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原文摘要

Autonomous navigation of mobile robots in crowded environments, while preserving pedestrians' psychological comfort, requires distinguishing pedestrians from other obstacles, predicting their future motion, and accounting for uncertainty in the predicted trajectories of both pedestrians and the robot. This work extends a previous MPC framework by integrating obstacle and pedestrian detection, Koopman-based motion prediction, and online prediction-error estimation directly within the collision-avoidance mechanism. To reduce collision-avoidance complexity in crowded scenarios, nearby obstacles are aggregated, thereby reducing the number of collision-avoidance constraints. Simulation studies evaluate the prediction-error margins and the computational benefits of semantics-aware aggregation. In a paired 300-seed closed-loop comparison with a moving generic obstacle, Markov residual tightening reduces the fraction of runs with physical contact from $10.0\%$ to $1.33\%$, at a mean travel-time cost of $0.68$ s. In a separate crowd-navigation semantic ablation, the offline robot-footprint/personal-space overlap occupies $2.11\%$ of observed time with pedestrian-aware handling versus $22.84\%$ with generic-obstacle handling.