← 研踪 PaperTrailarXiv:2611.15781 · v1
Prediction-Error-Tightened MPC for Socially Compliant Crowd Navigation
首次提交 2026-11-12 01:56
版本更新 2026-11-12 01:56
雷达观测 2026-11-13 14:16
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 to , at a mean travel-time cost of s. In a separate crowd-navigation semantic ablation, the offline robot-footprint/personal-space overlap occupies of observed time with pedestrian-aware handling versus with generic-obstacle handling.
作者备注：9 pages, 3 figures, 4 tables. Submitted to the European Control Conference (ECC 2027)
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v1 · 2026-11-12 01:56 · 最新观测版本
Prediction-Error-Tightened MPC for Socially Compliant Crowd Navigation
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 to , at a mean travel-time cost of s. In a separate crowd-navigation semantic ablation, the offline robot-footprint/personal-space overlap occupies of observed time with pedestrian-aware handling versus with generic-obstacle handling.
查看此版本解读 →查看此版本原文 ↗