Dynamically Feasible Trajectory Generation With Optimization-Embedded Networks for Autonomous Flight

This paper aims to bridge perception and planning in navigation systems by learning optimal trajectories from depth information in an end-to-end fashion. However, using neural networks as closed-boxreplacements for traditional modules risks scalability and adaptability. Moreover, such methods often fall short in sufficiently incorporating the robot's dynamic constraints, resulting in trajectories that are either inadequately executable or unexpectedly aggressive, diverging from user expectations. In this paper, we fuse the benefits of conventional methods and neural networks by introducing an optimization-embedded network based on a compact trajectory library. The network distills spatial constraints, which are then applied to model-based spatial-temporal trajectory optimization problem, yielding feasible and optimal solutions. By making the optimization problem differentiable, our model seamlessly approximates the optimal trajectory. Additionally, the introduced regularized trajectory library permits efficient capture of the spatial distribution of optimal trajectories with minimal storage cost, safeguarding multimodal planning features. Benchmarking demonstrates the outstanding performance of our method in trajectory smoothness, success rate, and constraint satisfaction. Real-world flight experiments with an onboard computer showcase the autonomous quadrotor's ability to navigate swiftly through dense forests.