Safe and Efficient Navigation for Differential-Drive Robots in Dynamic Pedestrian Environments

Abstract

Differential-drive robots are widely used in dynamic pedestrian environments, such as hospitals, for time-sensitive tasks like medication delivery, which require high navigation efficiency to ensure timely arrivals. However, existing methods tend to overemphasize safety, resulting in overly conservative behaviors and prolonged navigation times, which in turn lead to reduced efficiency. To address this issue, this paper proposes a novel navigation framework that integrates a pedestrian risk map, modeled using asymmetric Gaussian distributions, into B-spline trajectory optimization. Rather than strictly avoiding high-risk regions, the method balances collision risk and trajectory length minimization, leading to more effective navigation. Additionally, multiple planning modes enhance adaptability in complex environments, ensuring both safety and efficiency. Furthermore, kinematic constraints specific to differential-drive robots are incorporated to ensure the feasibility of the generated trajectories. Simulations and real-world experiments validate the proposed method’s effectiveness in achieving safe and efficient navigation in dynamic pedestrian environments. The video is available at https://youtu.be/S9qJmXyPEzw.