MEAE 2023 Keynote Speakers


Prof. Wen-Hua Chen, Loughborough University, UK (FIEEE, CEng, FIMechE, FIET)

Dr Wen-Hua Chen holds Professor in Autonomous Vehicles in the Department of Aeronautical and Automotive Engineering at Loughborough University, UK. Prof. Chen has a considerable experience in control, signal processing and artificial intelligence and their applications in aerospace, automotive and agriculture systems. In the last 15 years, he has been working on the development and application of unmanned aircraft system and intelligent vehicle technologies, spanning autopilots, situational awareness, decision making, verification, remote sensing for precision agriculture and environment monitoring. His unmanned vehicles related research is widely supported by the UK government and industry. He is a Chartered Engineer, and a Fellow of IEEE, the Institution of Mechanical Engineers and the Institution of Engineering and Technology, UK. Recently Prof Chen was awarded the prestigious EPSRC (Engineering and Physical Science Research Council) Established Career Fellowship in developing control theory for next generation of control systems to enable high level of automation such as robotics and autonomous systems.


Speech Title: Autonomous Olfactory Robots for Environment Monitoring

Abstract: This talk presents recent work in searching and targeting unknown sources of airborne chemical and biological substance release using a ground mobile robot or an unmanned aerial vehicle. Hazard substance release in atmosphere is of major concerns in environment monitoring, maintenance, disaster and emergence management. The whole olfactory system consists of chemical sensors, mobile sensor platforms, reasoning and planning algorithms. By utilising the current and previous chemical sensor readings, reasoning algorithms developed in a Bayesian framework estimate key parameters associated with the release and environment conditions. Based on that, at each step, the decision for the next move of the sensor platform is optimised in order to maximise the chance of finding the source and reduce uncertainty in location estimation. Driven by the inference algorithm and informative based planning and control, the sensor platform is able to approach unknown sources under an unknown environment condition without a specified goal location and driving path. Particularly a dual control for exploration and exploitation is proposed, which exhibits exceptional performance. The Bayesian inference algorithms are implemented through the particle filtering technique. Experimental tests of the complete system were successfully conducted, which overcome the challenges of intermittent sensor readings due to air turbulent conditions, unknown release including location and release rate, unknown environment conditions (e.g. wind direction and speed) and a high level of noise to signal ratio in chemical sensors. The developed autonomous olfactory systems could be widely used in environment protection and monitoring, oil and gas industry, and disaster or emergency management, keeping the first responders out of harm.


Prof. Bing Li, Harbin Institute of Technology, China

Dr Bing Li is a professor of Harbin Institute of Technology Shenzhen, he got a Ph. D. degree from the Hong Kong Polytechnic University, Currently serve as executive dean of school of mechatronics engineering and automation, Harbin Institute of Technology Shenzhen. Research interests focus on mechanisms and robotics. He served as vice president of intelligent vehicle and robot branch of China Instrument Society, member of Production Engineering Branch of China Mechanical Engineering Society, associate editors of the journal of "IMechE Part C: Journal of Mechanical Engineering Science, Intelligent Service Robotics, and editorial board member of Industrial Robot. The research outputs have achieved the second prize of national technological invention award, two provincial science and technology awards, and one second prize of natural science award of Shenzhen.


