LPNet: A Reaction-Based Local Planner for Autonomous Collision Avoidance Using Imitation Learning
Junjie Lu, Bailing Tian, Hongming Shen, Xuewei Zhang, Yulin Hui
Abstract
In this work, we propose a reaction-based local planner for autonomous collision avoidance of quadrotor in obstacle-cluttered environment without relying on an explicit map. Our approach searches for feasible trajectory using a set of motion primitives in state lattice and represents the optimal one as a polynomial by solving an optimal control problem. A modified Q-network, termed LPNet, is presented to predict the action-values of motion primitives from the current depth image and the state estimation of the quadrotor directly. To train the proposed LPNet, a primitive-based expert policy with privileged information about the surroundings and unconstrained computational budget is developed to provide demonstrations for imitation learning. Finally, a series of experiments are conducted to demonstrate the effectiveness and time-efficiency of the proposed method in both simulation and real-world.