Many real-world applications require legged robots to be able to carry variable payloads. Model-Based controllers such as model predictive control (MPC) have become the de facto standard in research for controlling these systems. However, most model-based control architectures use fixed plant models, which limits their applicability to different tasks. In this paper, we present a Kalman filter (KF) formulation for online identification of the mass and center of mass (COM) of a four-legged robot. We evaluate our method on a quadrupedal robot carrying various payloads and find that it is more robust to strong measurement noise than classical recursive least squares (RLS) methods. Moreover, it improves the tracking performance of the model-based controller with varying payloads when the model parameters are adjusted at runtime.

In robotics, structural design and behavior optimization have long been considered separate processes, resulting in the development of systems with limited capabilities. Recently, co-design methods have gained popularity, where bi-level formulations are used to simultaneously optimize the robot design and behavior for specific tasks. However, most implementations assume a serial or tree-type model of the robot, overlooking the fact that many robot platforms incorporate parallel mechanisms. In this paper, we present a first co-design formulation that explicitly incorporates parallel coupling constraints into the dynamic model of the robot. In this framework, an outer optimization loop focuses on the design parameters, in our case the transmission ratios of a parallel belt-driven manipulator, which map the desired torques from the joint space to the actuation space. An inner loop performs trajectory optimization in the actuation space, thus exploiting the entire dynamic range of the manipulator. We compare the proposed method with a conventional co-design approach based on a simplified tree-type model. By taking advantage of the actuation space representation, our approach leads to a significant increase in dynamic payload capacity compared to the conventional co-design implementation.

Optimal behaviours of a system to perform a specific task can be achieved by leveraging the coupling between trajectory optimization, stabilization, and design optimization. This approach is particularly advantageous for underactuated systems, which are systems that have fewer actuators than degrees of freedom and thus require for more elaborate control systems. This paper proposes a novel co-design algorithm, namely Robust Trajectory Control with Design optimization (RTC-D). An inner optimization layer (RTC) simultaneously performs direct transcription (DIRTRAN) to find a nominal trajectory while computing optimal hyperparameters for a stabilizing time-varying linear quadratic regulator (TVLQR). RTC-D augments RTC with a design optimization layer, maximizing the system’s robustness through a time-varying Lyapunov-based region of attraction (ROA) analysis. This analysis provides a formal guarantee of stability for a set of off-nominal states. The proposed algorithm has been tested on two different underactuated systems: the torque-limited simple pendulum and the cart-pole. Extensive simulations of off-nominal initial conditions demonstrate improved robustness, while real-system experiments show increased insensitivity to torque disturbances.

This paper presents the design, analysis, and performance evaluation of RicMonk, a novel three-link brachiation robot equipped with passive hook-shaped grippers. Brachiation, an agile and energy-efficient mode of locomotion observed in primates, has inspired the development of RicMonk to explore versatile locomotion and maneuvers on ladder-like structures. The robot’s anatomical resemblance to gibbons and the integration of a tail mechanism for energy injection contribute to its unique capabilities. The paper discusses the use of the Direct Collocation methodology for optimizing trajectories for the robot’s dynamic behaviors and stabilization of these trajectories using a Time-varying Linear Quadratic Regulator. With RicMonk we demonstrate bidirectional brachiation, and provide comparative analysis with its predecessor, AcroMonk - a two-link brachiation robot, to demonstrate that the presence of a passive tail helps improve energy efficiency. The system design, controllers, and software implementation are publicly available on GitHub at https://github.com/dfki-ric-underactuated-lab/ricmonk and the video demonstration of the experiments can be viewed at https://youtu.be/hOuDQI7CD8w.

Trajectory optimization methods have become ubiquitous for the motion planning and control of underactuated robots for e.g., quadrupeds, humanoids etc. While they have been extensively used in the case of serial or tree type robots, they are seldomly used for planning and control of robots with closed loops. Series-parallel hybrid topology is quite commonly used in the design of humanoid robots, but they are often neglected during trajectory optimization and the movements are computed for a serial abstraction of the system and then the solution is mapped to the actuator coordinates. As a consequence, the full capability of the robot cannot be exploited. This paper presents a case study of trajectory optimization for series-parallel hybrid robot by taking into account all the holonomic constraints imposed by the closed kinematic loops present in the system. We demonstrate the advantages of this consideration with a weightlifting task on RH5 Manus humanoid in both simulation and experiments.

Legged locomotion is arguably the most suited and versatile mode to deal with natural or unstructured terrains. Intensive research into dynamic walking and running controllers has recently yielded great advances, both in the optimal control and reinforcement learning (RL) literature. Hopping is a challenging dynamic task involving a flight phase and has the potential to increase the traversability of legged robots. Model based control for hopping typically relies on accurate detection of different jump phases, such as lift-off or touch down, and using different controllers for each phase. In this paper, we present a end-to-end RL based torque controller that learns to implicitly detect the relevant jump phases, removing the need to provide manual heuristics for state detection. We also extend a method for simulation to reality transfer of the learned controller to contact rich dynamic tasks, resulting in successful deployment on the robot after training without parameter tuning.

It is well established that a stiff structure along with an optimal mass distribution are key features to perform dynamic movements, and parallel designs provide these characteristics to a robot. This work presents the new upper-body design of the humanoid robot RH5 named RH5 Manus with series-parallel hybrid design. The new design choices allow us to perform dynamic motions including tasks that involve a payload of 4 kg in each hand and fast boxing motions. The parallel kinematics combined with an overall serial chain of the robot provides us with high force production along with a larger range of motion and low peripheral inertia. The robot is equipped with backdrivable actuators with current sensing, force-torque sensors, stereo camera, laser scanners, high-resolution encoders etc that provide interaction with operators and environment. We generate several diverse dynamic motions using trajectory optimization, and successfully execute them on the robot with accurate trajectory and velocity tracking, while respecting joint rotation, velocity, and torque limits.

