Real-Time Scheduling of Mixed-Criticality Requests and End-to-End Latency Assurance in a Robot Fleet Management System

The large number of various requests for fleet management systems needs to be handled promptly, such as right-of-way grant applications, collision avoidance rerouting and task assignments. Among them, right-of-way grants and collision-avoidance requests will cause a protective stop of the robot if they exceed the deadline; task assignment requests can be delayed. The current system handles all requests through a first-come, first-served thread pool and fails to control tail latency for high-priority requests during peak load. We model the fleet management system as a mixed-criticality real-time system and propose a two-level scheduling framework (MC-TLS). The first level applies earliest-deadline-first scheduling and admission control using empirical upper-tail execution-time quantiles, while the second level employs a constant-bandwidth server to allocate processing budgets to soft-real-time requests and reduces their available bandwidth under overload. An end-to-end response-time assessment incorporates queuing, execution, communication, and scheduling overhead under the evaluated workload. MC-TLS was evaluated in an operational fleet management system serving 800 mobile robots. This 800-robot deployment constitutes the production baseline, whereas the larger fleet configurations were examined separately in the scalability evaluation.The production workload comprises right-of-way granting, collision-avoidance replanning, and task assignment requests, with deadlines of 50 ms, 200 ms, and 2 s, respectively. The reported peak arrival rate in the 800-robot production environment is 4,200 requests/s.The evaluation reported a maximum observed response time of 41 ms in the analyzed latency series and a model-derived reference of 47 ms, compared with a 99.9th-percentile latency of 312 ms for FCFS. The reported deadline-miss rate decreased to 0.003%, while task-assignment starvation was 14% under fixed-priority scheduling and 0% under MC-TLS. These results demonstrate improved deadline compliance under the evaluated workload. However, because admission control relies on an upper-tail quantile of historical execution-time distributions rather than a certified worst-case execution-time (WCET) bound, the findings do not establish deterministic schedulability guarantees.Protective stops due to request timeouts were reduced by 63%, fleet throughput increased by 7.8%, and processor utilisation remained at 78%. During the reported 30-day production observation, the 800-robot system processed 2.6×10⁹ requests, corresponding to an average processing rate of approximately 1,003 requests/s. These production observations are distinguished from the separate scalability evaluation.A separate scalability stress test examined fleet sizes from 800 to 1,400 robots under unchanged server hardware and scheduling parameters, corresponding to a 75% increase in the evaluated fleet size. The 1,400-robot configuration is not included in the reported 30-day, 800-robot production observation.This study integrates mixed-criticality scheduling into fleet management software and provides a workload-conditioned approach to deadline assurance and end-to-end latency assessment.