Delay-Aware Risk-Adaptive Model Predictive Control for Delivery Robot Navigation in Pedestrian Corridors

Last-mile delivery robots frequently navigate pedestrian-rich local corridors, where sensing, tracking, communication, and computation delays can make pedestrian observations stale and degrade collision-risk estimation. This paper develops a delay-aware risk-adaptive model predictive control (MPC) pipeline that coordinates observation-age compensation and continuous multi-parameter adaptation in a common candidate-based planner. A normalized interaction-risk score jointly schedules the reference speed, pedestrian-avoidance weight, and motion-smoothness weight, enabling efficient motion in low-risk situations and progressively more conservative behavior as predicted risk increases. The method is evaluated through 800 performance simulations, including a 480-run main study over three pedestrian densities, four observation delays, and ten random seeds, a 240-run ablation study, and an 80-run 40 m extended-route evaluation. In the main study, the proposed method achieves a 100% goal-reaching rate, compared with 81.7% for the efficiency-oriented baseline, while reducing average collision count and collision duration by 61.5% and 74.8%, respectively, and increasing minimum robot–human distance by 55.9%. The corresponding collision-free run rate reaches 69.2%. Among successful runs, its mean completion time is 15.32 s, only 6.9% longer than the efficiency-oriented baseline. At a 1.0 s observation delay, the proposed method reduces collision count by 70.2% relative to the efficiency-oriented controller and by 49.9% relative to rule-based switching, while reducing absolute clearance-estimation error to 0.056 m under the tested prescribed pedestrian-motion model. Ablation results confirm that both delay compensation and adaptive weighting contribute to the improvement. On the 40 m extended route, the proposed controller retains a 100% goal-reaching rate and a 70% collision-free run rate. Its mean decision time and average run-wise 95th-percentile decision time are 58.28 ms and 74.21 ms, respectively, both below the 100 ms control period, indicating real-time potential without a measurable average computational penalty.