The program includes invited talks, contributed papers, and demos. The details for each talk are below the program.
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08:00 – 8:50 Registration + Breakfast
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08:00 – 08:30 CARRE: Jonathan Kelly and Hugh Liu, University of Toronto, Overview
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08:50 – 9:00 Welcome and Opening Remarks
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08:35 – 09:30 Plenary 5: Dimitra Panagou, University of Michigan, Adversarially-Robust Aerial Swarms
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09:05 – 10:00 Plenary 1: Anibal Ollero, University of Seville, Aerial robots with manipulation capabilities in flight and perched
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09:35 – 10:30 Plenary 6: Tim McLain, Brigham Young University, We’ve lost GPS! Now what? Navigation of small unmanned aircraft in environments where GPS is intermittent, degraded, or unavailable
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10:05 – 11:00 Plenary 2: Nora Ayanian, University of Southern California, Downwash-aware trajectory planning for large teams of robots
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10:30 – 11:00 coffee break
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11:05 – 11:30 Invited Talk 1
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11:05 – 12:00 Plenary 7: Stephan Weiss, Alpen Adria University, Dense Information for State Estimation on Computationally Constrained Platforms
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and Lunch with Poster Session
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12:05 – 13:00 Plenary 8: Giuseppe Loianno, New York University, Autonomous Agile Human Friendly Drones
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13:05 – 14:00 Plenary 3: Shaojie Shen, HKUST, Minimalist Visual Perception and Navigation for Consumer Drones
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13:00 – 15:00 Lunch + Posters + Closing Remarks
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14:05 – 15:00 Plenary 4: Koushil Sreenath, UC Berkeley, Safety-Critical Control for Dynamic Aerial Robotics
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15:00 – 15:30 coffee break
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15:35 – 16:00 Invited talk 2: Ming Hou, Defense Research and Development Canada, Interaction-Centred Design for Safe Operation of Aerial Robots
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16:05 – 16:30 Invited talk 3: Thomas Lee, Quanser, Abstractions and Platforms for Contemporary Research and Education in Aerial Robotics
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- C. Yao, N. Dunkelberg and K. Janschek, Trajectory Generation and Tracking of a Hexarotor with Fixed Tilted Rotors Considering Bounded Rotor Velocity
- S. Zhou, Transferring Deep Inverse Models Across Robots for High-Accuracy Impromptu Tracking
- S. Hosseini Semnani, A. de Ruiter, and H. Liu, Motion Planning of Large Drone Teams
- S. Fang and S. O’Young, Implementation of ADS-B Based Target Tracking for Small Unmanned Aerial Vehicles
- C. Luis, M. Vukosavljev, and Angela Schoellig, Towards Scalable Online Trajectory Generation for Multi-robot Systems
- M. Vukosavljev, J. Scherer, and A. Schoellig, Motion Planning of Networked UAVs for Monitoring Tasks
- M. Warren, M. Greeff, B. Patel, J. Collier, A. P. Schoellig, and T. D. Barfoot, There’s No Place Like Home: Visual Teach and Repeat for Emergency Return of Multirotor UAVs during GPS Failure
- J. Liao and H.T. Liu, Reinforcement Learning for Multi-UAV Cooperative Hunting
Speakers abstract and info
Title: Aerial robots with manipulation capabilities in flight and perched
Abstract: The presentation will be devoted to new methods and technologies for aerial robotic manipulators.
In the first part aerial robotic manipulation in free flight will be analysed and methods for manipulation with dual arms to increase the dexterity will be presented. Compliance plays a significant role to increase the safety of the aerial manipulator. The development of perception and planning capabilities will be presented. Perception will include on-board mapping, localization and visual servoing. Planning will also involve real time implementation with dynamic awareness to take into account the motion of the arms in environments with many obstacles.
The presentation will also include aerial robots with dual arms attached with one hand to a fixed contact while the manipulation is performed with the other hand. In addition, hybrid robots that are able to fly, perch and roll in very constrained surfaces, such as pipes, to reach the manipulation location will be presented.
In the talk I will also present new recent inspection and maintenance applications of industrial plants, and infrastructures. Particularly, I will present recent industrial results of the AEROARMS H2020 https://aeroarms-project.eu/ project obtained in a cement kiln and in a refinery, including mapping and the contact inspection to obtain the thickness of the wall in pipes and tanks of a refinery .
