CONtext-aware VerifIable dyNamiC dEliberation

上下文感知的可验证动态审议

• 批准号: 10042096
• 金额: $ 72.13万
• 依托单位: UNIVERSITY OF BIRMINGHAM
• 依托单位国家: 英国
• 项目类别: EU-Funded
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10042096
• 关键词: CONtext; aware; VerifIable; dyNamiC; dEliberation

In recent years, robots are being increasingly deployed outside strictly controlled environments. When faced with unexpected situations, these robots are often incapable of taking appropriate action and require human intervention. The goal of CONVINCE is to advance the capabilities of robots to perform complex tasks robustly and safely within unstructured environments via autonomous and unsupervised adaptation to the environment and operational context. More specifically, the key contribution is to develop cognitive deliberation capabilities that ensure safe robot operation over extended periods of time without human intervention. These capabilities will be integrated into a model-driven software toolchain to allow developers to build application-specific deliberation systems able to i) determine robot’s behaviors required to fulfill a given task, also taking into account the context in which the robot operates and the experience gained during previous executions of the same task, ii) deploy and configure the components that are required to execute these behaviors, iii) automate the analysis of behaviors to ensure that they are safe and secure, leveraging on formal models and tools for design-time and run-time verification. The toolchain shall be based on proven system modeling concepts, particularly from the EU-funded project RobMoSys. Major parts shall be open-sourced with adapters to relevant robotics frameworks like the Robot Operating System (ROS). To ensure real-world applicability, CONVINCE will demonstrate the technology developed in the project on three different realworld use cases, each of which presents unique technical difficulties and utilizes robotic systems of increasing complexity, in different application domains: vacuum cleaner robot, assembly robot, and robotic museum guide.

近年来，机器人越来越多地部署在严格控制的环境之外。当面对意外情况时，这些机器人通常无法采取适当的行动并需要人类干预。说服力的目的是通过自主和无监督的环境和操作环境中的自主和无监督的适应来推进机器人在非结构化环境中坚固，安全地执行复杂任务的能力。更具体地说，关键的贡献是开发认知审议能力，以确保在没有人类干预的情况下长时间的安全机器人操作。这些功能将集成到模型驱动的软件工具链中，以允许开发人员构建针对申请的特定审议系统，i）确定机器人的行为以完成给定的任务所需的行为，还考虑了机器人操作以及机器人操作的上下文以及在同一任务中执行这些行为的同一任务的先前执行过程中所需的行为和执行行为的情况，iii III的行为，iii ii ii III的行为，安全，利用正式模型和工具进行设计时间和运行时验证。该工具链应基于经过验证的系统建模概念，尤其是来自欧盟资助的项目Robmosys。主要部分应由相关机器人框架（例如机器人操作系统（ROS））的适配器开源。为了确保现实世界中的适用性，Corecince将证明该项目在三种不同的现实世界用例中开发的技术，每种案例都带来了独特的技术困难，并利用了不同应用领域的机器人系统，以增加复杂性的机器人系统：真空吸尘器机器人，组装机器人机器人和机器人博物馆指南。