ReFrESH: A self-adaptation framework to support fault tolerance in field mobile robots

Mobile robots are being employed far more often in extreme environments, such as urban search and rescue, with greater levels of autonomy; yet recent studies on field robotics show that numerous failure modes affect the reliability of the robot in meeting mission objectives. Therefore, fault tolerance is increasingly important for field robots operating in unpredictable environments to ensure safety and effectiveness of the system. This paper demonstrates a self-adaptation framework, ReFrESH, that contains mechanisms for fault detection and fault mitigation. The goal of ReFrESH is to provide diagnosable and maintainable infrastructure support, built into a real-time operating system, to manage task performance in the presence of unexpected uncertainties. ReFrESH augments the port-based object framework by attaching evaluation and estimation mechanisms to each functional component so that the robot can easily detect and locate faults. In conjunction, a task level decision mechanism interacts with the fault detection elements in order to generate and choose an optimal approach to mitigating faults. Moreover, to increase flexibility of the fault tolerance, ReFrESH provides self-adaptation support for both software and hardware functionality. To our knowledge, this is the first framework to support both software and hardware self-adaptation. A demonstrative application of ReFrESH illustrates its applicability through a target tracking task deployed on a mobile robot system.