Resonant-Inductive Wireless Power Transfer for Autonomous Mobile Robots

Due to the increasing commercial interest in autonomy and sustainability,this paper reviews and presents a comprehensive summary of the resonant-inductive power transmission(RPT)technology for autonomous mobile robots.It outlines historic and recent research activities in wireless power transmission,covering the fundamental operation of microwave,capacitive and inductive power transfer technologies,state-of-the-art developments in RPT for high-power applications,current design and health standards,technological drawbacks,and possible future trends.In this paper,coupling-enhanced pad designs,adaptive tuning techniques,compensation network designs,and control techniques are explored.Major design issues such as coupling variation,frequency splitting,and bifurcation are reviewed.The difference between maximum power transfer and maximum energy efficiency is highlighted.Human exposure guidelines are summarized from documentations provided by the Institute of Electrical and Electronics Engineers(IEEE)and the International Commission on Non-ionizing Radiation Protection(ICNIRP).Other standards like WPC’s Qi and Airfuel design standards are also summarized.Finally,the possible trends of the relevant research and development,particularly dynamic charging,are discussed.The intention of this review is to encourage designs that will relieve robot operators of the burden of frequent manual recharging,and to reduce downtime and increase the productivity of autonomous mobile robots in industrial environments.

Visual Avoidance of Collision with Randomly Moving Obstacles through Approximate Reinforcement Learning

In this research work,a hierarchical controller has been designed for an autonomous navigation robot to avoid unexpected moving obstacles where the state and action spaces are continuous.The proposed scheme consists of two parts:1)a controller with a high-level approximate reinforcement learning(ARL)technique for choosing an optimal trajectory in autonomous navigation;and 2)a low-level,appearance-based visual servoing(ABVS)controller which controls and execute the motion of the robot.A novel approach for path planning and visual servoing has been proposed by the combined system framework.The characteristics of the on-board camera which is equipped on the robot is naturally suitable for conducting the reinforcement learning algorithm.Regarding the ARL controller,the computational overhead is quite low thanks to the fact that a knowledge of obstacle motion is not necessary.The developed scheme has been implemented and validated in a simulation system of obstacle avoidance.It is noted that findings of the proposed method are successfully verified by obtaining an optimal robotic plan motion strategy.

Development and Modeling of Remotely Operated Scaled Multi-wheeled Combat Vehicle Using System Identification

This paper describes the development and modeling of a remotely operated scaled multi-wheeled combat vehicle(ROMWCV)using system identification methodology for heading angle tracking.The vehicle was developed at the vehicle dynamics and crash research(VDCR)Lab at the University of Ontario Institute of Technology(UOIT)to analyze the characteristics of the full-size model.For such vehicles,the development of controllers is considered the most crucial issue.In this paper,the ROMWCV is developed first.An experimental test was carried out to record and analyze the vehicle input/output signals in open loop system,which is considered a multi-input-single-output(MISO)system.Subsequently,a fuzzy logic controller(FLC)was developed for heading angle tracking.The experiments showed that it was feasible to represent the dynamic characteristics of the vehicle using the system identification technique.The estimation and validation results demonstrated that the obtained identified model was able to explain 88.44%of the output variation.In addition,the developed FLC showed a good heading angle tracking.