\section{Sensors used in Smart Wheelchairs}\label{examplesensors}

The discussion of various shared control techniques presented in \hyperref[exampletechniques]{Section 2.2} has provided examples of numerous sensor modalities employed for gaining useful information about the environment. Simpson (2005) has summarised the main advantages and limitations of commonly used sensors. 

Ultrasonic sensors are one of the most popular types of sensors used, being cheap, easily available, light weight, compact, low powered devices etc. (Velázquez, 2010). They are based on the principle that sound waves incident on a surface are reflected back. Hence they provide accurate distance measurements provided that the angle of inclination of the beam is almost perpendicular to the object detected. If the angle of incidence is large, the reflected beam may not be captured by the sensor. Substances that absorb sound cannot be detected. Moreover, multiple reflections of a sound wave can cause perception errors due to ghost echoes and cross talk can occur due to sensors picking up sound waves from other sensors.

Infra-Red (IR) sensors offer all the advantages of sonar sensors but are cheaper and may not be as accurate. They are sensitive to background light and colour of obstacles since they work on the principle of transmission and reflection of infra-red light rays. They do not detect transparent or refractive surfaces. Both sonar and IR sensors can detect drop-offs only if oriented directly facing the ground in order to be able to receive the echoes.

Laser range finders (LRF) or scanners provide more accuracy and overcome many of the drawbacks of sonar and IR sensors. They are capable of providing a 180° view and can detect drop-offs. However this comes at the expense of high power consumption, larger dimensions and high costs. As an illustration of cost, the laser range finder used in the MAid project cost approximately US\$4000, compared to just a few tens of dollars for sonar or IR sensors. However it was deemed indispensable for detecting objects in a dynamic and crowded environment and hence incorporated in the system. The wheelchair used by Carlson \& Demiris (2010) also uses a laser scanner. Sharioto (Nuttin et al, 2002) uses an infra-red scanner.

Machine vision is gaining popularity with smart wheelchair developers, being more robust and accurate. They provide wider and deeper coverage of the environment, are reasonably priced, have higher sampling speeds and are non-invasive (Velázquez, 2010). They are smaller compared to LRFs and hence easier to mount. They have been used as the access device for eye, face or head movements and also for sensing the environment (Kuno et al, 1999; Yanco, 2000; Carslon \& Demiris, 2008). However, machine vision requires the use of image processing to extract useful information and hence the computational requirements are higher.  

Wheel encoders are commonly used to provide odometry or ground truth data. 

Most intelligent wheelchairs use multiple sensor modalities as seen in the discussion in \hyperref[exampletechniques]{Section 2.2}. By utilising the advantages provided by each type of sensor and compensating for limitations of one type by using another, system reliability can be improved. Moreover, robust shared control algorithms that are not constrained by the accuracy of sensor data will also enhance the performance of the wheelchair.

Sensor arrangement on the wheelchair significantly contributes to the utility of the sensor data and the effectiveness of the algorithms used. All wheelchairs providing obstacle avoidance have one or another sensor modality placed on the front of the wheelchair. Collision with the sides and rear can be avoided by providing sensor coverage in these places as well. Another challenge is the height of mounting the sensors. Due to varied environments and different seating arrangements for wheelchair users, the sensors must be suitably positioned to prevent blind spots. This is also an important issue to be considered to enable commercialisation of smart wheelchairs (Bourhis et al, 2001). 
