A significant barrier regarding a successful implementation of fenceless robot cells into manufacturing areas with humans is given by the inefficiency due to safety requirements. Robot motions have to be slowed down so that an unexpected collision with a human does not result in human injuries. This velocity reduction leads to longer cycle times and, hence, fenceless robot cells turn out as uneconomic. In this paper, a new approach for human-robot collaboration in assembly tasks is presented. For a better performance of the robot, methods are investigated on how the robot can exploit a maximum performance while maintaining the safety of collaborating humans. For this purpose, the kinematics and dynamics of a human arm are described by a control-oriented dynamic model to determine its capability and reachability. Successful experiments validate the dynamic model as well as a corresponding projection approach for calculating possible movements of the human arm that may lead to a collision with the robot. Finally, this information is used to calculate an admissible path velocity that minimizes the danger of human injuries.

This paper is devoted to the study of multirotor Micro Aerial Vehicles (MAVs) with fixed-pitch propellers and bidirectional thrust vector. The latter is realized by using dedicated motor controllers, which allow to invert the propellers' direction of rotation during flight (so-called 3D mode), and almost or fully symmetric propellers. We present a unified modeling, controller design, and control allocation approach that accounts for bidirectional thrust. Suitable propellers with the ability to produce thrust and torque in both directions are compared and their parameters are identified through a static thrust test. Furthermore, we discuss applications of bidirectional thrust, like inverted flight or surface slip reduction, which are impossible to realize with common unidirectional thrust vehicles. We generate suitable flight trajectories and evaluate our unified approach in experiments with a custom-built quadrotor.

In this paper, a tracking control approach for robot assisted landing of UAV rotorcraft, such as multicopters and helicopters, is presented. The aim is to safely land these type of flying vehicles under side wind conditions and on moving surfaces using a robot manipulator arm mounted on the ground. In our previous work, we have considered light UAVs compared to the robot's payload capabilities. In this work, we present a method that enables to realize robot assisted landing for arbitrary combinations of aerial vehicles and robot arms. We show that the combined system is overactuated. This allows to distribute the workload and enables to assist heavy rotorcraft, whose weight would otherwise exceed the nominal payload of the robot arm. The performance of the controller is evaluated in numerical simulations as well as in experiments using a custom-built hexacopter and a KUKA/DLR light-weight robot.

In this paper, we present a method for learning and online generalization of maneuvers for quadrotor-type vehicles. The maneuvers are formulated as optimal control problems, which are solved using a general purpose optimal control solver. The solutions are then encoded and generalized with Dynamic Movement Primitives (DMPs). This allows for real-time generalization to new goals and in-flight modifications. An effective method for joining the generalized trajectories is implemented. We present the necessary theoretical background and error analysis of the generalization. The effectiveness of the proposed method is showcased using planar point-to-point and perching maneuvers in simulation and experiment.