Finally, I will briefly introduce a new generation of bio-inspired aerial robots capable to fly, combining gliding and flapping wings, perch in cables and other constrained surfaces using claws with variable compliance, fold the wings and manipulate with very light arms. This new generation is being developed in the GRIFFIN (General compliant aerial Robotic manipulation system Integrating Fixed and Flapping wings to INcrease range and safety) Advanced Grant of the European Research Council.
Bio: Anibal Ollero is full Professor Head of GRVC at University Seville, and Scientific Advisor of the Center for Aerospace Technologies (CATEC) also in Seville. He has been full professor at the Universities of Santiago in the Vigo Campus and Malaga (Spain). In all these institutions he has funded research groups and laboratories. He has been also researcher at the Robotics Institute of Carnegie Mellon University (Pittsburgh, USA) and LAAS-CNRS (Toulouse, France).
He authored more than 700 publications, including 9 books and 180 papers in journals of the citation index and has been editor of 13 books. He has been supervisor or co-supervisor of 40 PhD Thesis that have received many awards. He lead more than 160 research projects, participating in more than 22 projects of the European Research Programmes being coordinator of 7 and associated or deputy coordinator of 3. In these projects he developed several world-wide innovations, particularly in aerial robotics and unmanned aerial vehicles, such as the aerial robotic manipulation, the fully autonomous landing on mobile platforms, the transportation of a load by means of several autonomous helicopters, and the cooperation of multiple unmanned aerial systems for detection and monitoring applications.
He is currently the coordinator of the H2020-AEROARMS project with the participation of 10 universities, research centers and companies. In this project he has developed the first aerial robotic manipulators with multiple arms for applications involving physical interactions with the environment and particularly for contact inspection and maintenance in inaccessible sites. From November 2018 he is running the GRIFFIN ERC-Advanced Grant with the objective of developing a new generation of aerial robots that will be able to glide, flapping the wings, perch and manipulate by maintaining the equilibrium.
He has transferred technologies to 20 companies and has been awarded with 20 international research and innovation awards, including the recent Overall Information and Communication Technologies Innovation Radar Prize 2017 of the European Commission, and has been also elected between the three European innovators of the year being candidate to the European personalities of the year.
He has delivered plenaries and keynotes in more than 100 events including IEEE ICRA 2016 and IEEE IROS 2018.
He is IEEE Fellow “for contributions to the development and deployment of aerial robots”. Currently co-chair of the “IEEE Technical Committee on Aerial Robotics and Unmanned Aerial Vehicles”, coordinator of the “Aerial Robotics Topic Group” de euRobotics and member of the “Board of Directors” of euRobotics. He has been also founder and president of the Spanish Society for the Research and Development in Robotics (SEIDROB) until November 2017 and has been IFAC Vice-chair of the Technical Board, Coordinating Committee Chair and Technical Committee Chair.
Title: Downwash-aware trajectory planning for large teams of robots
Bio: Nora Ayanian is an Assistant Professor of Computer Science and Andrew and Erna Viterbi Early Career Chair at the University of Southern California. Her research focuses on creating end-to-end solutions for coordinating teams of robots that start from high-level specifications and deliver code for individual robots in the team, such as using simple multitouch inputs to control a team of UAVs. Her approach to multirobot systems creates unified solutions that concurrently address task assignment, path planning, and control, and that are broadly applicable across all aspects of multirobot systems and mobile sensor networks. Ayanian’s work received the best student paper award at ICRA 2008, and recently, best paper in the robotics track at ICAPS 2016. In 2013 she was named one of IEEE Intelligent Systems “AI’s 10 to watch” and in 2016, she was recognized as a visionary in MIT Technology Review’s 35 Innovators Under 35 (TR35). Ayanian is a co-founder and current co-chair of the IEEE Robotics and Automation Society Technical Committee on Multi-Robot Systems.
Title: Safety-Critical Control for Dynamic Aerial Robotics
Abstract: Aerial robots are subject to range, power, communication, and collision constraints. These constraints need to be strictly enforced for safe operation. Such constraints are dynamic constraints and require the state of the system to be forward-invariant with respect to a safe set. In this talk, I will present a controller that uses control Lyapunov functions to enforce stability and control barrier functions to enforce safety-critical constraints. I will show how these techniques can be extended to safety-critical constraints with high relative-degree as well as safety-critical constraints on geometric manifolds. I will also show how these safety-critical constrains can be enforced in a robust manner for systems with dynamic uncertainties. Finally, I will present how the safety-critical controller can be used to smoothly blend manual teleoperative and autonomous control inputs.
