Air–Ground Collaborative Multi-Target Detection Task Assignment and Path Planning Optimization

Collaborative exploration in environments involving multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) represents a crucial research direction in multi-agent systems. However, there is still a lack of research in the areas of multi-target detection task assignment and swarm path planning, both of which play a vital role in enhancing the efficiency of environment exploration and reducing energy consumption. In this paper, we propose an air–ground collaborative multi-target detection task model based on Mixed Integer Linear Programming (MILP). In order to make the model more suitable for real situations, kinematic constraints of the UAVs and UGVs, dynamic collision avoidance constraints, task allocation constraints, and obstacle avoidance constraints are added to the model. We also establish an objective function that comprehensively considers time consumption, energy consumption, and trajectory smoothness to improve the authenticity of the model and achieve a more realistic purpose. Meanwhile, a Branch-and-Bound method combined with the Improved Genetic Algorithm (IGA-B&B) is proposed to solve the objective function, and the optimal task assignment and optimal path of air–ground collaborative multi-target detection can be obtained. A simulation environment with multi-agents, multi-obstacles, and multi-task points is established. The simulation results show that the proposed IGA-B&B algorithm can reduce the computation time cost by 30% compared to the traditional Branch-and-Bound (B&B) method. In addition, an experiment is carried out in an outdoor environment, which further validates the effectiveness and feasibility of the proposed method.