In order to measure how well a device is or a person deals with a given vehicle, many people work on predefined obstacle courses. Kilkens et al. reviewed a wide array of wheelchair skill tests, aimed to asses the ability to propel and maneuver a wheelchair under standardized
and/or simulated conditions of daily living. Skills included in the 24 tests in order of frequency were:

- Wheelchair propulsion, assessed in terms of period of time, a fixed distance or longest distance possible.
- Transfer from and to the wheelchair, usually examining the performance in different transfers.
- Negotiation of kerbs, with kerb's height ranging from 0.025 to 0.15m, and some requiring ascending and descending the kerb.
- Ascending slopes, defined in terms of inclination (ranging from 1 to 11 degrees) and length (ranging from 3 to 21m).
- Traversing tracks, e.g. slalom, figure of eight and obstacle course.
- Sprinting over a fixed distance (ranging from 6.5 to 30m).
- Performing a wheelie was also a skill included in few of the tests assessed.
- Half of the tests included other specific wheelchair skills, e.g. managing brakes, negotiating doors or loading the wheelchair into a car.

In eight of these tests, wheelchair skills were a part of a broader measure of ADL skills like eating, bed mobility skills and washing hands. Ideally, tests have to be as efficient and as short as possible, and should not require much space or special equipment. The VFM or the TAMP take like 1 hour to complete, while the WST needs only 30 min.

The most common outcome of these tests are values like task performance time, Physical strain, Independence in performance, distance covered in propulsion, endurance and other subjective ratings like perceived difficulty. Kilkens argues that tests should have preferably a simple scoring system, convenient to use and easi to analyze. It is important to note that these metrics can be applied mostly to obstacle courses but usually not to daily living, as they are measured over a given time and trajectory.



The main problem with most of these courses is that they require a large space and investment to build structures just according to specifications. Furthermore, they are used to measure skills in controlled situations, rather than in everyday ones. Still, they are widely used because they provide a benchmark to test most wheelchairs models.

The easiest solution to the localization problem is to use dead reckoning: if we know how fast and how long we have been moving, we can estimate approximately how far we are from our original position. Alternatively, we can measure the number and length of our steps or the number of wheel turns of our vehicle to do the same. Indeed, boats used to proceed like that: their speed and direction was estimated in terms of how long a knot in a rope dropped from the bow of the vessel took to reach its back side (1) and new locations were estimated in terms of how long that speed was kept. Naturally, odometrics can only work for a time, because errors tend to accumulate in an unbounded way. If no external reference is used to correct the position from time to time, after a while the vehicle is lost. One could try, for example, to precalculate the path to a spot 10 m away and then try to walk there blindfolded. Chances are one would most likely end fairly far from the desired destination, specially if turns are involved, or, at worst, stuck into an unexpected obstacle. In order to avoid this problem, we could just open the eyes that, in the robot case, means we can combine different information sources using methods like Kalman filters or Montecarlo techniques to remove or, at least, reduce uncertainty.

Ok, so maybe dead reckoning was not such a good idea...

Things are easier if we know the location of at least two relevant features in the environment that we can perceive at a given location. For example, boats originally relied on the position of the stars and nowadays they use GPS satellites to triangulate where they are. Mobile robots can do the same if external beacons are available (GPS, ultrasounds, wireless beacons, etc).

Of course, active beacons might not be available; GPS, for example, can not be used indoors. If this is the case, robots can refer to known features in the environment -natural or artificial landmarks- to estimate where they are, in the same way we could check a metro station we are watching in a map to decide where we are at the moment. In these cases, cameras are typically used not as range sensors, but to detect the expected landmarks within the field of view e.g. (Urdiales et al, 2009).
A very special case among these ones is omnicameras, whose optics have been designed so that all points in the world are projected through a single center of projection. Hence, they capture 360º fields of view in a single frame and usually extract fairly reliable landmarks from the picture (see Keith Price bibliography on the subject).

If cameras are not available or do not want to be used, geometric landmarks can also be detected. These landmarks are significant, distinct locations that can be detected by means of range sensors, like a corridor with two open doors at each side. This would be equivalent to try to check where we are by touching the walls if the lights go out. However, similar landmarks may exist in different areas of the environment, so it is necessary to disambiguate perceptions by keeping some track of the mobile position on accumulating several landmarks in a row via a statistic method like, say, Markov Model e.g. (Baltzakis, 2003)(Fox, 1998).

In absence of a model of the environment, it is necessary to correct the position, store detected landmarks and build a new model, all at the same time. Think, for example, of trying to draw a map of a city we have never been in by marking every distinctive building we see and trying to guess the distance and relative position between each two of them. This problem is known as SLAM. SLAM conforms a quite complex, complete field of research and it is out of the scope of our work.

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(1) In fact, the idea was to sing a song and write down in the bitacora in which word it had stopped. One can only hope that the crew was able to keep the rythm.