Pathnet: Learning To Generate Trajectories Avoiding Obstacles
Alassane Watt, Yusuke Yoshiyasu · 2020
This paper presents a novel approach to solving 2D motion planning problems using deep neural networks, which we refer to as PathNet. PathNet first takes a 2D environment map composed of obstacle zone and free zone and compresses it to a latent vector. The latent vector is afterward concatenated with the start and goal positions to generate a trajectory connecting those positions. Our learning-based neural planner can solve motion planning problems in unseen environments and is computationally efficient as it only needs one single pass in our network to generate trajectories.