Robotic Safe Adaptation In unprecedented Situations

前所未有的情况下的机器人安全适应

• 批准号: 10109092
• 金额: $ 70.48万
• 依托单位: UNIVERSITY OF YORK
• 依托单位国家: 英国
• 项目类别: EU-Funded
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10109092
• 关键词: Robotic; Safe; Adaptation; unprecedented; Situations

The robots of tomorrow will be endowed with the ability to adapt to drastic and unpredicted changes in their environment including humans. Such adaptations can however not be boundless: the robot must stay trustworthy, i.e. the adaptations should not be just a recovery into a degraded functionality. Instead, it must be a true adaptation, meaning that the robot will change its behavior while maintaining or even increasing its expected performance, and stays at least as safe and robust as before. RoboSAPIENS will focus on autonomous robotic software adaptations and will lay the foundations for ensuring that such software adaptations are carried out in an intrinsically safe, trustworthy and efficient manner, thereby reconciling open-ended self-adaptation with safety by design. RoboSAPIENS will also transform these foundations into 'first time right'-design tools and robotic platforms, and will validate and demonstrate them up to TRL4. To achieve this over-all goal, RoboSAPIENS will extend the state of the art in four main objectives. 1. It will enable robotic open-ended self-adaptation in response to unprecedented system structural and environmental changes. 2. It will advance safety engineering techniques to assure robotic safety not only before, during and after adaptation. 3. It will advance deep learning techniques to actively reduce uncertainty in robotic self-adaptation. 4. It will assure trustworthiness of systems that use both deep-learning and computational architecturesfor robotic self-adaptation. To realise these objectives, RoboSAPIENS will extend techniquessuch as MAPE K (Monitor, Analyze, Plan, Execute, Knowledge) and Deep Learning to set up generic adaptation procedures and also use an SSH dimension. RoboSAPIENS will demonstrate this trustworthy robotic self-adaptation on four industry-scale use cases centered around an industrial disassembly robot, a warehouse robotic swarm, a prolonged hull of an autonomous vessel, and human-robotic interaction.

未来的机器人将被赋予适应包括人类在内的环境中剧烈和不可预测变化的能力。然而，这样的适应不能是无限的：机器人必须保持可信赖，即适应不应该只是恢复到退化的功能。相反，它必须是一个真正的适应，这意味着机器人将改变其行为，同时保持甚至提高其预期的性能，并保持至少像以前一样安全和健壮。RoboSAPIEN将专注于自主机器人软件的调整，并将为确保以本质安全，可靠和有效的方式进行此类软件调整奠定基础，从而协调开放式自适应与设计安全。RoboSAPIEN还将把这些基础转化为“第一次正确”的设计工具和机器人平台，并将验证和演示它们直到TRL4。为了实现这一总体目标，RoboSAPIEN将在四个主要目标上扩展最先进的技术。1.它将使机器人能够进行开放式的自我适应，以应对前所未有的系统结构和环境变化。2.它将推进安全工程技术，以确保机器人不仅在适应之前，期间和之后的安全。3.它将推进深度学习技术，以积极减少机器人自适应的不确定性。4.它将确保使用深度学习和计算架构进行机器人自适应的系统的可信度。为了实现这些目标，RoboSAPIEN将扩展MAPE K（监视、分析、计划、执行、知识）和深度学习等技术，以建立通用的适应程序，并使用SSH维度。RoboSAPIEN将在四个行业规模的用例中展示这种值得信赖的机器人自适应能力，这些用例分别围绕工业拆卸机器人、仓库机器人群、自主船舶的延长船体和人机交互。