Cooperative trajectory planning of aerial-ground systems is a fundamental and challenging problem, which aims to leverage the aerial information to assist the ground tasks. Existing methods often suffer from suboptimal trajectories or computation burden. In this paper, we address cooperative trajectory planning of aerial-ground systems in which an unmanned ground vehicle (UGV) plans its local trajectory in real-time with the assistance of an unmanned aerial vehicle (UAV). Firstly, the UAV generates guidance trajectory using nonlinear model predictive control (NMPC), which considers the obstacle distribution density as a factor reflecting the coupling effect of multiple obstacles on the UGV, thereby avoiding local minima problem and improving the feasibility of the planned trajectory. Secondly, a null-space-based behavioral control (NSBC) framework is employed to merge the guidance trajectory into the UGV’s own planned one as a task. Finally, an event triggering task supervisor is developed for the UGV to decide the priorities of all tasks, which reduces the switching frequency of task priorities brought by traditional rule-based task supervisors. Both simulation and experiment results show that the proposed approach has superior trajectory planning performance in terms of trajectory error, on-line computation time and the success rate of task execution.

空地系统协同轨迹规划是一个基本且具有挑战性的问题，旨在利用空中信息辅助地面任务。现有方法经常遭受次优轨迹或计算负担的困扰。在本文中，我们解决了空中-地面系统的协作轨迹规划，其中无人驾驶地面车辆（UGV）在无人驾驶飞行器（UAV）的帮助下实时规划其本地轨迹。首先，无人机采用非线性模型预测控制（NMPC）生成制导轨迹，将障碍物分布密度作为反映多个障碍物对UGV耦合影响的因素，从而避免局部极小问题，提高规划轨迹的可行性。其次，采用基于零空间的行为控制（NSBC）框架将引导轨迹合并到UGV自己的计划轨迹中作为一项任务。最后，为UGV开发了事件触发任务管理器来决定所有任务的优先级，减少了传统基于规则的任务管理器带来的任务优先级切换频率。仿真和实验结果表明，该方法在轨迹误差、在线计算时间和任务执行成功率方面具有优越的轨迹规划性能。