Adap-RPF: Adaptive Trajectory Sampling for Robot Person Following in Dynamic Crowded Environments

2025-10-13

AI Summary

To address trajectory discontinuity and safety degradation in Robot-Person Following (RPF) within dynamic, dense pedestrian environments—primarily caused by occlusions—we propose a socially aware adaptive trajectory planning method. Our approach integrates pedestrian motion prediction with the Social Force Model to generate dense candidate trajectories confined to socially compliant regions. A multi-objective cost function—balancing trajectory smoothness, safety, human comfort, and occlusion avoidance—is optimized online via a prediction-aware Model Predictive Path Integral (MPPI) controller. Key innovations include: (i) a prediction-driven adaptive sampling mechanism that dynamically adjusts trajectory candidates based on predicted pedestrian motions; and (ii) a tightly coupled control architecture unifying motion prediction and MPPI-based optimization. Extensive evaluations in simulation and on real mobile robots demonstrate significant improvements over state-of-the-art methods across trajectory continuity, obstacle avoidance robustness, human comfort, and safety.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Addresses robot person following challenges in crowded dynamic environments
• Overcomes occlusion issues caused by moving obstacles like pedestrians
• Improves trajectory planning with adaptive sampling and multi-objective evaluation

Innovation

• Adaptive trajectory sampling with dense candidate points
• Multi-objective cost function for optimal point selection
• Prediction-aware MPPI controller for trajectory tracking