Self Detection of Failure in Robots

Robotics is an emerging field and robots may be employed in places where human intervention is not possible. Multiple robots may work in a coordinated manner to achieve certain tasks. However one of the big problems is the detection and recovery from failures, since human intervention may not be possible. To this end we propose an autonomic self-detection and self-recovery robotics architecture based on the human immune system. In particular we look at two types of communication failure, failures caused by robot isolation and failures caused by intermittent message loss. This thesis focuses on one component of an autonomic robotic architecture namely, self failure detection mechanism in robots. Our goal is to make the robot recognize the failure encountered during its operation and send related failure information to the activation unit for further action. We propose an approach to self-detection based on observation graphs. Simulation results show that the failures were effectively detected. The proposed recognition unit is similar to T-cells in the human immune system.Computer Science Departmen

Efficient Failure Detection on Mobile Robots Using Particle Filters with Gaussian Process Proposals

The ability to detect failures and to analyze their causes is one of the preconditions of truly autonomous mobile robots. Especially online failure detection is a complex task, since the effects of failures are typically difficult to model and often resemble the noisy system behavior in a fault-free operational mode. The extremely low a priori likelihood of failures poses additional challenges for detection algorithms. In this paper, we present an approach that applies Gaussian process classification and regression techniques for learning highly effective proposal distributions of a particle filter that is applied to track the state of the system. As a result, the efficiency and robustness of the state estimation process is substantially improved. In practical experiments carried out with a real robot we demonstrate that our system is capable of detecting collisions with unseen obstacles while at the same time estimating the changing point of contact with the obstacle