The handling of liquid filled containers is a challenging task in industrial manufacturing processes. Without an appropriate trajectory sloshing of the liquid occurs during point-to-point motion. This paper presents a time optimal trajectory planning approach that is used for feedforward motion control and prevents the liquid filled container from sloshing. For this, a model of the liquid inside the container is included into the formulation of a constrained dynamic optimization problem to achieve rest-to-rest motion while simultaneously avoiding obstacles. The presented approach is validated by simulation and on an experimental setup using an industrial robot handling the container.

Flexibility combined with the ability to consider external constraints comprises the main advantages of nonlinear model predictive control (NMPC). Applied as a motion controller, NMPC enables applications in varying and disturbed environments, but requires time-consuming computations. Hence, given the full nonlinear multi-DOF robot model, a delay-free execution providing short control horizons at appropriate prediction horizons for accurate motions is not applicable in common use. This contribution introduces an approach that analyzes and decomposes the differential kinematics similar to the inverse kinematics method to assign Cartesian boundary conditions to specific systems of equations during the model building, reducing the online computational costs. The resulting fully constrained NMPC realizes the translational obstacle avoidance during trajectory tracking using a reduced model considering both joint and Cartesian constraints coupled with a Jacobian transposed controller performing the end-effector’s orientation correction. Apart from a safe distance from the obstacles, the presented approach does not lead to any limitations of the reachable workspace, and all degrees of freedom (DOFs) of the robot are used. The simulative evaluation in Gazebo using the Stäubli TX2-90 commanded of ROS on a standard computer emphasizes the significantly lower online computational costs, accuracy analysis, and extended adaptability in obstacle avoidance, providing additional flexibility. An interpretation of the new concept is discussed for further use and extensions.

Imaging techniques are established aids in surgical procedures and have become indispensable in modern medicine. Combined with robot-assisted systems, significantly higher precision can already be achieved today and work steps can be simplified. We hypothesize that algorithms can make the operations of the future safer and easier. The paper introduces an approach for the control of a robotic manipulator that, based on standardized imaging techniques, monitors and adapts externally given movement commands. The physician controls the system, benefits from the robot’s precision, and movements will be intervened in if unwanted contact with the environment would occur. This is important, e.g., when operating near sensitive tissue, nerves or bone that must not be injured. Objects from the image data are considered in the control scheme and may have arbitrary shape and size. Furthermore, since the approach can be in principle adapted to any shape it is shown how robot operations in confined spaces, that are challenging to achieve using an external controller, can be simplified by the introduced approach. The online evaluation takes place in Cartesian space and is then transformed into joint space using the inverse kinematics. The approach is implemented using GAZEBO and a simulation study is performed for the 6-DOF industrial manipulator Stäubli TX2-60.