There exist numerous ways to achieve multi-tasking control in kinematically redundant robots to accomplish several goals simultaneously. In all approaches, regardless of the specific type of controller, one has to make a choice about the closed-loop inertia and consequently the dynamic task couplings. Here, we introduce a new control strategy which combines two fundamentally different properties that have not been brought together yet. First, we fully dynamically decouple all individual subtasks, which cannot be achieved with classical passivity-based or hierarchical approaches. Second, we provide high robustness in practice which is structurally not possible with any inverse-dynamics approaches enforcing a decoupled but constant closed-loop inertia. Beside formal proofs of stability and passivity, we compare our approach with the other categories in various simulations and experiments. Since the proposed controller is grounded on the fundamental property of full natural task-space decoupling, this underlying strategy and its benefits can also be transferred to other design methods such as quadratic programming, MPC, or learning-based approaches.

In this paper, we propose a model predictive control (MPC) that accomplishes interactive robotic tasks, in which multiple contacts may occur at unknown locations. To address such scenarios, we made an explicit contact feedback loop in the MPC framework. An algorithm called Multi-Contact Particle Filter with Exploration Particle (MCP-EP) is employed to establish real-time feedback of multi-contact information. Then the interaction locations and forces are accommodated in the MPC framework via a spring contact model. Moreover, we achieved real-time control for a 7 degrees of freedom robot without any simplifying assumptions by employing a Differential-Dynamic-Programming algorithm. We achieved 6.8kHz, 1.9kHz, and 1.8kHz update rates of the MPC for 0, 1, and 2 contacts, respectively. This allows the robot to handle unexpected contacts in real time. Real-world experiments show the effectiveness of the proposed method in various scenarios.

Since modern robots are supposed to work closely together with humans, physical human-robot interaction is gaining importance. One crucial aspect for safe collaboration is a robust collision reaction strategy that is triggered after an unintentional physical contact. In this work, we propose a dynamically-consistent collision reaction controller, where the reactive motion is performed in one particular desired direction in Cartesian space, without disturbing the remaining ones. This results in more intuitive and more predictable behavior of the end-effector. In addition, the proposed reaction control law is independent of contact and internal observer dynamics used for collision detection. The theoretical claims are validated in simulation and experiments. The proposed reaction controller is experimentally compared with a conventional approach for collision reaction. All experiments have been conducted on a torque controlled KUKA LWR IV + lightweight robot.

As labor shortage increases in the health sector, the demand for assistive robotics grows. However, the needed test data to develop those robots is scarce, especially for the application of active 3D object detection, where no real data exists at all. This short paper counters this by introducing such an annotated dataset of real environments. The captured environments represent areas which are already in use in the field of robotic health care research. We further provide ground truth data within one room, for assessing SLAM algorithms running directly on a health care robot.

Physical human-robot interaction is known to be a crucial aspect in modern lightweight robotics. Herein, the estimation of external interactions is essential for the effective and safe collaboration. In this work, an extended momentum-based disturbance observer is presented which includes the sensing redundancy related to additional force-torque measurements. The observer eliminates the need for acceleration measurements/estimates and it is able to accurately reconstruct multiple simultaneous contact locations. Moreover, it provides uncoupled, configuration-independent, and singularity-free estimates of the external forces. The performance of the approach is experimentally validated on the SARA robot, the new generation of DLR lightweight robots, involving high resolution force-torque sensors in a redundant arrangement.

Light-weight robotic manipulators can be used to restore the manipulation capability of people with a motor disability. However, manipulating the environment poses a complex task, especially when the control interface is of low bandwidth, as may be the case for users with impairments. Therefore, we propose a constraint-based shared control scheme to define skills which provide support during task execution. This is achieved by representing a skill as a sequence of states, with specific user command mappings and different sets of constraints being applied in each state. New skills are defined by combining different types of constraints and conditions for state transitions, in a human-readable format. We demonstrate its versatility in a pilot experiment with three activities of daily living. Results show that even complex, high-dimensional tasks can be performed with a low-dimensional interface using our shared control approach.

Lightweight robots are known to be intrinsically elastic in their joints. The established classical approaches to control such systems are mostly based on motor-side coordinates since the joints are comparatively stiff. However, that inevitably introduces errors in the coordinates that actually matter: the ones on the link side. Here we present a new joint-torque controller that uses feedback of the link-side positions. Passivity during interaction with the environment is formally shown as well as asymptotic stability of the desired equilibrium in the regulation case. The performance of the control approach is experimentally validated on DLR's new generation of lightweight robots, namely the SARA robot, which enables this step from motor-side-based to link-sided-based control due to sensors with higher resolution and improved sampling rate.

Injuries, accidents, strokes, and other diseases can significantly degrade the capabilities to perform even the most simple activities in daily life. A large share of these cases involves neuromuscular diseases, which lead to severely reduced muscle function. However, even though affected people are no longer able to move their limbs, residual muscle function can still be existent. Previous work has shown that this residual muscular activity can suffice to apply an EMG-based user interface. In this paper, we introduce DLR's robotic wheelchair EDAN (EMG-controlled Daily Assistant), which is equipped with a torque-controlled, eight degree-of-freedom light-weight arm and a dexterous, five-fingered robotic hand. Using electromyography, muscular activity of the user is measured, processed and utilized to control both the wheelchair and the robotic manipulator. This EMG-based interface is enhanced with shared control functionality to allow for efficient and safe physical interaction with the environment.

A physically-motivated friction model with a parametric description of the nonlinear dependency of the temperature and velocity as well as the dependency on external load is presented. The fully parametric approach extends a static friction model in the gross sliding regime. We show how it can be seamlessly integrated in standard dynamic friction models such as Lund Grenoble (LuGre) and Generalized-Maxwell-Slip (GMS). Parameters of a Harmonic Drive CSD 25 gear are experimentally identified and the final model is evaluated on a dedicated test-bed. We show the integration and effectiveness in dynamic simulation, friction compensation, and external torque estimation.

Light-weight robotic manipulators in combination with power wheelchairs can help to restore the mobility of people with disabilities. While such systems are available on the market, they typically are limited to fully manual control modes. In research, shared control methods are employed, to increase the usability of these systems. Here, we present an additional extension, by introducing a whole-body control concept to the assistive robotic system EDAN. Combined with shared control, the whole-body controller allows the realization of complex tasks which necessitate the coordination of arm and platform, while ensuring compliant behavior resulting from the impedance control law. The implemented approach is analyzed and validated in an exemplary task of opening a door, passing through it and closing it afterwards. While this task would exceed the reachability of the arm in a classical approach, the combination of whole-body control with a shared control scheme allows for quick and efficient execution.

In robotic actuation a well identified and modeled friction behavior of the actuator components helps to significantly improve friction compensation, output torque estimation, and dynamic simulations. The friction of two components, i.e. a brush-less DC motor and a harmonic drive gear (HD) is investigated in order to build an accurate dynamic model of the main actuator of the arms of the humanoid David namely the DLR Floating Spring Joint (FSJ). A dedicated testbed is built to precisely identify input and output torques, temperatures, positions, and elasticities of the investigated components at a controlled environment temperature. Extensive test series are performed in the full velocity operating range in a temperature interval from 24 to 50 °C. The nonlinear influences of velocity and temperature are identified to be dominant effects. It is proposed how to include these nonlinear velocity and temperature dependencies into a static and a dynamic friction model, e.g. LuGre. Dynamic models of the motor and HD are built with the proposed method and experimentally evaluated. The new models are compared to friction models with linear dependencies and show a significant improvement of correspondence with reality.