overcome shortcomings of conventional CCTV equipment fixed positioning where the viewing angle and equipment deployment are fixed during the events, we propose to use unmanned ariel vehicle (UAV) carried appropriate optical sensory equipment to perform aerial scene surveillance. However, UAV flight missions have problems such as poor adaptability of single-mode path planning to site conditions and complex cluster scheduling systems. Therefore, we combine the improved particle swarm optimization(PSO) algorithm, artificial potential algorithm, path exploration mode switching strategy and energy-based task scheduling mechanism to propose a joint global and local path planning optimization for UAV task scheduling toward crowd air monitoring (JGLPP-UTS). In this model, the PSO algorithm is improved based on the adaptive inertia weight update and mutation mechanism related to the number of iterations thus the generated global path is smoothed. Then by using the artificial potential algorithm an optimization is performed to address the problem of unreachable target point and local minimum. Eventually, the proposed model switches the path planning mode according to the global and local obstacle environment. Finally, our model comprehensively considers the information of the site to realize the surveillance task scheduling of the UAV. Experiments show that our proposed algorithm can effectively improve the ability of global and local path planning. Compared with the standard PSO path length, the global path is reduced by 8.92%, and the adaptive value is reduced by 82.9%. After the smoothing operation, we also report that the path length can be further reduced. Moreover, the task scheduling strategy can realize the effective use of airborne resources.