Speech Title: System Design and Key Technologies of Amphibious Jumping Robot

Abstract: Jumping is an effective way to move across obstacles several times larger than themselves. Jumping ability can improve the maneuverability and adaptability of a robot in complex environments. In recent decades, the study of jumping robot has experienced rapid progress, most of which were launched from the firm ground. Existing terrestrial jumping robots combine the elastic components (e.g. springs or torsion springs) and locking release mechanisms to achieve intermittent or continuous jumping actions. However, the terrestrial jumping mechanisms are typically not suitable for the amphibious environments. The amphibious jumping robots are capable of jumping from the ground and from the water at the same time. Thus, they can perform in the environments that are more challenging, such as the oceans, muds, beaches, grasslands, etc. To escape from the water surface tension, some aquatic jumping robots preferred the method of fast water surface pressing with super-hydrophobic pads, and some others preferred the underwater pre-acceleration. The shortcomings of both methods are apparent, which includes the weak load capacity, limited jumping height, and complex control processes. Meanwhile, there have been several successful cases of water jet propulsion in aquatic jumping scenarios, which has gained much more attention in recent years. By using the custom designed water tanks, the water jet thruster are likely to achieve high performance jumps in both aquatic and terrestrial environments. Therefore, our study aims to develop an amphibious jumping robot with high load and efficiency powered by water jet thruster. The system design, and iterative optimization were adopted to realize the jumping robot. Firstly, a high-pressure water jetting scheme was proposed to address the problems of poor load capacity, and low energy density of existing terrestrial and aquatic jumping mechanisms. Secondly, to ensure the landing safety, the aerial attitude control strategy of the jumping robot was proposed and experimentally verified. Meanwhile, based on the different types of obstacles, an improved whale optimization algorithm was adopted to improve the robot's obstacle crossing performance by altering the take-off parameters. Combining the water jet thruster and the auxiliary modules, an amphibious jumping robot that capable of intermitted consecutive jumps was developed. The outdoor experiments demonstrated that the maximum jumping heights of the robot were 1.46 m in the water and 1.95 m on the ground, respectively, with a load/mass ratio of ~2. Meanwhile, the residual errors of aerial attitude control were as small as 2.79° in pitch and 3.56° in roll, respectively. Thus, our proposed amphibious jumping robot revealed high jumping performance and excellent aerial attitude stability. Based on the preceding research achievements, future works on the high-mobility amphibious robots or aquatic-terrestrial-aerial multi-role robots can be developed, and provide solid insights for the study of trans-medium fluid dynamics.


Prof. Zheng Hong Zhu, York University, Canada

Dr. Zheng H. (George) Zhu received B.Eng. (1983), M.Eng. (1986), and Ph.D. (1989) degrees in Engineering Mechanics from Shanghai Jiao Tong University in China. He also received his M.A.Sc. degree (1998) in Robot Control from the University of Waterloo and Ph.D. degree (2004) in Mechanical Engineering from the University of Toronto in Canada. He is currently a Professor and Tier I York Research Chair in Space Technology with the Department of Mechanical Engineering at York University in Toronto, Canada. Before joining York University in 2006, he worked as a senior stress/structural engineer in Curtiss-Wright Indal Technologies in Mississauga, Canada. From 2019-2022, he served as the inaugural Academic Director of Research Commons at the Vice-President Research and Innovation Office. His research interests include dynamics and control of tethered space systems, spacecraft attitude dynamics, computational control, space robotics control, machine learning, and space debris removal. He has authored and co-authored more than 340 articles. Dr. Zhu is the Principal Investigator of two CubeSat missions for deorbiting space debris for sustainable use of space and measuring the environmental impact of permafrost thawing in Northern Canada. Dr. Zhu is an elected Member of the International Academy of Astronautics, College Member of the Royal Society of Canada, Fellow of the Canadian Academy of Engineering, Fellow of the Engineering Institute of Canada, Fellow of the Canadian Society for Mechanical Engineering, Fellow of the American Society of Mechanical Engineers, Academician of International Academy of Astronautics, Associate Fellow of American Institute of Aeronautics and Astronautics. He is the recipient of the 2021 York President’s Research Excellence award, the 2021 Robert W. Angus Medal by the Canadian Society for Mechanical Engineering, the 2019 PEO Engineering Medal in R&D by Professional Engineer Ontario, the 2013 & 2018 NSERC Discovery Accelerator Supplement awards, and ranked in the Top 2% Most cited Scientists of All Knowledge Fields Combined since 2020 by a Stanford University list.