Parallel mechanisms are becoming increasingly popular as subsystems in various robots due to their superior stiffness, payload-to-weight ratio, and dynamic properties. The serial connection of parallel subsystems leads to series-parallel hybrid robots, which are more difficult to model and control than serial or tree-type systems. At the same time, Whole-Body Control (WBC) has become the method of choice in the control of robots with redundant degrees of freedom, e.g., legged robots. However, most state-of-the-art WBC frameworks can only deal with serial or tree-type robot topologies. In this paper, we describe a computationally efficient framework for Whole-Body Control of series-parallel hybrid robots subjected to a large number of holonomic constraints. In contrast to existing WBC frameworks, our approach describes the optimization problem in the actuation space of a series-parallel robot, which provides better exploitation of the feasible workspace, higher accuracy, and more transparent behavior near singularities. We evaluate the proposed framework on two different humanoids with series-parallel architecture and compare its performance to a WBC approach for tree-type robots.

Musical dancing is an ubiquitous phenomenon in the human society. Providing robots the ability to dance has the potential to make the human robot co-existence more acceptable in our society. Hence, dancing robots have generated a considerable research interest in the recent years. In this paper, we present a novel formalization of robot dancing as planning and control of optimally timed actions based on beat timings and additional features extracted from the music. We showcase the use of this formulation in three different variations: with input of human expert choreography, imitation of a predefined choreography, and automated generation of a novel choreography. Our method has been validated on four different musical pieces, both in simulation and on a real robot, using the upper-body humanoid robot RH5 Manus.

Modeling closed loop mechanisms is a necessity for the control and simulation of various systems and poses a great challenge to rigid body dynamics algorithms. Solving the forward and inverse dynamics for such systems require resolution of loop closure constraints which are often solved via numerical procedures. This brings an additional burden to these algorithms as they have to stabilize and control the loop closure errors. In order to avoid this issue, analytical solutions are preferred for commonly studied parallel mechanisms. This paper has two contributions. Firstly, it reports a case study on a modular and hybrid numerical-analytical approach to model and control series-parallel hybrid robots which are subjected to large number of holonomic constraints. The approach exploits the modularity in the robot design to combine analytical loop closure for the known submechanisms and numerical loop closure for submechanisms where analytical solutions are not available. This offers an edge over purely numerical approach in terms of computational efficiency. Secondly, an adaption of the constraint embedding approach in Articulated Body Algorithm (ABA) is presented which yields a recursive algorithm in minimal coordinates for computing the forward dynamics of series-parallel hybrid systems. The proposed modification exploits the Lie group formulations and allows easy implementation of recursive forward dynamics of constrained systems in state of the art multi-body solvers.

Unlike fully actuated systems, the control of underactuated robots necessitates the use of passive dynamics to fulfill control objectives. Hence, there is an increased interdependence between their design parameters and the closed loop performance. This paper proposes a novel approach for co-optimization of robot design and controller parameters for increased certifiable stability obtained with means of region of attraction analysis and gradient free optimization. In particular, it discusses the co-optimization problem of a gymnastic acrobot robot where the design and the controller are optimized to have a large region of attraction (ROA) taking into account the closed loop dynamics of the non-linear system stabilized by a linear quadratic regulator (LQR) controller. The results are validated by extensive simulation of the acrobot's closed loop dynamics.

Derivatives of equations of motion describing the rigid body dynamics are becoming increasingly relevant for the robotics community and find many applications in design and control of robotic systems. Controlling robots, and multibody systems comprising elastic components in particular, not only requires smooth trajectories but also the time derivatives of the control forces/torques, hence of the equations of motion (EOM). This paper presents novel nthorder time derivatives of the EOM in both closed and recursive forms. While the former provides a direct insight into the structure of these derivatives, the latter leads to their highly efficient implementation for large degree of freedom robotic system.

It is becoming increasingly popular to use parallel mechanisms as modular subsystem units in the design of various robots for their superior stiffness, payload-to-weight ratio and dynamic properties. This leads to series-parallel hybrid robotic systems which pose several challenges in their modeling and control e.g. resolution of loop closure constraints, large size of their spanning tree etc. These robots are typically position-controlled and when equipped with real time dynamic control, often a simplified inverse dynamic model of these systems is utilized. However, the trade-offs of this model simplification has not been studied previously. This paper presents a representative study of the neglected dynamics by introducing some error metrics which are useful in highlighting the advantages and disadvantages of such model simplification. The study is guided with the help of a series-parallel humanoid leg which has been recently developed at DFKI-RIC.

In the contemporary digital era, access control is one of the security issues in the modern Electronic Healthcare System (EHS). In particular, the E-Health Cloud (EHC) needed a secure and reliable access control to access the EHC resources. In the past, several attempts have been made to provide secure and reliable access control (AC) to the EHS. But, due to lack of trust and dynamic nature of EHC, the model will suffer from different types of attacks and threats. The provided Access Control Models (ACMs) does not provide complete security to EHS. So, in this paper, we have proposed a Secure Access Control Model (SACM) for E-Health Cloud. The proposed model dynamically calculates the trust degree of the users based on their behavior. The computed trust degree will be used for adjusting the access view of the user. The access view is controlled with the help of access control rule set. We also verified our proposed rule set by using CPN Tools.