The ForNextCobot project requires analysis of human movement in interaction tasks based on several different sensory information (movement analysis, muscle activity, contact forces), then assessment of dynamic interaction parameters (mechanical impedance) by combining sensory information and created models, as well as generating the desired pattern of the robot-like functioning human through the application of nonlinear optimization methods with limitations introduced by robot construction and learning.
Concept
Expected results
Through the ForNextCobot project, we will try to answer the question in what way human, by reconfiguring the position of the arm and muscle activities, manages the dynamics of interaction of contact tasks so that these patterns can be used to plan the work of a robot that should either replace a human in these tasks or collaborate with him on the same task. Tools that combine mathematical models and sensory measurements that estimate the mechanical impedance of humans and robots will be developed and that will on the one hand allow us to analyze patterns of human behavior, and on the other hand give the ability to control robotic systems in closed coupling. Finally, the result of the project will be algorithms for the realization of the desired mechanical impedance of the robot tool by combining the reconfiguration of the robot and the mechanical impedance of the actuator that performs the movement of the robot.
In healthcare and industry, efficient impedance estimations will be necessary, outperforming models provided by manufacturers, as those might not suit the actual impedance due to exploitation. Then, the industry will be the first to benefit from superior robots that can work safely side-by-side with humans. Besides, methods and knowledge base in human impedance estimation will allow new studies in ergonomy of workplaces, but even more behavior pattern of humans as collaborators of robots which should adjust their behavior for efficient and safe human-robot collaborative tasks. On the other hand, the insight of the robot’s impedance estimation will benefit to the research of human arm’s impedance by yielding the ideas for necessary sensor information. Even more significantly, it will allow healthcare robots to be better rehabilitation partners adjusting impedance to support and follow different level in the rehabilitation process. And on top of the project, the impact on the healthcare, development, and adoption of surgical robots is directly related to robots’ ability to plan, estimate and control the impedance of the end-effector.
