让机器人通信更安全高效,兼顾任务效果与安全约束。
Safety-aware Goal-oriented Semantic Sensing, Communication, and Control for Robotics
- 设计安全感知的语义通信框架,只传关键信息
- 实测无人机追踪任务安全率提升2倍以上
- 适合关注智能系统安全性的研究者与工程师
无线连接的机器人系统通过远程计算资源实现实时智能决策,但机器人与边缘服务器间的数据交换常导致通信拥塞,引入延迟并降低任务性能。为此,面向目标的语义通信(GSC)被提出,仅传输与任务相关的目标语义表示,提升了任务有效性,但通常忽视实际安全需求。现有机器人研究多将安全视为控制层面问题,未系统考虑感知、通信、控制在闭环中的协同安全。为此,本文研究无线连接机器人系统中安全感知的目标导向语义(SA-GS)感知、通信与控制协同设计,旨在最大化任务有效性的同时满足实际安全要求。首先提出系统架构与典型应用场景;总结各类场景下的通用安全要求与有效性指标;系统分析感知、通信、控制中的独特安全与有效性挑战;据此提出潜在的SA-GS研究方向。最后,基于无人机目标追踪案例验证:其中一种研究方向——基于语义的通信与控制包执行,可使安全率提升超过2倍,追踪成功率提升超过4.5倍。
原文摘要 · Abstract (English)
Wirelessly-connected robotic systems empower robots with real-time intelligence by leveraging remote computing resources for decision-making. However, the data exchange between robots and edge servers often overwhelms communication links, introducing latency that degrades task performance. To tackle this, goal-oriented semantic communication (GSC) has been introduced for wirelessly-connected robotic systems to extract and transmit only goal-relevant semantic representations. While this improves task effectiveness, it generally overlooks practical safety requirements. Meanwhile, existing robotics research often treats safety primarily as a control-level problem, without systematically considering safety across sensing, communication, and control in a closed-loop manner. To bridge this gap, we investigate how to enable safety-aware goal-oriented semantic (SA-GS) sensing, communication, and control co-design in wirelessly-connected robotic systems, aiming to maximize the robotic task effectiveness subject to practical safety requirements. We first introduce {an} architecture {for} wirelessly-connected robotic systems and representative use cases. We then summarize general safety requirements and effectiveness metrics across the use cases. Next, we systematically analyze the unique safety and effectiveness challenges in sensing, communication, and control. Based on these, we further present potential SA-GS research directions. Finally, an Unmanned Aerial Vehicle (UAV) target tracking case study validates that one of the presented SA-GS research directions, i.e., semantic-based C\&C packet execution, could significantly improve safety rate and tracking success rate by more than 2 times and 4.5 times, respectively.
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