延迟远程操纵中基于时空上下文的个性化运动补偿

中文摘要 AI 辅助

通信延迟是远程机器人技术中的核心挑战，会破坏视觉运动协调并降低任务精度。运动缩放是应对延迟导致的超调的有效对策，但典型应用依赖于统一增益，忽略了个体和上下文变异性。我们提出一种以人为本的方法，为每个参与者拟合个性化的、与延迟、方向和距离相关的缩放参数。我们开展了实验，20名参与者在虚拟模拟器中执行带延迟的到达任务。在每种实验条件组合下，计算缩放增益以最小化模拟中的平均超调。在模拟环境和远程手术机器人上进行评估，以评估辅助的益处。通过超调、端点误差、轨迹平滑度、运动经济性以及复合误差-时间指标，在多种延迟、距离和运动方向下评估性能。与无辅助试验相比，运动缩放持续提升性能，在关键指标上实现了高达20-25%的性能增益。在较长延迟下效果最为显著。个性化在中等延迟下的短距离内向到达任务中展现出额外的精度益处。结果表明，个性化缩放具有作为更具适应性框架基础的潜力，该框架整合上下文信息以提升远程操作过程的安全性和精度。

英文摘要

Communication delay remains a central challenge in telerobotics, where it disrupts visuomotor coordination and reduces task precision. Motion scaling is an effective countermeasure to delay-induced overshoot, yet typical deployments rely on uniform gains that neglect individual and contextual variability. We propose a human-centered method that fits personalized delay-, direction-, and distance-specific scaling parameters for each participant. We conducted experiments with twenty participants who performed delayed reaching tasks in a virtual simulator. Scaling gains were computed to minimize mean overshoot in simulation in each combination of experimental conditions. Evaluation was done in simulation and on a telesurgical robot to evaluate assistance benefits. Performance was assessed across multiple delays, distances, and movement directions using overshoot, endpoint error, trajectory smoothness, economy of motion, and a composite error-time metric. Motion scaling consistently improved performance relative to unassisted trials, yielding up to 20-25% performance gains in key metrics. Effects were most pronounced at longer delays. Personalization demonstrated additional accuracy benefits for inward reaching at a short distance under moderate delay. The results highlight the potential of personalized scaling as a foundation for more adaptive frameworks that integrate contextual information to improve the safety and precision of teleoperated procedures.

发表机构

• Case Western Reserve University（凯斯西储大学）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

备注

10 pages, 6 figures