Daniel Bonilla Licea

A robotic mobility diversity algorithm with Markovian trajectory planners

In this paper we develop an intelligent algorithm that obtains the optimal trajectory (i.e., a non equally spaced sequence of stopping points) for a robot which tries to find a wireless channel with a minimum predefined channel gain over which to transmit its data. We show that this algorithm can be optimized in two ways: (i) minimum searching time (but suboptimal energy expenditure), or (ii) minimum energy (but not necessarily minimum searching time). This problem can be viewed as similar to classical RF selection diversity (but with an infinite number of diversity branches). However, it is different in so far as here we seek either highly or poorly correlated branches (i.e., the wireless channels at stopping points) depending upon the realisation at the last stopping point(s). We show that this strategy is superior (both in searching time and energy expenditure) when compared with a classical diversity approach for devising the robots trajectory.

Mobility Diversity-Assisted Wireless Communication for Mobile Robots

Mobile robots that wish to communicate wirelessly often suffer from fading channels. They need to devise an energy-efficient strategy to search for a high-channel-gain position in a near vicinity from which to begin communications. Such a strategy has recently been introduced through the mobility diversity with multithreshold algorithm (MDMTA). In this paper, we establish the theoretical framework for a generalized version of the MDMTA. This allows improved wireless communications in fading channels for mobile robots via intelligent robotic motion with low mechanical energy expenditure.

Mobile Robot Path Planners With Memory for Mobility Diversity Algorithms

Mobile robots (MRs) using wireless communications often experience small-scale fading so that the wireless channel gain can be low. If the channel gain is poor (due to fading), the robot can move (a small distance) to another location to improve the channel gain and so compensate for fading. Techniques using this principle are called mobility diversity algorithms (MDAs). MDAs intelligently explore a number of points to find a location with high channel gain while using little mechanical energy during the exploration. Until now, the location of these points has been predetermined. In this paper, we show how we can adapt their positions by using channel predictors. Our results show that MDAs, which adapt the location of those points, can in fact outperform (in terms of the channel gain obtained and mechanical energy used) the MDAs that use predetermined locations for those points. These results will significantly improve the performance of the MDAs and consequently allow MRs to mitigate poor wireless channel conditions in an energy-efficient manner.

Robust Trajectory Planning for Robotic Communications Under Fading Channels

We consider a new problem of robust trajectory planning for robots that have a physical destination and a communication constraint. Specifically, the robot or automatic vehicle must move from a given starting point to a target point while uploading/downloading a given amount of data within a given time, while accounting for the energy cost and the time taken to download. However, this trajectory is assumed to be planned in advance (e.g., because online computation cannot be performed). Due to wireless channel fluctuations, it is essential for the planned trajectory to be robust to packet losses and meet the communication target with a sufficiently high probability. This optimization problem contrasts with the classical mobile communications paradigm in which communication aspects are assumed to be independent from the motion aspects. This setup is formalized here and leads us to determining non-trivial trajectories for the mobile, which are highlighted in the numerical result.