In a human–robot collaboration scenario, operator safety is the main problem and must be guaranteed under all conditions. Collision avoidance control techniques are essential to improve operator safety and robot flexibility by preventing impacts that can occur between the robot and humans or with objects inadvertently left within the operational workspace. On this basis, collision avoidance algorithms for moving obstacles are presented in this paper: inspired by algorithms already developed by the authors for planar manipulators, algorithms are adapted for the 6-DOF collaborative manipulators by Universal Robots, and some new contributions are introduced. First, in this work, the safety region wrapping each link of the manipulator assumes a cylindrical shape whose radius varies according to the speed of the colliding obstacle, so that dynamical obstacles are avoided with increased safety regions in order to reduce the risk, whereas fixed obstacles allow us to use smaller safety regions, facilitating the motion of the robot. In addition, three different modalities for the collision avoidance control law are proposed, which differ in the type of motion admitted for the perturbation of the end-effector: the general mode allows for a 6-DOF perturbation, but restrictions can be imposed on the orientation part of the avoidance motion using 4-DOF or 3-DOF modes. In order to demonstrate the effectiveness of the control strategy, simulations with dynamic and fixed obstacles are presented and discussed. Simulations are also used to estimate the required computational effort in order to verify the transferability to a real system.
The use of collaborative robots (or cobots) in rehabilitation therapies is aimed at assisting and shortening the patient’s recovery after neurological injuries. Cobots are inherently safe when interacting with humans and can be programmed in different working modalities based on the patient’s needs and the level of the injury. This study presents a design optimization of a robotic system for upper limb rehabilitation based on the manipulability ellipsoid method. The human–robot system is modeled as a closed kinematic chain in which the human hand grasps a handle attached to the robot’s end effector. The manipulability ellipsoids are determined for both the human and the robotic arm and compared by calculating an index that quantifies the alignment of the principal axes. The optimal position of the robot base with respect to the patient is identified by a first global optimization and by a further local refinement, seeking the best alignment of the manipulability ellipsoids in a series of points uniformly distributed within the shared workspace.
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