Abstract
Robot swarms are large-scale multirobot systems 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 compared with systems that rely on traditional approaches to multirobot coordination. 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. In this paper, we present a generic fault-detection system for robot swarms. We show 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 in a detailed simulation environment. In particular, we analyze our system’s capacity to correctly detect robots with faults, false positive rates, performance in a foraging task in which each robot exhibits a composite behavior, and performance under perturbations of the task environment. Results show that our generic fault-detection system is robust, that it is able to detect faults in a timely manner, and that it achieves a low false positive rate. The developed fault-detection system has the potential to enable long-term autonomy for robust multirobot systems, thus increasing the usefulness of robots for a diverse repertoire of upcoming applications in the area of distributed intelligent automation.
Highlights
Robot swarms have the potential to take on numerous real-world tasks [1]
For robot swarms operating in real-world scenarios, we can not rely on any external observatory infrastructure to directly detect presence of faults
We present and study a fault detection approach based on crossregulation model (CRM)-based abnormality detection for robot swarms
Summary
Robot swarms have the potential to take on numerous real-world tasks [1]. In particular, tasks that require sensing or action over large areas or at a high spatiotemporal resolution, such as warehouse management, agriculture automation and environmental monitoring, are candidates for application of future swarm robotics systems. For robot swarms operating in real-world scenarios, we can not rely on any external observatory infrastructure to directly detect presence of faults. The software must rely on readings from the robots’ limited, imperfect sensors, to infer the presence of robots whose behavior deviates from the normal or expected behavior. Such fault detection systems can be divided into two categories: endogenous fault detection and exogenous fault detection. It has been demonstrated that endogenous fault detection approaches can enable a robot to detect the presence of faults such as broken sensors and actuators, see [3,4,5,6] for examples. Catastrophic faults, such as a malfunctioning power source or issues with the onboard computational hardware usually cannot be detected endogenously as they render the robot completely non-operational
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