Title: Adversarially-Robust Aerial Swarms
Abstract: Planning, decision-making and control for uncertain multi-agent systems has been a popular topic of research with numerous applications, e.g., in robotic networks operating in dynamic, unknown, or even adversarial environments. Despite significant progress over the years, issues such as constraints (in terms of state and time specifications), malicious or faulty information, environmental uncertainty and scalability remain open challenges. In this talk, I will present some of our recent results and ongoing work on safety and resilience of multi-agent systems in the presence of adversaries. I will discuss (i) our approach on achieving safe, resilient consensus in the presence of malicious information and its application to resilient leader-follower aerial teams under bounded inputs, and (ii) our method on safe multi-UAV path planning and de-confliction using finite-time controllers and estimators in the presence of bounded uncertainty.
Bio: Dimitra Panagou received the Diploma and PhD degrees in Mechanical Engineering from the National Technical University of Athens, Greece, in 2006 and 2012, respectively. Since September 2014 she has been an Assistant Professor with the Department of Aerospace Engineering, University of Michigan. Prior to joining the University of Michigan, she was a postdoctoral research associate with the Coordinated Science Laboratory, University of Illinois, Urbana-Champaign (2012-2014), a visiting research scholar with the GRASP Lab, University of Pennsylvania (June 2013, fall 2010) and a visiting research scholar with the University of Delaware, Mechanical Engineering Department (spring 2009).
Dr. Panagou’s research program emphasizes in the exploration, development, and implementation of control and estimation methods in order to address real-world problems via provably correct solutions. Her research spans the areas of nonlinear systems and control; control of multi-agent systems and networks; distributed systems and control; motion and path planning; switched and hybrid systems; constrained decision-making and control; navigation, guidance, and control of aerospace vehicles. She is particularly interested in the development of provably correct methods for the robustly safe and secure (resilient) operation of autonomous systems in complex missions, with applications in unmanned aerial systems, robot/sensor networks and multi-vehicle systems (ground, marine, aerial, space). Dr. Panagou is a recipient of a NASA Early Career Faculty Award, of an AFOSR Young Investigator Award, and a member of the IEEE and the AIAA.
More details: http://www-personal.umich.edu/~dpanagou/research/index.html
Title: Autonomous Agile Human Friendly Drones
Abstract: Drones are starting to play a major role in several tasks such as search and rescue, interaction with the environment, inspection, patrolling and monitoring. Agile navigation of Micro Aerial Vehicles (MAVs) through unknown environments poses a number of challenges in terms of perception, state estimation, planning, and control. To achieve this, MAVs have to localize themselves and coordinate between each other in unstructured environments. In this talk, I will present some recent research results on high speed and agile flight maneuvers for navigation, transportation, physical environment interaction, and human drone collaboration using a minimal on-board sensor suite composed by a single camera system and IMU. Finally, I will also present some recent achievements that can improve the autonomy of micro and nano platforms recovering the robot’s state just using inertial data and optionally the information from vision sensors.
Bio: Prof. Giuseppe Loianno is an assistant professor at the New York University and director of the Agile Robotics and Perception Lab (https://wp.nyu.edu/arpl/) working on autonomous Micro Aerial Vehicles. Prior to NYU he was a lecturer, research scientist, and team leader at the General Robotics, Automation, Sensing and Perception (GRASP) Laboratory at the University of Pennsylvania. He received his BSc and MSc degrees in automation engineering, both with honors, from the University of Naples “Federico II” in December 2007 and February 2010, respectively. He received his PhD in computer and control engineering focusing in robotics in May 2014 in the PRISMA Lab group. Dr. Loianno has published more than 50 conference papers, journal papers, and book chapters. His research interests include visual odometry, sensor fusion, and visual servoing for micro aerial vehicles. He is worldwide recognized for his expertise in autonomy for agile small scale aircrafts. He received the Conference Editorial Board Best Reviewer Award at ICRA 2016, National Italian American Foundation (NIAF) Young Investigator Award 2018. He is the program chair of IEEE SSRR 2019. He has organized multiple workshops on Micro Aerial Vehicles during IROS conferences and created the new International Symposium on Aerial Robotics (ISAR). His work has been featured in a large number of renowned international news and magazines.
