Today, robotics and autonomous systems have been increasingly used in various areas such as manufacturing, military, agriculture, medical sciences, and environmental monitoring. However, most of these systems are fragile and vulnerable to adversarial attacks and uncertain environmental conditions. In most cases, even if a part of the system fails, the entire system performance can be significantly undermined. As robots start to coexist with humans, we need algorithms that can be trusted under real-world (not just ideal) conditions. To this end, our research focuses on enabling security, trustworthiness, and long-term autonomy in robotics and autonomous systems. We devise efficient coordination algorithms with rigorous theoretical guarantees to make robots resilient to attacks and aware of the loss from uncertainty. Our long-term goal is to investigate secure, reliable, and scalable multi-robot autonomy when robots use data-driven machine learning techniques in the areas of cyber-physical systems, the Internet of Things, precision agriculture, and smart cities.
Current Robotics Reports: a survery of multi-robot coordination and planning in uncertain and adversarial environments.