We are developing novel energy-efficient designs and model-based control algorithms for lower-limb and upper-limb exoskeletons and prostheses using integrated biomechatronic systems modelling. Automated environment recognition systems and onboard sensors inform the dynamic controllers, which optimize assistive loads and electrical energy regeneration capabilities of these wearable biomechatronic devices for rehabilitation and manufacturing applications.
We are developing machine learning algorithms to automatically track, model, and control multibody dynamic systems. Our efficient algorithms are deployed on mobile devices and control hardware units. Applications include markerless tracking for human movement biomechanics, control of autonomous vehicles, automotive powertrain models, and environment recognition systems for robotic lower-limb exoskeletons and prostheses.
We are developing model-predictive control (MPC) algorithms to integrate planning and motion control of autonomous vehicle systems. These algorithms are deployed using robot operating system (ROS) on the University of Waterloo “Autonomoose”. Vehicle and tire models are developed from track testing using our intelligent vehicle measurement system, and the controllers are evaluated in virtual simulations and hardware-in-loop experiments prior to vehicle testing.
We are developing physics-based models of different automotive components and subsystems, including tires, suspensions, engines, catalytic converters, batteries, drivebelts, and torque converters. Model reduction approaches are used to create “control-oriented” models for model-predictive controllers. For hybrid electric and fuel-cell powertrain systems, we are using machine learning algorithms to develop neural network models from experimental CAN measurements during vehicle testing on our outdoor track or $10M Green and Intelligent Automotive (GAIA) Research Facility.
Alongside human neuromusculoskeletal models, we have developed dynamic models and optimal controllers for upper-limb stroke rehabilitation robotic systems, which provide assistive/resistive loads according to patient-specific needs. We are now gamifying the robotic system to enhance patient experience and are using artificial intelligence to learn patient-specific characteristics. The stroke rehabilitation robotic system is currently being tested at Grand River Hospital.
We have developed high-fidelity multibody dynamic models of golfer biomechanics, flexible shafts, ball-flight aerodynamics, and clubhead-ball contact dynamics. Optimization methods are used to systematically evaluate the effects of swing and equipment changes, and are conducted in collaboration with golf club companies and professional regulatory agencies. Model validation is provided by optical and inertial motion capture systems, our AboutGolf simulator, pressurized air cannon, and high-speed video camera (up to 600,000 fps).
We work alongside Team Canada Olympic and Paralympic athletes to enhance their sport performance and equipment design. Predictive biomechanical models and optimal controllers were recently used to optimize the standing start biomechanics for Team Canada track cyclists. For Canada’s Wheelchair Curling Team, we have designed and prototyped a 3D-printed novel curling end-effector that provides better movement control while delivering the curling stone. Predictive biomechanical models and optimal controllers of Team Canada Wheelchair Basketball players were used to analytically determine optimal seat positions that maximized forward propulsion.
We are developing predictive dynamic simulations of human legged locomotion to better understand human motor control and balance. Our “what-if” predictive simulations require high-fidelity mathematical models of foot-ground physical contact and can facilitate virtual exploration and optimization of assistive device design parameters and surgical interventions, without requiring time-consuming and expensive trial-and-error human experiments.
In collaboration with Intellijoint Surgical, we are developing multibody dynamic models and optimal controllers to predict human movement biomechanics before and after orthopaedic surgery, specifically hip and knee joint replacement arthroplasty. Our primary objective is to inform orthopaedic surgeons of optimal hip and knee implant positionings to minimize the risk of joint dislocation and discomfort.