Title: Minimalist Visual Perception and Navigation for Consumer Drones
Bio: Shaojie Shen received B.Eng. degree in Electronic Engineering from the Hong Kong University of Science and Technology (HKUST) in 2009. He received M.S. in Robotics and Ph.D. in Electrical and Systems Engineering in 2011 and 2014, respectively, all from the University of Pennsylvania. He joined the Department of Electronic and Computer Engineering at the HKUST in September 2014 as an Assistant Professor. He is the founding director of the HKUST-DJI Joint Innovation Laboratory (HDJI Lab). His research interests are in the areas of robotics and unmanned aerial vehicles, with focus on state estimation, sensor fusion, localization and mapping, and autonomous navigation in complex environments. He is currently serving as associate editors for T-RO and AURO. His recent work, VINS-Mono, received the Honorable Mention status for the 2018 T-RO Best Paper Award. He and his research team also won the Best Student Paper Award in IROS 2018, Best Service Robot Paper Finalist in ICRA 2017, Best Paper Finalist in ICRA 2011, and Best Paper Awards in SSRR 2016 and SSRR 2015.
Abstract: Consumer drone developers often face the challenge of achieving safe autonomous navigation under very tight size, weight, power, and cost constraints. In this talk, I will present our recent results towards a minimalist, but complete perception and navigation solution utilizing only a low-cost monocular visual-inertial sensor suite. I will start with an introduction of VINS-Mono, a robust state estimation solution packed with multiple features for easy deployment, such as online spatial and temporal inter-sensor calibration, loop closure, and map reuse. I will then describe efficient monocular dense mapping solutions utilizing efficient map representation, parallel computing, and deep learning techniques for real-time reconstruction of the environment. The perception system is completed by a geometric-based method for estimating full 6-DoF poses of arbitrary rigid dynamic objects using only one camera. With this real-time perception capability, trajectory planning and replanning methods with optimal time allocation are proposed to close the perception-action loop. The performance of the overall system is demonstrated via autonomous navigation in unknown complex environments, as well as aggressive drone racing in a teach-and-repeat setting.
Title: Dense Information for State Estimation on Computationally Constrained Platforms
Bio: Stephan Weiss is Full Professor of Robotics and head of the Control of Networked Systems Group at the Universität Klagenfurt(AAU) in Austria. He received both his MSc in Electrical Engineering and Information Technology in 2008 and his Ph.D. in 2012 from the Eidgenössische Technische Hochschule (ETH) Zurich, Switzerland. His Ph.D. Thesis on “Vision Based Navigation for Micro Helicopters” first enabled GPS independent navigation of small UAVs using on-board visual-inertial state estimation. His algorithms were the key to enable the Mars Helicopter Scout project and corresponding proof-of-concept technology demonstration at NASA’s Jet Propulsion Laboratory where he worked from 2012 until 2015 as Research Technologist in the Mobility and Robotic Systems Section and where he lectured at the California Institute of Technology.
Abstract: Computational constraints prevent processing vast amount of information for accurate navigation of mobile platforms. Only recently, hardware advances and novel algorithm development mitigated this issue to some extent and enabled usage of imagery for autonomous navigation tasks. However, today’s methods still distill the vast information of a camera to highly reduced and compressed image features or contrast rich areas only leveraging a fraction of the information this sensor could provide. In this talk, we focus on an approach to use the full image information for motion estimation in near-real time on a CPU. When using every single pixel of a camera for state estimation, the dependency on high-contrast areas is highly reduced such that vision-based navigation in low-contrast areas with smooth gradients becomes possible. Also, by including the depth estimation per pixel tightly in the estimator, map uncertainties are no longer decoupled and approximated but consistently and inherently integrated in the estimation process. As vision based approaches have natural failure modes, we will further extend the notion of dense information to a multitude of sensor modalities and discuss scalability and observability aspects maintaining self-calibrating properties of such multi-sensor fusion systems.
Title: We’ve lost GPS! Now what?
Navigation of small unmanned aircraft in environments where GPS is intermittent, degraded, or unavailable
Bio: Tim McLain is a professor of mechanical engineering at Brigham Young University, where he has also served as department chair. He received BS and MS degrees in from BYU and a PhD from Stanford University, all in mechanical engineering. He joined BYU as a professor in 1995. During 1999 and 2000, he was a visiting scientist at the Air Force Research Laboratory where he initiated research in the guidance and control of unmanned aircraft systems. Since then, his UAS research has attracted the support of the Air Force, the Army, DARPA, NASA, NSF, ONR, NIST, and numerous companies. With Professor Randy Beard and students, he helped found Procerus Technologies that produces UAS autopilot, sensing, and guidance technology. Lockheed Martin acquired Procerus in 2012. He is a co-author of the textbook Small Unmanned Aircraft published by Princeton University Press. He is the author of over 140 peer-reviewed articles with over 8600 citations of his work. Since 2012, he has been the director of the Center for Unmanned Aircraft Systems sponsored by the National Science Foundation that currently involves four universities and over 30 industry partners.
