Imitation Learning-Based Drone Motion Planning in Dense Obstacle Scenarios

Abstract

For the drone motion planning problem in dense obstacle scenarios, we introduce a trajectory generation method based on imitation learning that does not require the establish-ment of a local map, which greatly increases the planning speed. This method utilizes only onboard sensors and depth camera perception. We specially made the Imitation Learning Planning-Drones (ILP-Drones) dataset for training. The kinodynamic and smoothness of the generated trajectory are improved with local nonlinear optimization. The uniform B-Spline parameterization is adopted to allocate a reasonable time interval for the generated trajectory. Ultimately, our method is able to plan high quality trajectories with excellent collision avoidance ability within mil-liseconds. This is demonstrated by comparative experiments with various advanced algorithms. At the same time, the flexibility and adaptability of our method are demonstrated by ablation experiments with different number of predicted points and different simulation environments.