You Only Plan Once: A Learning-based One-stage Planner with Guidance Learning
the School of Electrical and Information Egineering Tianjin University
Junjie Lu Xuewei Zhang Hongming ShenLiwen XuBailing Tian
(1) Tianjin University(2) the School of Electrical and Information Egineering
Abstract
Abstract—In this work, we propose a learning-based onestage
planner for trajectory generation of quadrotor in obstaclecluttered environment
without relying on explicit map. We integrate perception and mapping,
front-end path searching,and back-end optimization into a single network.
We frame the motion planning problem as a regression of spatially separated
polynomial trajectories and associated scores. Specifically, our
approach adopts a set of motion primitives to cover the searching
space, and predicts the offsets and scores of primitives for
local optimization in a single forward propagation. A novel
unsupervised learning strategy, termed guidance learning, is
developed to provide numerical gradients as the guidance for
training. We train the network policy with privileged information
about the surroundings while only the noisy depth observations
are available during inference. Finally, a series of experiments are
conducted to demonstrate the effectiveness and time-efficiency
of the proposed method in both simulation and real-world.
For supplementary video see: https://youtu.be/GoqZM3TxDbM.
The code will be released at https://github.com/TJU-AerialRobotics/YOPO.