A Decentralized Approach to Human-Robot Object Transportation Through the Control of a Catenary Cable

An important challenge for human-robot interaction is for both the human and the robot to agree on a model of motion. However, most applications require machine learning and heavy communication for the robot to be an active member in executing a joint task and adapt to human behavior. In this work, we develop a decentralized approach to control and track a catenary cable for object transportation in a human robot interaction scope. Our system is composed of a linked chain that is attached to a quadrotor on one end, and the human on the other. The chain is defined as a catenary curve with five degrees of freedom, and Motion Capture technology is used to track the components of our system. Given the human’s position, we use shape estimation of the curve to determine the drone’s position and control the trajectory of the chain and thus the load attached to it. We then proceed to implement a swing load controller that minimizes the oscillations of the load created by the chain’s movement.