REXASI-PRO: REliable & eXplAinable Swarm Intelligence for People with Reduced mObility

REXASI-PRO项目旨在发布一个新颖的工程框架。REXASI-PRO项目旨在发布一个新的工程框架，以开发更环保、更值得信赖的人工智能解决方案。在方法论中，安全、保障和可解释性是纠缠在一起的。此外，在框架的整个生命周期中，道德方面将被持续监控。为此，REXASI-PRO项目引入了几个新特性。该项目将同时开发用于社交导航的新型建筑可靠性解决方案的设计，以及一种方法来证明基于人工智能的自动驾驶汽车对行动不便的人的稳健性。构建可信度社会导航算法将利用社交机器人的数学模型。机器人将通过使用隐式和显式交流进行训练。REXASI-PRO方法通过利用新颖的可解释性方法来提高整个系统的健壮性，从而增强了现有的系统级和项目级工程框架。REXASIPRO将发布额外的验证和验证方法，以确保人工智能在循环中的安全性。在其他发展中，一种新的学习范式将安全要求嵌入到深度神经网络中，用于规划算法、基于保形预测区域的运行时监控、可信感知和安全通信。该方法将用于验证自动轮椅和飞行机器人的鲁棒性。飞行机器人将配备无偏见的机器学习解决方案，用于人员检测，在紧急情况下也将是可靠的。因此，REXASI-PRO将使AIsolutions更环保。为此，将开发基于人工智能的协调器来增强机器人的智能和拓扑方法。REXASI-PRO框架将通过自动轮椅和飞行机器人之间的协作来展示，以帮助行动不便的人。

The REXASI-PRO project aims to release a novel engineering framework. The REXASI-PRO project aims to release a novel engineering framework to develop greener and Trustworthy Artificial Intelligence solutions. In the methodology, safety, security, and explainability are entangled. In addition, throughout the entire lifecycle of the framework, ethics aspects will be continuously monitored. To this end, the REXASI-PRO project introduces several novelties. The project will develop in parallel the design of novel trustworthy-by-construction solutions for social navigations and a methodology to certify the robustness of AI-based autonomous vehicles for people with reduced mobility. The trustworthy-by-construction social navigation algorithms will exploit mathematical models of social robots. The robots will be trained by using both implicit and explicit communication. REXASI-PRO methodology augments existing system-level and item level engineering frameworks by leveraging novel eXplainability methods to improve the entire system's robustness. REXASIPRO will release additional verification and validation approaches for safety and security with the AI in the loop. Among the other developments, a novel learning paradigm embeds safety requirements in Deep Neural Network for planning algorithms, runtime monitoring based on conformal prediction regions, trustable sensing, and secure communication. The methodology will be used to certify the robustness of both autonomous wheelchairs and flying robots. The flying robots will be equipped with unbiased machine learning solutions for people detection that will be reliable also in an emergency. Thus, REXASI-PRO will make the AIsolutions greener. To this end, both an AI-based orchestrator to augment the intelligence of the robots and topological methods will be developed. The REXASI-PRO framework will be demonstrated by enabling the collaboration among autonomous wheelchairs and flying robots to help people with reduced mobility.