Fault detection and isolation in cooperative mobile robots using multilayer architecture and dynamic observers

Abstract

SUMMARYMobile robot systems are being used more often in tasks that protect human operators from dangerous environments, but these benefits can be easily lost if the robots spend much of their time being repaired. This implies that any increment in their reliability will also improve their benefits. One way to achieve this is by adding redundant elements to the robot, but this adds complexity and cost to the design. On the other hand, cooperative mobile robots formed by members with the same basic structure provide a natural redundancy of elements, which may be used for reliability improvement. This work presents an architecture that takes advantage of the analytical and sensor redundancy present in groups of cooperative mobile robots in order to increase the reliability of the whole system. First, the design of the architecture is portrayed and the faults to be detected are described. The different layers of the system are then explained and analyzed using several simulations to test their capabilities and limitations. Finally, the experimental results on a group of small mobile robots are shown, validating the results delivered by simulations. These results show that the proposed architecture is able to detect and isolate correctly most of the faults tested.

Generic, scalable and decentralized fault detection for robot swarms

“…A multi-layered approach to fault detection [ 7 ] integrates both endogenous [ 3 – 6 ] and exogenous fault detection [ 8 – 10 , 12 – 14 , 18 , 51 ] in their design, consequently benefiting from both approaches. Robots employing a multi-layered approach can utilize the multitude of robots in large-scale swarms for exogenous fault detection, while simultaneously relying on endogenous fault detection when isolated, or in scenarios where a faulty robot’s neighbors may compensate for its abnormal behavior (see Table 4 , SWARM-COMPENSATE fault-negative incidents).…”

“…Exogenous fault detection has the potential to detect any type of fault, including catastrophic faults. When endogenous fault detection and exogenous fault detection are combined, it is sometimes referred to as multi-layered fault detection [ 7 ].…”

“…Different data-driven approaches to fault detection in multirobot systems have been proposed. In [ 7 ], for instance, robots that meet one another share their respective estimates on the relative distance and direction to one another. Based on a comparison, robots can infer the presence of faults in their onboard sonars and compass sensor.…”

Cooperative Virtual Sensor for Fault Detection and Identification in Multi-UAV Applications

“…Other techniques have been used for vehicle relative position estimation and applied to FDI systems. Work [21] shows this cooperative FDI approach with small ground vehicles in a laboratory experiment, using cameras to detect the position of other ground robots. In [22], a cooperative FDI system for the odometry sensors is presented, in which the relative velocity of a robot with respect to the leader robot is obtained by matching a laser range sensor (LRS) image transmitted by the leader robot with the LRS obtained by its own laser sensor.…”

Cooperative sensor fault recovery in multi-UAV systems

“…Some representative examples involving close operations between two or more UAVs include autonomous aerial refueling [1], cooperative aerial transportation [2], or formation flights [3]. Having some method for detecting unexpected deviations in the normal operation of the multi-UAV system would then be useful for reducing the probability of accidents [4] [5], and thus, the associated time and cost for repairing or replacing the affected vehicles. In this sense, if the UAVs of the fleet are capable to perceive partially the state of the other vehicles, then it would be possible to provide fault tolerance without requiring any addition vehicles, just exploiting the sensors onboard each UAV.…”