Abstract: Increases in accuracy of the global positioning system (GPS) and significant reductions in size and cost of GPS receivers allowed GPS to play a significant role in the emergence of small unmanned aircraft systems (sUAS) as a viable and useful technology. GPS is used to provide global position and velocity information required for navigation of sUAS, but it also plays a critical role in bounding the drift in attitude estimates produced by today’s modern micro-electro-mechanical systems (MEMS)-based inertial measurement units. Simply stated, GPS is an essential component for virtually all modern sUAS. While GPS is highly reliable and widely used, there are numerous conditions that result in degraded or intermittent GPS measurements. Some of these conditions include jamming or spoofing of GPS signals in military settings, multipath and occlusion of GPS signals in urban environments or densely forested areas, and obstruction of GPS signals indoors or beneath structures. To ensure safe and reliable operation of unmanned aircraft in their increasing range of applications, they must be robust to loss of GPS and be able to transition seamlessly between various levels and types of GPS degradation. This presentation will provide an overview of the GPS-denied navigation problem and discuss ongoing research in the area. Our approach to this challenging problem, called relative navigation, will be introduced and its application to several scenarios involving both multi-rotor and fixed-wing aircraft will be discussed. In the front-end of the relative navigation architecture, onboard sensors, such as cameras, depth sensors, and lidar, are used in place of, or in tandem with, GPS to produce position and attitude estimates relative to the surrounding environment. This relative front-end odometry information can be optimized in the back-end portion of the relative navigation architecture to generate and optimize a global map of the aircraft’s path. The salient features of the relative navigation approach, including its inherent observability and consistency properties, will be highlighted. Recent flight-test results demonstrating the effectiveness of the relative navigation approach will be presented and discussed.
Abstract: The Centre for Aerial Robotics Research and Education (CARRE) is a University of Toronto initiative to unify research and teaching activities with the goal of transforming the nascent field of unmanned aerial systems. CARRE is an interdisciplinary and cross-disciplinary Centre based within our Faculty of Applied Science & Engineering, with partners at several other institutions. The Centre has developed an industrial training and collaboration program and also directs key outreach and educational initiatives. For students, CARRE offers a Certificate of Emphasis in Aerial Robotics and opportunities for internships with industrial partners; for academic and industry members, CARRE manages collaborative research projects and access to early research results; for the public, CARRE serves as a coordinator for youth and indigenous community programs. CARRE has established itself as a centre of excellence in Canada. The Centre is open to students, commercial, not-for-profit, and government organizations in Canada. For more information, visit http://www.carre.utoronto.ca. In this brief presentation, we will provide the research profile of CARRE academic members and update of recent projects.
Bio: Hugh H.T. Liu is a full Professor of the University of Toronto Institute for Aerospace Studies, where he has been on the faculty since 2000. He currently also serves as the Director of Natural Science and Engineering Research Council of Canada (NSERC) Collaborative Research and Training Experience (CREATE) Program on Unmanned Aerial Vehicles and Centre for Aerial Robotics Research and Education. He received his Bachelor’s degree from Shanghai Jiao Tong University (1991), Master’s degree from Beijing University of Aeronautics and Astronautics (1994), and Ph.D. from the University of Toronto (1998). His research interests in the area of aircraft systems and control include autonomous unmanned systems, cooperative and formation control, fault tolerant control, active control on advanced aircraft systems, as well as integrated modeling and simulations. Prior to his academic appointment, Dr. Liu was a systems engineer at AlliedSignal (now Honeywell) Aerospace Canada where he worked on various aircraft systems projects. He has served many years as a member of the AIAA Guidance, Navigation, and Control Technical Committee. He currently serves as the Associate Editor of AIAA Journal of Guidance, Control and Dynamics. He is also an Associate Editor of the Canadian Aeronautics and Space Journal. Dr. Liu is a fellow of Engineering Institute of Canada, an Associate Fellow of AIAA, a Fellow of Canadian Society of Mechanical Engineers, and a registered Professional Engineer in Ontario, Canada.
