@inproceedings{DrakYoussefPloegeretal.2016,
  author    = {Ahmad Drak and Youssef Youssef and Paul Pl{\"o}ger and Anastassia Kuestenmacher},
  title     = {Remote Fault Diagnosis of Robots Using a Robotic Black Box},
  series = {27th International Workshop on Principles of Diagnosis: DX-2016, Denver, Colorado, October 4-7, 2016},
  year      = {2016},
  abstract  = {Autonomous mobile robots comprise of several hardware and software components. These components interact with each other continuously in order to achieve autonomity. Due to the complexity of such a task, a monumental responsibility is bestowed upon the developer to make sure that the robot is always operable. Hence, some means of detecting faults should be readily available. In this work, the aforementioned fault-detection system is a robotic black box (RBB) attached to the robot which acquires all the relevant measurements of the system that are needed to achieve a fault-free robot. Due to limited computational and memory resources on-board the RBB, a distributed diagnosis is proposed. That is, the fault diagnosis task (detection and isolation) is shared among an on-board component (the black box) and an off-board component (an external computer). The distribution of the diagnosis task allows for a non-intrusive method of detecting and diagnosing faults, in addition to the ability of remotely diagnosing a robot and potentially issuing a repair command. In addition to decomposing the diagnosis task and allowing remote diagnosability of the robot, another key feature of this work is the addition of expert human knowledge to aid in the fault detection process.},
  language  = {en}
}