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

TL;DR

This paper presents a hardware-in-the-loop optimization framework for robotic wireless energy transfer in dynamic environments, enabling efficient navigation and charging of remote sensors with experimental validation.

Contribution

It introduces a novel joint optimization framework that adapts to unknown environments through iterative model updates and anchor list refinement, validated in simulation and real-world tests.

Findings

Significantly reduces WET mission completion time

Ensures collision avoidance and energy harvesting constraints

Validated in high-fidelity simulation and multi-robot testbed

Abstract

Wireless energy transfer (WET) is a ground-breaking technology for cutting the last wire between mobile sensors and power grids in smart cities. Yet, 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 paper proposes a hardware-in-the-loop joint optimization framework that offers three distinctive features: 1) efficient model updates and re-optimization based on the last-round experimental data; 2) iterative refinement of the anchor list for adaptation to different environments; 3) verification of algorithms in a high-fidelity Gazebo simulator and a multi-robot testbed. Experimental results show that the proposed framework significantly saves the WET mission completion time while satisfying collision avoidance and energy harvesting constraints.