Matthew Pan

Ph.D.

Ph.D.

Project Title: Collaborative, Human-focused, Assistive Robotics for Manufacturing (CHARM)

Funding:  NSERC PGS-D, UBC 4-Year Fellowship

Principal Investigator: Dr. Elizabeth Croft

Academic Collaborating Organizations: Computer Vision and Systems Laboratory of the Université Laval,  Laboratoire de robotique de l’Universite Laval,  Artificial Perception Laboratory at the McGill Center for Intelligent Machines

Industrial Collaborating Organization: GM Research and Development Center

Project Description: 

Current industrial robots lack the abilities (dexterity, complex sensing and cognitive processes) possessed by skilled workers need to perform many manufacturing tasks such as product assembly, inspection and packaging. For example, in the automotive industry, robots are used to perform tasks that are entirely repeatable and require little or no human intervention, such as painting, welding and pick-and-place operations. Such robots work in confined spaces isolated from human workers, as improper interactions could result in severe injury or death. Since robots have optimized production efficiency under these conditions however, industries are now directing efforts to achieve similar improvements in worker efficiency through the development of safe, robotic assistants that are able to co-operate with workers.

The research project proposed in this document seeks to exploit this emerging paradigm-shift for manufacturing systems. It is in this context that I propose to develop robot controllers and intuitive interaction strategies to facilitate cooperation between intelligent robotic assistants and non-expert human workers. It is expected that this work will focus on developing motion control models for interactions between different participants which involve safe contact, sharing and hand-off of common payloads. These control systems will allow intelligent robots to co-operate with non-expert workers safely, intuitively and effectively within a shared workspace.

To attain this goal, I intend to draw on elements of safe, collaborative human-robot interaction (HRI) explored through previously-conducted research [2, 3] to develop a preliminary motion control framework. Much of the hardware, communication algorithms and generalized interaction strategies necessary for designing this HRI already exist. However, a wide range of technological advancements necessary to support specific task-driven HRI such as real-time gesture recognition, interaction role negotiation and robust safety systems [1] must still be developed. Thus, studies investigating typical human-human collaborative interaction methods will be used to supplement this work. Specific focus will be given to examining how humans use non-verbal communication to negotiate leading and following roles. Several basic gestures and behaviors will be studied including: co-operative lifting, hand-offs and trajectory control of objects. I aim to leverage these findings by developing a library of motion control strategies for mobile manipulator-type robots which are safe, ergonomic, and allow for the efficient use of the worker and robotic assistant’s skills and abilities.

The control models constructed from these methods will be applied in the context of a specific use case representative of a typical production operation. The use case will consist of non-value added activities within an automotive manufacturing process having component tasks deemed to be complex and diverse. Motion control strategies will be evaluated and refined on a robot platform through human participant studies involving component tasks typical of those seen in the use case. These control strategies will be assessed both subjectively as they relate to the user (e.g., intuitiveness, perceived robot intelligence, ease of use) and objectively through performance measures (e.g., time trials).

The significance of this research lies in the advancement of HRI and the development and deployment of a new class of industrial robots intended to work alongside human counterparts beyond the laboratory. Novel forms of admittance control will be developed with the explicit intention of driving HRI, cooperation and shared object handling. This work is expected to produce useful data and methods contributing to the development and application of safe, collaborative HRI and human-in-the-loop control systems. Although this research is directed towards applications in manufacturing, the knowledge acquired will be extendable to HRI in other domains including rehabilitation, homecare and early child development.

[1] Breazeal, C. et al. “Effects of Nonverbal Communication on Efficiency and Robustness in Human-Robot Teamwork”, IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 383-388, 2005.

[2] Fischer, K., Muller, J.P., Pischel, M., “Unifying Control in a Layered Agent Architecture,” Int. Joint Conf. on AI (IJCAI’95), Agent Theory, Architecture and Language Workshop, 1995.

[3] Moon, A., Panton, B., Van der Loos, H.F.M., Croft, E.A., “Safe and Ethical Human-Robot Interaction Using Hesitation Gestures,” IEEE Conf. on Robotics and Automation., pp. 2, May 2010.