Collision Prediction and Social-Norm-Fusion-Based Social-Navigation Method for Quadruped Robots

作者：Junxian Bei, Qingyun Zhu, Zhuorong Shi, Yonghua Liu · 发表于：Biomimetics · 年份：2026 · DOI：10.3390/biomimetics11040228 · 研究领域：Robotic Locomotion and Control、Social Robot Interaction and HRI、Human Motion and Animation

As a typical biomimetic robotic system, quadruped robots replicate the flexible locomotion of quadruped mammals, outperforming wheeled robots in human-centered daily scenarios. To improve the social navigation adaptability of biomimetic quadruped robots in human-robot shared environments, this paper proposes a collision-aware orthogonal steering social force model (COSFM), an enhanced social force model that integrates collision prediction and social norms, inspired by human-like collision avoidance behaviors and social interaction rules. The model addresses key limitations of conventional social force models: delayed responses to dynamic pedestrians and inadequate consideration of pedestrians' comfort zones. It introduces a time-to-collision prediction mechanism to mimic human predictive decision-making in dynamic social interactions, enhancing the robot's anticipation of pedestrian motion intentions, and designs an orthogonal steering-based avoidance strategy for four typical human-robot interaction scenarios (head-on encounters, intersecting paths, active overtaking, passive yielding). This strategy replicates humans' natural priority of lateral steering over abrupt deceleration or retreat, generating socially compliant trajectories aligned with human behavioral expectations. The proposed method is validated via simulation and real-world experiments on a Unitree Aliengo quadruped robot. Results show that the COSFM algorithm achieves a higher navigation success rate and bet...