Robot person following (RPF) is a core capability in human-robot interaction, enabling robots to assist users in daily activities, collaborative work, and other service scenarios. However, achieving practical RPF remains challenging due to frequent occlusions, particularly in dynamic and crowded environments. Existing approaches often rely on fixed-point following or sparse candidate-point selection with oversimplified heuristics, which cannot adequately handle complex occlusions caused by moving obstacles such as pedestrians. To address these limitations, we propose an adaptive trajectory sampling method that generates dense candidate points within socially aware zones and evaluates them using a multi-objective cost function. Based on the optimal point, a person-following trajectory is estimated relative to the predicted motion of the target. We further design a prediction-aware model predictive path integral (MPPI) controller that simultaneously tracks this trajectory and proactively avoids collisions using predicted pedestrian motions. Extensive experiments show that our method outperforms state-of-the-art baselines in smoothness, safety, robustness, and human comfort, with its effectiveness further demonstrated on a mobile robot in real-world scenarios.

翻译：机器人跟随（RPF）是人机交互的核心能力，使机器人能够在日常活动、协作工作及其他服务场景中协助用户。然而，由于频繁的遮挡问题，特别是在动态拥挤环境中，实现实用的RPF仍然具有挑战性。现有方法通常依赖于固定点跟随或采用过于简化的启发式方法进行稀疏候选点选择，这些方法无法充分处理由行人等移动障碍物引起的复杂遮挡。为应对这些局限，我们提出了一种自适应轨迹采样方法，该方法在社会感知区域内生成密集的候选点，并使用多目标代价函数对其进行评估。基于最优候选点，我们根据目标的预测运动估计出跟随轨迹。我们进一步设计了一种预测感知的模型预测路径积分（MPPI）控制器，该控制器在跟踪该轨迹的同时，利用预测的行人运动主动避碰。大量实验表明，我们的方法在平滑性、安全性、鲁棒性和人机舒适度方面均优于现有先进基线方法，其有效性在真实场景的移动机器人上得到了进一步验证。