1st year CDT student

这个博士研究项目的重点是增强机器人中ai /ML系统的安全性和鲁棒性。随着这些技术成为自主系统不可或缺的一部分，它们引入了攻击者可以利用的漏洞，可能危及安全性和功能。该研究旨在识别和解决自动路径规划、计算机视觉和强化学习等应用中的安全威胁。该研究包括模拟对抗性攻击，例如对路径规划算法的暴力攻击，以发现可能影响系统完整性的弱点和环境因素。通过在模拟和现实世界的机器人平台上实施这些攻击，该研究旨在了解当前AI/ML算法在对抗条件下的局限性。全面的安全评估将对机器人中的各种AI/ML应用进行分类，优先考虑风险最大的应用。该项目将循环评估多个AI/ML应用程序，使用对抗性机器学习方法来测试和提高其稳健性。这种迭代方法确保了对漏洞的详细检查和有效对策的开发。最终目标是创建一个适用于各种机器人系统的强大安全框架，确保各行业的安全可靠运行。本研究解决了提高AI/ML应用安全性的关键需求，提供了支持机器人技术不断发展的解决方案。

This PhD research project focuses on enhancing the security and robustness ofAI/ML systems in robotics. As these technologies become integral to autonomous systems,they introduce vulnerabilities that adversaries can exploit, potentially compromising safetyand functionality. The research aims to identify and address security threats in applicationslike autonomous path planning, computer vision, and reinforcement learning.The study includes simulating adversarial attacks, such as brute-force attacks on path-planningalgorithms, to uncover weaknesses and environmental factors that may affect system integrity.By implementing these attacks on both simulated and real-world robotic platforms, the research seeks to understand the limitations of current AI/ML algorithms under adversarialconditions.A comprehensive security assessment will catalogue various AI/ML uses in robotics, prioritisingthose most at risk. The project will cyclically evaluate multiple AI/ML applications, usingadversarial machine learning methods to test and improve their robustness. This iterativeapproach ensures a detailed examination of vulnerabilities and the development of effectivecountermeasures.The ultimate goal is to create a robust security framework applicable to various robotic systems,ensuring safe and reliable operations across industries. This research addresses the critical needfor improved security in AI/ML applications, providing solutions that support the evolvinglandscape of robotics technology.