1. Mackey 2001

A similar system designed for autonomous robots would provide all the functionality required for fault tolerance and health monitoring, but that development would take a fair amount of time and data gathering to develop models of the target systems.

2. Rymon 1993

Even though this system was designed for medical diagnosis, this is the most promising method which relies on an expert diagnosis system rather then a model of the system. Its primary advantage is that it is completely goal-oriented and that it considers the limited resources required to probe for the diagnosis and to repair of the problem in its decision-making process. Though user (repairer) interaction is provided in this technique, some modifications and additions would have to be made to interface this system with the sensor data on an online system.

3. Soika 1997

The usefulness of this system is its inherent flexibility. It does not rely on models or training, does not care whether the sensors are physically or logically redundant, and only takes into account that redundant sensor readings should agree. As this technique builds up belief over time it is a more robust solution then other agreement based techniques which consider only one point in time. The simplicity of this system may make it difficult to use it to find higher-level planning or navigation problems.

4. Reed 1998

This technique may be particularly useful for diagnosing more challenging cases when failures occur together. Other advantages are that it does not require a model of the system and is sufficiently fast for online diagnosis. The difficulty in using this system will be in gathering adequate training data.

5. Stuck 1995

The usefulness of this technique lies in its ability to detect high level navigational mistakes which lower level diagnosis systems would miss. The system finds mistakes by generating expectations based on a priori information and past results. As this technique builds up belief over time it is a more robust solution then other techniques which consider only one point in time. The usefulness of this technique in reaching the goal of robot self-diagnosis will depend on the computational overhead of this technique and planner’s ability to give this system the information it needs.

6. Visinsky 1995

This technique’s layered approach makes it more suitable for hybrid robotics architectures. Unfortunately its reliance in the low and middle layers on accurate models of normal sensor data make it harder to apply to autonomous robots. The top layer's capabilities including replacement between subsystems for malfunctioning components, tracking the failure rates of components, and tracking the overall capabilities of the robot to complete its task are rare but needed features of fault tolerant robotics systems.

7. Sary 1998

This system is useful in that it is designed to work in real time and has an online learning capability. One limitation to its applicability is the need for a complete model of the target system.

8. Washington 2000

The usefulness of this system lies in its use of a combination of Markov models and Kalman filters which are established methods for discrete and continuous analysis respectively in this field, as well as its speed.

9. Feret 1994

This technique is useful as an enhancement to purely model-based methods. As a generic enhancement it applies to all model-based methods, even those which use only a partial causal model. Like any other learning technique some training time will be required depending on the accuracy of the model.

10. Kleer 1995

The usefulness of this enhancement is limited to diagnosis problems where the required model is available or can be created. The system uses a model of the device based on constraints and an assumption-based truth maintenance system to generate hypotheses. The usefulness of this technique is further reduced by the fact that all probes are assigned the same cost, though it is not difficult to see how varying costs could be added.

11. Teije 1997

If a good casual model can be developed for the faults encountered on mobile robots the strategies presented in this paper might be useful in optimizing the diagnosis system.

12. Lamine 2000

This technique is useful because it provides a flexible framework in which to define faults in the system including planning and navigation problems, but may be too computationally complex to run on a robot.

13. Hung 1999

By using a combination of proven methods the paper develops a system which is general and works well, but may be too computationally complex to run on a robot.

14. Rinner 1999

The system is useful in that only weak models are required to initialize the system and it can handle asynchronous data updates.

15. Narasimham 2000

This paper mainly covers exactly the same system as in McIlraith 1999 with a small contribution from Hung 1999. The only concept presented in this paper which is not covered in the other two is the notion of model-driven adaptive signal processing.

16. Ayab 1993

The usefulness of this system is limited due to its basic assumption that there is only one actual cause for each effect and that it cannot handle sensor data directly. Instead it deals with symbolic models of identifiable causes and effects and therefore could be used for offline refinement and validation of casual models.

17. Vos 1996

The usefulness of this system is enhanced by the requirement for only one model of the normal operation of the robot through any operational mode, though it is not clear how much more difficult it might be to develop that model.

18. Lerner 2000

This technique is useful where a rigorous model of the target system can be built but few sensor readings are available. The applicability of this system is improved through its use of dynamic Bayesian networks which are expressive enough to capture temporal dependencies as well as discrete and continuous data. It is difficult to tell if the system can detect faults quickly enough for robotics applications since the paper specifies how long it took to detect a fault in terms of steps as opposed to runtime.

19. Deuker 1998

This technique is useful where a rigorous model of the normal functioning of the robot is available for fault detection and there is ample time to train the system. Both of these resources are hard to come by in the domain of unmanned ground vehicle self-diagnosis.

20. McIlraith 1999

The usefulness of this system are limited in two ways: it requires a rigorous model of the target device and that it is not expected to work in real time.

21. Madden 1999

The usefulness of this system is limited by the fact that the success of the diagnosis is heavily dependant upon the presence of similar examples in the training set. This weakness makes this system more useful for the industrial manipulator considered and similar devices working in controlled environments then for systems which need to work in an open world like autonomous robots.

22. Helfman 1998

Though little detail is provided in this paper on the system itself, there is plenty of evidence that this system was useful and that its design can be used for similar field diagnosis systems. One possible limit to its applicability is that it seems to be designed solely as a mechanic’s aid, no mention of interfaces between the engine itself and the system are described.

23. Krishnamurthi 1992

The usefulness of this system lies in its use of several built-in diagnosis modules which consist of a shallow reasoning, a deep reasoning module, and a learning component which records the results of the deep reasoning module. One limit to its applicability is that it is designed to generate diagnostic aids for human users, it is not designed to deal directly with the devices it is diagnosing.

24. Djath 2000

This technique concentrates more on using the state of the sensors (faulty or not faulty) to help determine the environment of the robot then on developing a robust determination of that state, which severely limits its applicability to diagnosis problems. The only two features of this system which are of interest are not described in detail. These would be the control logic and the Kalman filter used to combine information from different sensors.

25. Lee 2001

While the results are good this method depends on a higher level of redundancy then is usually found on robot platforms.

26. Ishida 1997

This method has the advantage of being modular which fits well into a behavioral framework, but the high level of overhead makes it a less practical solution to the robot diagnosis problem.

27. Darwiche 1999

This technique is useful only for improving existing structural based diagnosis systems. Since structural based systems require a more complete model of the system then is usually available, this information is not very applicable to the problem of robot self-diagnosis.

28. Goldberg 2001

This analysis is useful in that it provides a framework with which to formally analyze collaborative control systems. It does not provide any new methods for diagnosis or optimizations to diagnosis systems.

29. Sheldon 1993

The usefulness of this technique lies in the graphs used which make it easy to identify and analyze key components of a system design and their dependencies. It does not provide any new methods for diagnosis or optimizations to diagnosis systems.

30. Perraju 1997

The usefulness of these models will be determined in future work where mathematical expressions will be developed for calculating the performance of these automata in the presence of faults.

31. Console 1999

The usefulness of this analysis lies in an important conclusion drawn which is the following: the ability to diagnose a system would be greatly enhanced if the requirements of a diagnosis system are considered in the design of a device, instead of afterwards. It also serves as a good source of information for the state of the field of model based diagnosis.