| Summary: | End-to-end deep neural network (DNN)-based motion planners have shown great potential in high-speed autonomous UAV flight. Yet, most existing methods only employ a single high-capacity DNN, which typically lacks generalization ability and suffers from high sample complexity. We propose a novel event-triggered hierarchical planner (ETHP), which exploits the bi-level optimization nature of the navigation task to achieve both efficient training and improved optimality. Specifically, we learn a depth-image-based end-to-end motion planner in a hierarchical reinforcement learning framework, where the high-level DNN is a reactive collision avoidance rerouter triggered by the clearance distance, and the low-level DNN is a goal-chaser that generates the heading and velocity references in real time. Our training considers the field-of-view constraint and explores the bi-level structural flexibility to promote the spatio–temporal optimality of planning. Moreover, we design simple yet effective rules to collect hindsight experience replay buffers, yielding more high-quality samples and faster convergence. The experiments show that, compared with a single-DNN baseline planner, ETHP significantly improves the success rate and generalizes better to the unseen environment.
|
|---|