Final Report Summary - GIFTED-MRS (Generic fault-detection for multirobot systems) Robot swarms are large-scale multirobot systems (robot teams) with decentralized control which means that each robot acts based only on local perception and on local coordination with neighboring robots. The decentralized approach to control confers number of potential benefits. In particular, inherent scalability and robustness are often highlighted as key distinguishing features of robot swarms. It has, however, been shown that swarm robotics systems are not always fault tolerant. To realize the robustness potential of robot swarms, it is thus essential to give systems the capacity to actively detect and accommodate faults. This is the motivation underlying our project In the GiFteD-MRS project, we have developed a generic fault-detection system for robot swarms, thus giving the swarm the capability to monitor itself and signal the presence of faulty robots. We demonstrate how robots with limited and imperfect sensing capabilities are able to observe and classify the behavior of one another. In order to achieve this, the underlying classifier is an immune system-inspired algorithm that learns to distinguish between normal behavior and abnormal behavior online. Through a series of experiments, we systematically assess the performance of our approach for a large swarm of robots. In particular, we analyze our system's capacity to correctly detect robots with faults, the time required to detect a fault since it first occurred in the robot, and the false-alarm rate. Our results show that our developed generic fault-detection system is robust, that it is able to detect faults in a timely manner (in less than 3 minutes for more than 90% of the tested conditions), and that it achieves a low false-alarm rate (mean less than 2% of the experiment duration). Robot fault detection and fault tolerance are two of the most important problems in the field of robotics. Robot swarms in many real world scenarios, operating in unstructured environments for instance, require a fault-detection system that can adapt to temporal variations in the robot's behavior and perturbations to the environment. The GiFteD-MRS fault-detection system demonstrates these capabilities. Therefore, our developed fault-detection system has the potential to assist in long-term autonomous operation with minimal human intervention, thus increasing the usefulness of robots for a diverse repertoire of upcoming applications in distributed intelligent automation, such as environmental monitoring, and agriculture automation.
