Path Planning Optimization With Multiple Pesticide and Power Loading Bases Using Several Unmanned Aerial Systems on Segmented Agricultural Fields

We propose a hybrid algorithm based on the nested genetic algorithm (GA) and integer particle swarm optimization (PSO) for multiple unmanned aerial systems (multi-UASs) plant protection operations in multiple segmented fields, with multibases loading pesticides and power. In the proposed algorithm, a preprocessing phase is adopted that includes segmented fields splitting and merging, to shrink the operation field number (i.e., multi-UASs total sorties) and ensure that each generated field area remains smaller than (but close to) the unmanned aerial system (UAS) maximum single-sortie operation area. An improved one-sortie coverage path generation model is established, avoiding power waste in the case when UAS needs to operate on more than one subfield in one sortie. The integer PSO is then used to optimize the initial assignment for UASs on multibases, with the purpose of reducing total operation time and nonoperation flight distance of overall coverage path planning. The proposed algorithm solves the problems of how to design pesticide spraying coverage path on segmented agricultural fields with several bases, and allocate to multiple plant protection UASs with constrained loading capacity. Computer simulations are provided to validate the effectiveness of our algorithms and the performance compared with other algorithms. The test results show that the total number of flight sorties using nested GA (53) is smaller than that using some representative metaheuristic algorithms (57), density-based spatial clustering of applications with noise (60), and traditional planning methods (83). Both nonoperation distance and total operation time obtained by nest GA and integer PSO are smaller than those using other planning methods. The appearance of multibases loading pesticide and power, would reduce the nonspraying flight energy and operation time wastage of UASs.