Socially Adaptive Path Planning Based on Generative Adversarial Network

The natural interaction between robots and pedestrians in the process of autonomous navigation is crucial for the intelligent development of mobile robots, which requires robots to fully consider social rules and guarantee the psychological comfort of pedestrians. Learning-based socially adaptive algorithms have shown promise in specific human-robot interaction environments, yet generalization of robot path planning requires improvement. This work combines the generative adversarial network (GAN) with the Optimal Rapidly-exploring Random Tree (RRT*) navigation algorithm. It proposes a GAN model with strong generalization performance and a GAN-RRT* algorithm for path generation in human-robot interaction environments. Additionally, a socially adaptive path planning framework named GAN-RTIRL is presented, which combines GAN with Rapidly-exploring random Trees Inverse Reinforcement Learning (RTIRL) to enhance the homotopy rate between planned and demonstration paths. The GAN-RRT* path planner can update the GAN model from the demonstration path, enabling the robot to generate more anthropomorphic paths and have stronger generalization in complex environments. Experimental results indicate that the proposed method effectively improves the anthropomorphic degree of robot motion planning and the homotopy rate.