发表机构
VJTI（维杰拉吉·鲁勒学院（VJTI））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对普通MPPI规划低估动态场景风险的问题，提出PGIF方法，结合高斯排斥场实现0%碰撞率，同时保持实时规划性能，适用于社交导航。
AI 中文摘要
在拥挤环境中实现机器人安全导航，需要规划时考虑行人未来位置，而非仅当前位置。模型预测路径积分（MPPI）控制是一种有效的采样式规划器，但许多实现将行人编码为当前位置的静态点障碍物，低估了动态场景中的风险。我们提出预测高斯交互场（PGIF），这是一种时空代价公式，可在整个规划时域内传播行人预测，并将其编码为与每个行人运动方向对齐的各向同性高斯排斥场。每个场的前向扩散随行人速度增大，形成运动锥危险区，对机器人轨迹进入行人行进路径的惩罚强于从后方接近的情况。该公式为闭式形式，可在所有rollout间完全并行化，无显著计算开销。在三个密度等级的300个随机人群场景中评估，PGIF-MPPI在各密度等级均达到0%的碰撞率，而普通MPPI的碰撞率最高达82%，同时保持实时规划性能。
英文摘要
Safe robot navigation in crowded spaces requires planning that accounts for where people will be, not only where they are now. Model Predictive Path Integral (MPPI) control is an effective sampling-based planner, but many implementations encode humans as static point obstacles at their current positions, underestimating risk in dynamic scenes. We propose Predictive Gaussian Interaction Fields (PGIF), a spatiotemporal cost formulation that propagates pedestrian predictions forward over the full planning horizon and encodes them as anisotropic Gaussian repulsive fields aligned with each pedestrian's direction of motion. The forward spread of each field grows with the pedestrian's speed, creating a motion cone danger zone that penalises robot trajectories entering the pedestrian's path of travel more strongly than those approaching from behind. The formulation is closed-form and fully parallelisable across rollouts, adding no measurable computational overhead. Evaluated over 300 randomised crowd scenarios at three density levels, PGIF-MPPI achieves a 0% collision rate at every density level, compared with up to 82% for vanilla MPPI, while maintaining real-time planning performance.
CommentsWill appear in Springer's Proceedings in Advanced Robotics series
