Towards Safe Navigation Through Crowded Dynamic Environments

Abstract

This paper proposes a novel neural network-based control policy to enable a mobile robot to navigate safety through environments filled with both static obstacles, such as tables and chairs, and dense crowds of pedestrians. The network architecture uses early fusion to combine a short history of lidar data with kinematic data about nearby pedestrians. This kinematic data is key to enable safe robot navigation in these uncontrolled, human-filled environments. The network is trained in a supervised setting, using expert demonstrations to learn safe navigation behaviors. A series of experiments in detailed simulated environments demonstrate the efficacy of this policy, which is able to achieve a higher success rate than either standard model-based planners or state-of-the-art neural network control policies that use only raw sensor data.