Robotic Wireless Energy Transfer in Dynamic Environments: System Design and Experimental Validation

Abstract

Wireless energy transfer (WET) is a ground-breaking technology for cutting the last wire between mobile sensors and power grids in smart cities. However, WET only offers effective transmission of energy over a short distance. Robotic WET is an emerging paradigm that mounts the energy transmitter on a mobile robot and navigates the robot through different regions in a large area to charge remote energy harvesters. However, it is challenging to determine the robotic charging strategy in an unknown and dynamic environment due to the uncertainty of obstacles. This article proposes a hardware-in-the-loop joint optimization framework that offers three distinctive features: efficient model updates and re-optimization based on the last-round experimental data; iterative refinement of the anchor list for adaptation to different environments; and verification of algorithms in a high-fidelity Gazebo simulator and a multi-robot testbed. Experimental results show that the proposed framework significantly saves WET mission completion time while satisfying energy harvesting and collision avoidance constraints.