Nowadays wireless communications is an important aspect of mobile robotics. It is common that mobile robots need to establish wireless links to exchange information with other robots, base stations or sensor nodes. And, as in traditional mobile communications small-scale wireless channel fading also occurs in these scenarios. This phenomenon means that the channel gain will vary significantly over small-distances and in a random manner. This degrades both the communication ability of the robots and as a consequence, their overall performance in executing certain tasks. There is therefore a clear need to compensate for this small-scale fading.
We could of course compensate small-scale fading in robotic communications using classical diversity techniques. But these diversity techniques were designed for transceivers that either cannot move, or can move but have no control over their position. In the context of robotic communications we can think of mobile robots as transceivers who know their own position and can also control it. This allows us to create a new form of diversity called mobility diversity whose principle is as follows. If the mobile robot experiences a poor channel gain due to a deep fading then it can alter its location by a small amount in order to find a new point with a higher channel gain (note that a low channel gain requires more transmitter energy to achieve the same SNR at the receiver as a high channel gain). Now, the more points the robot explores then the higher is the probability of obtaining a high channel gain but the consumption of mechanical energy also increases. Thus efficient mobility diversity algorithms (MDAs) must be able to deliver high channel gains while simultaneously using a small amount of mechanical energy.
In this thesis, we start by simultaneously considering the theoretical aspects of both wireless communications and robotics that underpin this interdisciplinary problem. We then develop intelligent algorithms (MDAs) to solve the maximise channel gain/ minimise mechanical energy challenge while looking at various modifications that can occur i.e., predetermined and adaptive stopping points; MDAs for robots as wireless relays; MDAs that incorporate energy harvesting; and finally optimisation over a continuous search space.
In summary, mobility diversity is a relatively new research area in an early stage of development. In this thesis we have developed a comprehensive theory for MDAs that will form the basis for future applications as outlined in chapter 7.
