为机器人提供高速避障与反应规划的超级计算
Supercomputing for High-speed Avoidance and Reactive Planning in Robots
AI总结：
SHARP通过HPC实现机器人毫秒级避障与反应规划，展示高并发计算在实时机器人中的可行性及混合控制架构的应用。
摘要：
本文介绍了SHARP（为高速避障与反应规划的超级计算），一项证明概念的研究，展示了高性能计算（HPC）如何使机器人控制实现毫秒级响应能力。尽管现代机器人在人机共享工作空间中面临越来越高的反应需求，但机载处理器受到大小、功率和成本的限制。将任务卸载到HPC可以为轨迹规划提供大规模并行计算，但其在实时机器人中的可行性仍存疑，因为网络延迟和抖动可能影响性能。我们在压力测试场景中评估了SHARP，其中一个7自由度的机械臂必须躲避高速泡沫弹。使用基于MPI实现的哈希分布式多目标A*搜索算法，在本地和远程HPC集群上运行，系统实现了平均规划延迟为22.9毫秒（本地）和30.0毫秒（远程，约300公里远），避障成功率为84%和88%。这些结果表明，当往返延迟保持在几十毫秒范围内时，HPC侧的计算不再是瓶颈，能够实现低于人类反应时间的避障能力。SHARP的结果促使混合控制架构的发展：低级反射仍保留在机载系统中以确保安全，而突发性的高吞吐量规划任务则卸载到HPC以实现可扩展性。通过报告各阶段的时间和成功率，本研究提供了一个可复现的模板，用于评估HPC驱动机器人实时可行性的能力。总体而言，SHARP将HPC卸载重新定位为在动态环境中实现可靠、反应性机器人的可行路径。
英文摘要：
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.
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