This paper presents SHARP (Supercomputing for High-speed Avoidance and Reactive Planning), a proof-of-concept study demonstrating how high-performance computing (HPC) can enable millisecond-scale responsiveness in robotic control. While modern robots face increasing demands for reactivity in human-robot shared workspaces, onboard processors are constrained by size, power, and cost. Offloading to HPC offers massive parallelism for trajectory planning, but its feasibility for real-time robotics remains uncertain due to network latency and jitter. We evaluate SHARP in a stress-test scenario where a 7-DOF manipulator must dodge high-speed foam projectiles. Using a hash-distributed multi-goal A* search implemented with MPI on both local and remote HPC clusters, the system achieves mean planning latencies of 22.9 ms (local) and 30.0 ms (remote, ~300 km away), with avoidance success rates of 84% and 88%, respectively. These results show that when round-trip latency remains within the tens-of-milliseconds regime, HPC-side computation is no longer the bottleneck, enabling avoidance well below human reaction times. The SHARP results motivate hybrid control architectures: low-level reflexes remain onboard for safety, while bursty, high-throughput planning tasks are offloaded to HPC for scalability. By reporting per-stage timing and success rates, this study provides a reproducible template for assessing real-time feasibility of HPC-driven robotics. Collectively, SHARP reframes HPC offloading as a viable pathway toward dependable, reactive robots in dynamic environments.

翻译：本文提出SHARP（面向高速避障与反应式规划的超级计算），一项概念验证研究，展示了高性能计算（HPC）如何实现机器人控制中的毫秒级响应。尽管现代机器人在人机共享工作空间中对反应能力的要求日益提高，但其机载处理器受限于尺寸、功耗和成本。将计算任务卸载至HPC可为轨迹规划提供大规模并行处理能力，但由于网络延迟和抖动，其在实时机器人应用中的可行性仍不确定。我们在一个压力测试场景中评估SHARP，其中一台7自由度机械臂必须躲避高速泡沫弹射体。通过在本地及远程（约300公里外）HPC集群上使用MPI实现的哈希分布式多目标A*搜索算法，该系统实现了平均规划延迟分别为22.9毫秒（本地）和30.0毫秒（远程），避障成功率分别为84%和88%。这些结果表明，当往返延迟保持在数十毫秒范围内时，HPC侧的计算不再是瓶颈，从而实现了远低于人类反应时间的避障能力。SHARP的研究结果推动了混合控制架构的发展：低层级反射控制保留在机载端以确保安全，而突发性、高吞吐量的规划任务则卸载至HPC以实现可扩展性。通过报告各阶段时序数据与成功率，本研究为评估HPC驱动机器人技术的实时可行性提供了一个可复现的模板。总体而言，SHARP将HPC卸载重新定位为实现动态环境中可靠、反应灵敏机器人的可行路径。