Cooperative mapping task assignment of heterogeneous multi-UAV using an improved genetic algorithm

Abstract

A ground station uses heterogeneous unmanned aerial vehicles (UAVs) with varying observation resources (primarily airborne cameras) for oblique photography based on mapping task requirements to obtain access to geographic information. In this paper, a cooperative multi-task allocation model is proposed for heterogeneous UAVs based on area segmentation to approximate real-world mapping applications. Specifically, it considers area mapping task requirements, UAV kinematics constraints, resource constraints (maximum endurance and sensor capabilities), and task number ceiling constraints; and optimises the task sequence based on the objective function that considers UAVs coverage path planning characteristics. However, a general genetic algorithm (GA) could undermine the feasibility of the solution in the population, resulting in an undesirable optimisation effect when performing genetic operations on a large-scale task allocation problem with strong coupling constraints and an obscure encoding relation. An improved double-chromosome encoding GA with a conflict-mediation mechanism is proposed to perform genetic operations on the population while meeting these constraints. The proposed GA ensures the universal excellence of population evolution while significantly improving the population optimisation and convergence performance. The simulation results show that the proposed GA has greater global search ability, stability, and speed compared with existing algorithms.