Obstacle-free robot path planning based on variational autoencoder and generative networks

Robot route planning becomes crucial in intelligent navigation systems given the fast advancement of automation technologies. Concerning efficiency and resilience in handling challenging dynamic settings, traditional path-planning approaches have several restrictions. Based on variational autoencoder (VAE) and generative adversarial network (GAN), this work presents a path planning model, VAE-GAN PathNet, to handle this challenge. By combining the benefits of VAE in latent space modelling and the capacity of GAN in path optimisation, the model essentially increases the quality, smoothness and obstacle avoidance performance of path planning. This work uses the Stanford Drone Dataset and the ROS Path Planning Dataset to validate the efficacy of the model using trials. In terms of path length, obstacle avoidance performance, path smoothness and computing time, VAE-GAN PathNet beats conventional path planning algorithms experimental data demonstrate.