Cooperative robotics is the application field where the areas of networked control, data-driven control, human-centered control and bio-inspired design come together. Examples include distributed multi-agent systems and human-centered robot control. Additionally, on a physical level of interaction, we perform an exploration of haptics in cooperative control with teams of humans and robots. Employing haptic devices in cooperative control necessitates recognition and prediction of human haptic intention, as well as architectures enabling cognitive team control. On a practical side, we are also interested in employing control and motion schemes for state-of-the-art robots moving into dynamic home environments, with a human present. Such methods need to be safe for the user and robust in task execution, putting an emphasis on sound experimental validation.
Researcher: Jan Brüdigam
Typically, robotic systems are described in minimal (also called generalized) coordinates. Here, each coordinate represents a single degree of freedom of the underlying structure (for example the angle of a pendulum). The advantage of this parameterization lies in the small number of variables and the avoidance of constraints.
However, for modern robots carrying out complicated tasks, for example collaboratively carrying objects, minimal coordinates are not always ideal. Instead, it can be beneficial to use maximal coordinates resulting in a decoupled description of the system which can then be put together with additional constraints. This type of representation offers a number of numerical and control theoretic advantages. At the same time there are quite a few open questions still to be answered.
- Variational integrators are the ideal method to numerically integrate systems in maximal coordinates, but currently, only first-order methods are used. The development of higher-order variational integrators could provide improved performance.
- The modular structure of maximal coordinates is a key advantage over minimal coordinates and allows for the development of efficient parallelization and differentiation schemes.
- The accurate depiction of contact interactions and friction is still a widely research topic in the area of robotics and maximal coordinates can provide several advantages for treating such scenarios.
By using (higher-order) variational integrators, we can exploit the numerical advantages of maximal coordinates while avoiding the drawback of constraint drift, a property typical for regular integration of constraints. Following graph-based algorithms and modern optimization approaches allows us to derive efficient and performant numerical algorithms even for complex scenarios.
Key Results and Achievements
- Sparse factorization algorithm to solve dynamics in maximal coordinates in linear time.
- First-order variational integrator for dynamics in maximal coordinates.
Code for a dynamics simulation in maximal coordinates can be found here: https://github.com/janbruedigam/ConstrainedDynamics.jl
Researchers: Jan Brüdigam
Patients having suffered accidents or stroke often have to go through extensive rehabilitation to regain motor skills for an independent and self-determined life. In contrast to classical physical therapists, robotic rehabilitation systems are able to tirelessly and precisely apply intense manual labor over long periods of time, while accurately measuring performance and improvements of the patient.
The development of upper-body shared control strategies for rehabilitation routines requires accurate estimation of the human input during human-exoskeleton interactions. The main challenges for this estimation are the human model uncertainties.
- How to efficiently incorporate passive degrees of freedom and varying connection points
- How to deal with human model uncertainties
We are developing a torque observer based on the physical model of the exoskeleton and a nominal model of the human in combination with a data-driven identification of residual human model parameters to obtain precise results.
Researcher: Yi Ren
Multiple mobile manipulator ensemble has drawn increasing attention of the research community in recent years owing to its ability to perform more complex tasks such as transporting or assembling large and heavy objects that cannot be achieved by a single mobile robot.While these attracting advantages reveal a major increase of complexity for modelling and controlling such systems, especially for the case considered in our work that a team of uncertain nonholonomic mobile manipulators cooperate to grasp and move an unknown object. Cooperative transport task is very sensitive to dynamics/kinematic uncertainties of the interconnected system. Since the manipulators rigidly contact with the object, small kinematic discrepancy may lead to large tracking error and losing control of the internal force. Adaptability to dynamic/kinematic uncertainties endows the multiple robotic system with improved intelligence and flexibility.
- How to deal with the dynamic and closed-chain kinematic uncertainties to maintain high tracking performance?
- Internal force control loop is inherently centralized and the definition is still controversial in the research community, then how to control the internal force in a distributed manner?
- How to appropriately design the hierarchical task framework and maintain the priorities of the collective tasks under multiple uncertainties?
- How to avoid employment of some noisy signals in the practical scenario as far as possible to facilitate the control implementation?
- Adaptive control based on the linearization of the system dynamics/kinematics.
- Task-space motion synchronization of the mobile manipulators to alleviate the performance degradation during the transient phase.
- Cascade subsystem design of the controller, including global prioritized task assignment/decomposition, control allocation and local force/position controller design.
- Mechanism analysis of the internal force.
Key Results and Achievements
- Design of the adaptive synchronization based distributed control.
- Design of the distributed observers to obtain separation property.
- Distributed estimation of the desired cooperative trajectory.
- Establishment of simulation models for the multiple nonholonomic mobile manipulator system.
Researcher: Le Fu
Compliance is a key issue in human safely and efficiently collaborating with robot or multiple robots. Impedance control is a typical control methodology to realize compliance, which adjust the inertia, viscosity, and elasticity to achieve a dynamic relationship between displacement and external force. Classical impedance control methods mentioned above are based on the so-called Voigt model that connects a spring and damper in parallel way. By contrast, there is another parallel-type viscoelastic model called Maxwell model. This model is considered a contrasting approach relative to conventional impedance control in terms of the connection configuration. In comparison, this new kind of impedance control law can make robot more soft and compliant, which is of great significance to safety in human robot cooperation. However, it is not easy to control and implement in real robot tasks for several reasons. On the one hand, more control variables should be considered, including the rest point or equilibrium. On the other hand, since the robot should continue the task disturbed by impact or human contact, so merely controlling the rest point is inadequate. Therefore, we propose control schemes to take advantage of the plastic deformation behavior of Maxwell model in order to ensure safety, meanwhile overcome the shortcomings for more desirable control performance in real robot tasks, especially in human robot cooperation. In short, our objective is the compromise of safety and performance.
- How to implement this novel model into real robot task and endure the robot a plastic deformation behavior.
- Similar to hybrid motion and force control, how to design a control law to realize this kind of model in selected orientation.
- Since how much deviation depends on the external forces (amplitude, orientation...), not known ahead, how to recover from relatively large deviation and continue to execute tasks.
Feedback linearization or other nonlinear control methods to designed a new impedance control law and use Lyapunov stability analysis.
- Combine this with motion controllers to fulfil task while ensuring safety.
- Further development with some no-replanning methods like dynamic movement primitives.
Key Results and Achievements
- Design of the nonlinear control law and implement in a 3-DOF manipulator.
- Extension to more joints manipulators such as FRANKA 7-DOF redundant robot.