Abstract: Recent accidents to the Boing 737 Max passengers ring the alarm again about the important needs of appropriate design concepts and methodologies for developing safety critical Human-Machine Systems (HMSs) and effective partnership of Human and these intelligent aerial robots. It is not only about the design of these systems, training, airworthiness, and certification, etc., but also about a scientific and systematic approach when we design, develop, verify, validate, and regulate these systems. This includes but certainly is not limited to both manned and unmanned systems which have certain levels of autonomy as well as artificial/machine intelligence. It is not only about the safety of those systems, but more importantly human lives, especially when facing uncertain human factors in uncertain environments. This talk will discuss about the needs for HMS designers, developers, project manager, researchers, and all practitioners who are interested in building and using 21st century human-computer symbiosis technologies (why). The talk will also cover analytical methodologies for functional requirements of the intelligent HMSs, design methodologies, implementation strategies, and evaluation approaches, etc (what and how). These aspects will be explained in detail with real-world examples when considering constraints of technology, human capability and limitations, and functionalities that the HMS should achieve (When). Audience will gain insights of interaction-centred design approach for developing an ideal partnership between human and technology by optimizing the interaction between human intelligence and artificial/machine intelligence. The challenges and potential issues will also be discussed for guiding future R&D activities in this area.
Bio: Dr. Ming Hou obtained his PhD in Human Factors at the University of Toronto in 2002. Currently he is a Senior Defence Scientist at DRDC and the Principal Authority of Human-Technology Interactions within the Department of National Defence, Canada. He is responsible for providing science-based advice at national and international levels to the Canadian Armed Forces about the investment in and application of advanced technologies for human-machine systems requirements. Dr. Hou is an Integrator for the Canadian government $1.6 billion IDEaS program and one of the three Scientific Advisors to the Canadian National Centre of Expertise in Human Systems Performance with responsibilities of guiding R&D activities in Automation, Robotics, and Telepresence. As the Canadian National Leader of the Joint Panel on Human Systems Performance – Air under The Technical Cooperation Program (TTCP), Dr. Hou led Canadian collaboration efforts for military air systems, including managing a US$162M international project on Human-Autonomy Teaming. He was the Canadian National Lead and Scientific Authority during the TTCP Autonomous Warrior 2018 Joint Service Exercise. Dr. Hou is the Head of Canadian Delegation to NATO Human Factors Specialist Committee within NATO Joint Capability Group on Unmanned Aircraft Systems (UAS). His monograph “Intelligent Adaptive Systems: An Interaction-Centered Design Perspective” guided the development of NATO Standard Recommendations on “UAS Human Systems Integration Guidebook” and “Sense and Avoid Guidance for UAS”. As one of the four invited Lecturers, Dr. Hou delivers NATO Lecture Series on “UAVs: Technological Challenges, Concepts of Operations, and Regulatory Issues”. Dr. Hou also delivers many keynote and/or plenary speeches at multiple international venues while he serves international associations as a chair and/or a board member.
Abstract: Quanser is well known for a range of popular devices for control algorithm validation with many significant contributions made by its user-community in the aerospace fields. More recently, the company has introduced generalized hardware, software, and methods the increase the efficiency of academic applications (research and teaching) in the autonomous aerial robotics field as well as related application fields including self-driving cars, general ground robotics, manipulator robotics, and increasingly into topics triggered by the global interest in IoT, Industry 4.0, AI/ML and the like. Additionally, Quanser’s work has explored the potential of authentically complex applications from these fields within the undergraduate curriculum and correspondingly, Quanser has been a leading voice in the global discussion on engineering education reform in the face of such modern applications.
Bio: Dr. Lee will provide an overview of the thinking and the technologies that frame their approach, highlighting the refinements and in some cases, fundamental departure from prevailing techniques. Dr. Tom Lee is Chief Education Officer of Quanser in Toronto, Canada, a global company specializing in academic lab platforms for research and teaching in control, advanced mechatronics, advanced robotics, and more. In his position, Dr. Lee directs the application and academic strategies for the company’s technologies. He is also a recognized authority in transformative techniques in modern engineering education. Prior to Quanser, Dr. Lee was part of senior management at the mathematical application and modeling software company Maplesoft of Waterloo, Canada. Dr. Lee earned his Ph.D. in Mechanical Engineering at the University of Waterloo as part of the Control and Automation Group. His Master’s and Bachelors were in Systems Design Engineering at the University of Waterloo. Dr. Lee also has Adjunct faculty appointments at the University of Waterloo, University of New Mexico, and York University.