Speech Title: A Deep Reinforcement-Learning Approach for Autonomous Space Debris Removal by Free-Floating Robotic Manipulators

Abstract: This talk will discuss the application of deep reinforcement learning in path planning and visual servo for autonomous space debris removal by a free-floating space robotic manipulator. A free-floating space robotic manipulator exhibits strong dynamic coupling between the manipulator and its base spacecraft. The motion of the robotic arm disturbs the base spacecraft's position and attitude, and the end effector's pose depends on joint angles and angular velocities. To interact with the environment, an eye-in-hand camera is used for visual servo, which estimates and predicts the target's pose (position and attitude) in 3D space. The estimation is based on either a combination of photogrammetry and an adaptive extended Kalman filter or neural network-based machine learning algorithms. After the pose estimation, a model-free path planning strategy using deep reinforcement learning is then developed for a 6 DOF free-floating space manipulator, using the deep deterministic policy gradient optimization algorithm and the actor-critic network to learn a policy that combines policy gradient and temporal-difference learning via trial and error in a simulation environment. A feedforward neural network with two hidden layers is employed during this process, and a comparison of results using different reward functions and different mass ratios of manipulators over base spacecraft is discussed. Computer simulations are performed to validate the algorithms, and the results show that the end effector can reach its target position with the required orientation, stay in that pose for a longer duration (for later capture), and use minimal joint motion. Additionally, the talk will also discuss the development of a hardware-in-the-loop testbed with active gravity compensation for experimental validation of the reinforcement learning algorithm in the space environment. A reinforcement learning-based algorithm is developed for tactile feedback grasping control for capturing space debris. The challenges in training the reinforcement learning agent in this case will be discussed with computer simulation.

Prof. Haibo Liu, Dalian University of Technology, China

Haibo Liu, is professor and Ph.D. supervisor of school of mechanical engineering at Dalian University of Technology (DUT), China. He received his B.Eng. and Ph.D. degrees in Mechanical and Electrical Engineering from DLUT, in 2006 and 2012, respectively. He is IEEE member, ASME member, senior member of Chinese Society of Mechanical Engineering. He has served as the Guest Editor of the Frontiers in Mechanical Engineering, Frontiers in Materials and China Measurement & Testing Technology, and deputy secretary general of the SAC/TC22 International Standardization Working Committee.
His main research interests include, Measurement-machining integrated manufacturing, On-machine measurement, Phase-change fixturing based adaptive machining, Industrial-robot aided manufacturing. He has published over 80 peer-reviewed SCI/EI journal papers like International Journal of Machine Tools and Manufacture, International Journal of Mechanical Sciences, and IEEE/ASME Transactions on Mechatronic, and over 100 authorized or pending patents. He holds over 20 major projects, including National Natural Science Foundation of China, the sub-project of the Science Challenge Project, the sub-projects of the National Key Research and Development Program and National Science and Technology Major Project of China, etc. He is the recipient of the 1st prize for Liaoning Science and Technology Progress Award (twice), the 1st prize for Science and Technology Progress Award of China Machinery Industry Federation. He was awarded the Young Changjiang Scholars Program of the Ministry of Education in 2022 and the Liaoning Provincial Outstanding Youth Fund Program in 2020.


Speech Title: Ultrasonic non-contact scanning thickness measurement technology and application for large complex thin-walled parts
Abstract: The accurate measurement of the wall thickness distribution of large complex thin-walled parts is a critical process to ensure their manufacturing quality. Integrating ultrasonic sensors into motion equipment enables automatic thickness measurement. However, the large size and complex curvature contours seriously affect the efficiency, accuracy, and adaptability. Hence, this study focuses on suppressing influencing factors, improving measurement accuracy and efficiency, and developing and applying automated thickness measurement equipment. Considering the influence of local curvature and coupling state on the ultrasonic sound field, an automatic identification and error compensation model for ultrasonic incidence angle is established. The adaptive adjustment of ultrasonic coupling gap and non-contact thickness measurement method are invented. Besides, a fast and accurate calculation method for acoustic time difference based on echo matching is proposed. After that, the specialized wall thickness automated measurement equipment and measurement-processing integrated equipment for ultrasonic sensing integration are developed. The automatic measurement of wall thickness of rotary shell, grid wall panel, hemispherical shell scale parts is realized. The maximum measurement error is 0.03mm and the scanning measurement speed is 3m/min. This study systematically solves the problems in the automated ultrasonic thickness measurement process of large complex thin-walled parts, and the effectiveness is verified based on engineering applications.



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