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arXiv 2302.09182cs.ROcs.FLcs.SYeess.SY
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基于概率验证的安全网络化机器人技术

Safe Networked Robotics with Probabilistic Verification

The University of Texas at Austin(德克萨斯大学奥斯汀分校)

Sai Shankar Narasimhan, Sharachchandra Bhat, Sandeep P. Chinchali

AI总结:

针对网络化机器人因通信延迟导致安全属性违反的问题,提出基于形式化验证构建运行时防护盾的方法,能提供安全动作列表并以期望概率满足安全约束,在F1/10车辆上验证了有效性。

摘要:

自主机器人必须利用丰富的传感数据做出安全的控制决策。为处理这些数据,受计算资源限制的机器人通常需要远程计算或云的协助,这些远程计算或云运行着计算密集型的深度神经网络感知或控制模型。然而,这种协助会因网络延迟而产生时间代价,导致云使用过去的观测数据来计算当前机器人状态的控制指令。此类通信延迟可能潜在导致基本安全属性(如避障)的违反。本文提出了确保在具有随机延迟的通信网络上运行的机器人安全的方法。为此,我们使用形式化验证工具构建一个防护盾(即运行时监视器),该防护盾能针对任何延迟的传感观测,在给定预期和最大网络延迟的情况下提供安全动作列表。我们的防护盾具有最小侵入性,能使网络化机器人以期望的概率满足以时序逻辑规范表达的关键安全约束。我们在真实的F1/10自主车辆上演示了该方法,该车辆在室内环境中导航并通过拥堵的WiFi链路传输丰富的激光雷达传感数据。

英文摘要:

Autonomous robots must utilize rich sensory data to make safe control decisions. To process this data, compute-constrained robots often require assistance from remote computation, or the cloud, that runs compute-intensive deep neural network perception or control models. However, this assistance comes at the cost of a time delay due to network latency, resulting in past observations being used in the cloud to compute the control commands for the present robot state. Such communication delays could potentially lead to the violation of essential safety properties, such as collision avoidance. This paper develops methods to ensure the safety of robots operated over communication networks with stochastic latency. To do so, we use tools from formal verification to construct a shield, i.e., a run-time monitor, that provides a list of safe actions for any delayed sensory observation, given the expected and maximum network latency. Our shield is minimally intrusive and enables networked robots to satisfy key safety constraints, expressed as temporal logic specifications, with desired probability. We demonstrate our approach on a real F1/10th autonomous vehicle that navigates in indoor environments and transmits rich LiDAR sensory data over congested WiFi links.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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