Supercomputing for High-speed Avoidance and Reactive Planning in Robots

2025-09-23

AI Summary

To address the challenge of real-time obstacle avoidance under onboard computational constraints in human-robot coexistence scenarios, this paper proposes SHARP, a hybrid control framework. It offloads computationally intensive trajectory planning to local or remote high-performance computing (HPC) clusters—employing an MPI-parallelized multi-objective A* algorithm—while retaining low-level reflex control onboard for closed-loop responsiveness. Experimental validation on a 7-DOF robotic manipulator demonstrates feasibility under network latencies of tens of milliseconds: average planning latencies are 22.9 ms (local) and 30.0 ms (remote), with obstacle-avoidance success rates of 84% and 88%, respectively—both significantly faster than the human average reaction time (~250 ms). This work constitutes the first empirical demonstration that HPC-assisted planning can enable millisecond-scale reactive robot motion, establishing a novel paradigm for highly responsive, scalable hybrid intelligent control.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: The sharing economy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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 parallelized 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.

Problem

• Enabling millisecond-scale robotic responsiveness through high-performance computing
• Overcoming onboard processor limitations for real-time trajectory planning
• Assessing feasibility of HPC offloading for dynamic obstacle avoidance

Innovation

• Offloading trajectory planning to HPC clusters
• Using parallelized multi-goal A* search with MPI
• Achieving millisecond-scale latencies for real-time avoidance