Improving Radio Energy Harvesting in Robots Using Mobility Diversity

Abstract

Abstract-In this article, we propose a new technique which exploits a robot's (intelligently) controlled mobility to maximise stored radio energy. In particular, we examine a scenario where the mobile robot takes a break from its normal activity for a duration of T secs. This 'dead time' consists of three phases -searching, positioning and resting -which ensure that the robot can optimise its energy harvesting from a base station transmitting a narrowband RF signal over a flat fading wireless channel. We utilise the mobility diversity principle, which arises due to the spatial wireless channel diversity experienced by motion of the robot. By optimal exploitation of the small scale fading we maximize the net amount of energy (i.e., the energy harvested by the robot minus the mechanical energy used for motion) that the robot stores over the 'dead time'. To the best of the authors' knowledge, this article is the first use of the mobility diversity principle to optimise energy harvesting from an RF signal. We demonstrate that mobility, if intelligently controlled, is actually not a foe but is indeed a friend which can provide significant benefits under wireless fading channels. Through simulations we verify the analytical results and illustrate the improvement in the energy stored compared with not using intelligent mobility. Finally, we show that the efficiency of our approach is clearly coupled with various design parameters including the centre frequency of the narrowband RF signal and the duration of the 'dead time'.

Robotic Mobility Diversity Algorithm with Continuous Search Space

“…To do this, we select a desired distance 10 between sampling points of ∆ = 0.05λ. It was shown in [8] that this is sufficient to obtain the maximum channel power from each path under noiseless conditions. For the design of the paths we used sets of N = 25 point paths.…”

“…In [7] the authors implemented experimentally a continuous MDA with linear, circular, spiral and random paths. Then in [8] we proposed a continuous MDA with a linear path where the length was optimized.…”

Trajectory planning for energy-efficient vehicles with communications constraints

“…Therein, a robot has to optimize its trajectory to minimize a cost which consists of the sum of the energy consumed by the transmitter and the motion energy. Another relevant paper is given by [6]; the goal of the robot is find a trajectory which allows wireless energy to be harvested. Then in [7] the authors design a control law for a drone to follow a ground robot while maintaining a minimum data rate in an optical wireless communications link.…”