Collaborative robots are increasingly present in our lives. The KUKA LBR iiwa, equipped with the KUKA Sunrise.OS controller, is one example of a collaborative/sensitive robot. This tutorial presents the KUKA Sunrise Toolbox (KST), a MATLAB toolbox that interfaces with KUKA Sunrise.OS. KST contains functionalities for networking, soft control in real time, point-to-point motion, parameter setters/getters, general purpose, and physical interaction. It includes approximately 100 functions and runs on a remote computer connected with the KUKA Sunrise controller via Transmission Control Protocol/Internet Protocol (TCP/IP). The potentialities of the KST are demonstrated in nine application examples.

Redundancy in robotic manipulators has many advantages. It is successfully used to achieve better dexterity, and to avoid obstacles, singularities, or the kinematic limitations. However, redundancy makes the inverse kinematics (IK) problem harder to solve. The damped least squares (DLS) is a powerful method for calculating the IK of redundant robots, but it suffers from noncyclicity issue, where a closed curve motion in the Cartesian space of the end-effector (EEF) does not map into a closed curve in the joint space. This results in nonrepetitive motion in the joint space, even though the EEF motion is repetitive. In this article, we present a solution for the noncyclicity problem in the DLS method. The proposed scheme was successfully tested both in simulation (9 DoF robot) and on a real robotic manipulator (7 DoF robot).

The problem of minimum distance calculation between line-segments/capsules, in 3D space, is an important subject in many engineering applications, spanning CAD design, computer graphics, simulation and robotics. In the latter, the human-robot minimum distance is a main input for collision avoidance/detection algorithms to measure collision imminence. Capsules can be used to represent humans and objects, including robots, in a given dynamic environment. In this scenario, it is important to calculate the minimum distance between capsules efficiently, especially for scenes (situations) that include high number of capsules. This study investigates the utilization of QR factorization for performing efficient minimum distance calculation between capsules. The problem is reformulated as a bounded variable optimization in which an affine transformation, deduced from QR factorization, is applied on the region of feasible solutions. A geometrical approach is proposed to calculate the solution, which is achieved by computing the point closest to the origin from the transferred region of feasible solutions. The study is concluded with numerical tests, showing that the proposed method compares favourably with the most efficient method reported in literature.