Control Strategies for Physical Human—Robot Collaboration

Recent industrial interest in producing smaller volumes of products in shorter time frames, in contrast to mass production in previous decades, motivated the introduction of human—robot collaboration (HRC) in industrial settings, to increase flexibility in manufacturing applications. As a consequence, industrial environments would lose their fixed structure, thus increasing the uncertainties present in this workspace shared between humans and robots. This thesis presents robot control methods to mitigate such uncertainties and to improve the involvement of human operators in industrial settings where robots are present, with a particular focus on manual robot guidance, or kinesthetic teaching.

First, the accuracy of manual robot guidance was increased by reducing the joint static friction without altering the robotic task execution, using additional degrees of freedom (DOFs) available in collaborative robots. Additionally, previous methods for a fast identification of the source of robot—environment physical contact in partially-unknown industrial environments were evaluated, extended, and modified to perform effective manual corrections of the robot motion. Then, an iterative learning method was proposed to achieve a more accurate use of manually-defined trajectories, while allowing a safe physical robot—environment interaction.

Moreover, safety is a major concern in uncertain scenarios where humans and robots collaborate. Regulating the robot—environment interaction forces, e.g., using impedance control, would improve safety, yet undesired parts of the collaborative workspace might need to be entirely avoided. To this purpose, a stable online variation of robot impedance during the manual guidance of the robot was proposed. This proposal was later extended to further improve safety by considering a prediction of human guidance with coordinated robot control. Furthermore, the additional DOFs in collaborative robots were used to develop a stable online impedance variation method for robot obstacle avoidance without requiring modification of the main robot task.

All methods presented were tested experimentally on a real collaborative robot.