Social APF-RL: Safe Mapless Navigation in Unknown & Human-Populated Environments

Safe mapless navigation of mobile robots in unknown and human-populated areas is integral for increasing their usage in our daily lives. In this paper, we consider how such a behavior can be exhibited by a mobile robot and introduce Social APF-RL (Artificial Potential Functions with Reinforcement Learning). Social APF-RL extends our previously presented approach APF-RL in which the strengths of artificial potential functions (APF) with deep reinforcement learning are combined so that the robot learns how to adjust the input parameters of the APF controller. With Social APF-RL, the model is extended to accommodate the presence of humans and to respect their comfort zones while navigating. Our experimental results including both simulation and real-life scenarios demonstrate that differing from the classical navigation methods or social navigation methods, the robot can navigate successfully on its own even in complex scenarios with moving entities while maintaining social distance to humans encountered. Hence, it has better applicability in real-life scenarios. For future work, we plan to use the proposed approach in human following while adhering to social distance norms.