A High Performance Motion Capture System to Support Versatile Research Programs

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Accurate tracking of objects and measurement of their 3D pose (position-orientation) in real-time have numerous applications in industry and academia, e.g., for instruments calibration, and motion analysis. Motion capture or mocap systems have been designed to fulfill such a need. Since their introduction, mocap systems have been effectively used in a variety of applications in military, entertainment, sports, medicine, engineering, and, particularly, research for verification of concepts and methods. Several reports have shown that mocap systems have had a major role in speeding up the development cycle of complex systems. For instance, in robotics, mocap systems are commonly used for accurate tracking and pose measurement of mobile robots to evaluate and verify pose estimation, SLAM (simultaneous localization and mapping), and control methods. Moreover, the data from mocap systems are often used as ground-truth representing ideal perception modules. Recent market report indicates that global 3D motion capture market will grow from USD 129.8 million in 2018 to USD 252.88 million by 2024 (PR Newswire, Sep 11, 2019). Among mocap systems, optical systems have become popular due to their advantages, e.g., over inertial systems (that introduce drift and have lower positional accuracy), mechanical systems (which need wearing expensive mechanical suits), and magnetic systems (that are susceptible to electromagnetic interference from metal objects in the environment). ******In this proposal, an optical mocap system, namely Vicon Vero is requested to meet the needs of versatile research programs. The equipment will be installed in a zone accessible to other researchers across the campus to maximize its use. The research programs of the applicants that will immediately benefit from the requested equipment include projects related to: (1) modeling and control of image-guided continuum robots, with the focus on cardiac interventions; (2) development of a visual learning system for robot programming by demonstration; (3) AI-based methods for object detection, control, and farm-field coverage path planning of an autonomous seeding robot; (4) modeling and control of aerial manipulation systems; (5) design of telerobotic platforms for robotic surgery; and (6) simultaneous localization and mapping techniques for robotic vehicles.******Besides the applicants and collaborators, their postdoctoral fellows, graduate and undergraduate students will be trained to operate state-of-the-art mocap system, and learn advanced measurement, data integration and analysis methods. These skills will be important to their studies and in high-demand by key Canadian industries such as entertainment, manufacturing, and healthcare. In addition to Ryerson University, this new equipment will expand research capacity of industrial partners, iBest (Institute for Biomedical Engineering, Science, and Technology) and partner hospitals. ********