Coverage path planning (CPP) is the task of designing a trajectory that
enables a mobile agent to travel over every point of an area of interest. We
propose a new method to control an unmanned aerial vehicle (UAV) carrying a
camera on a CPP mission with random start positions and multiple options for
landing positions in an environment containing no-fly zones. While numerous
approaches have been proposed to solve similar CPP problems, we leverage
end-to-end reinforcement learning (RL) to learn a control policy that
generalizes over varying power constraints for the UAV. Despite recent
improvements in battery technology, the maximum flying range of small UAVs is
still a severe constraint, which is exacerbated by variations in the UAV's
power consumption that are hard to predict. By using map-like input channels to
feed spatial information through convolutional network layers to the agent, we
are able to train a double deep Q-network (DDQN) to make control decisions for
the UAV, balancing limited power budget and coverage goal. The proposed method
can be applied to a wide variety of environments and harmonizes complex goal
structures with system constraints.

本文提出了一种新方法，利用端到端强化学习通过地图通道提供空间信息并使用 DDQN 平衡有限电力预算和无人机的航程，以控制在一些禁飞区域有随机起飞位置和多个降落选项的环境中的无人机 CNN 航迹规划任务，此方法可以应用于各种环境且很好地协调了复杂的系统约束与目标结构。