Adaptive mutant particle swarm optimization based precise cargo airdrop of unmanned aerial vehicles

Emerging unmanned system technologies and smart logistics have motivated logistics enterprises to use unmanned aerial vehicles (UAVs) for last-mile cargo delivery through the air so as to benefit from its flexibility and low cost. It is useful to deliver cargoes by airdrops from fixed-wing UAVs. However, wind and parameter errors of the UAV could make the cargo deviate from the desired landing point. To improve the accuracy of airdrop methods, this paper presents the Continuously Computed Release Point under conditions of strong wind (SW-CCRP) airdrop strategy to transport the cargo to a given position by a fixed-wing UAV. In this strategy, a set of differential equations are used to model the cargo motion and wind perturbations are considered. Based on the established motion model and the kinematic relationships between the cargo and the target position, the expected release point can be accurately predicted in real time. In order to satisfy the precision requirement, the precision assignment is studied to determine the permissible parameter error ranges. In view of that the conventional precision assignment methods are difficult to be applied in the airdrop system because of its complexity and strong nonlinearity, an adaptive mutant particle swarm optimization (AMPSO) algorithm is proposed to solve this problem, in which the adaptive inertia weight can balance the global and local search. In addition, the mutation factor of the AMPSO is able to avoid premature convergence and stagnation. Finally, two specific test scenarios are designed to validate the effectiveness of the proposed approaches.

Automated summary

This study proposes an airdrop strategy using fixed-wing UAVs for cargo delivery under strong wind conditions. By modeling cargo motion using differential equations and considering wind perturbations, the expected release point can be accurately predicted in real time. The adaptive mutant particle swarm optimization algorithm is introduced to solve the precision assignment problem and avoid premature convergence and stagnation.