Momentum Dumping for Space Robots

During the robotic capture of a target object on orbit, accidental contacts may happen. During contacts, momentum is transferred to the system, causing a drift of the space robot in the inertial space. When no remediation is taken, the arm might converge to singularity or workspace limit within seconds, compromising the capture operation. This article presents a method to control the end-effector while simultaneously extracting any accumulated momentum in the system to cancel the drift. A feature of the method is that external actuators are only used for the momentum extraction and not to counterbalance the manipulator control forces. The control is validated with experiments using a Hardware-In-the- Loop (HIL) robotic simulator composed of a 7DOF (Degrees Of Freedom) arm mounted on a 6DOF moving base

From underactuation to quasi-full actuation: Aiming at a unifying control framework for articulated soft robots

We establish a structure preserving state and input transformation that allows a class of underactuated Euler Lagrange systems to be treated as “quasi-fully” actuated. In this equivalent quasi-fully actuated form, the system is characterized by the same Lagrangian structure as the original one. This facilitates the design of control approaches that take into account the underlying physics of the system and that shape the system dynamics to a minimum extent. Due to smoothness constraints on the new input vector that acts directly on the noncollocated coordinates, we coin the term quasi-fully actuated. The class of Euler–Lagrange systems we consider is the class of articulated soft robots with nonlinear spring characteristics that are modeled with a block diagonal inertia matrix. We illustrate how the quasi-fully actuated form enables the direct transfer of control concepts that have been derived for fully actuated manipulators. We adopt the popular energy-shaping and two passivity-based concepts. The exemplary adoptions of the PD+ and Slotine and Li controllers allow us to solve the task-space tracking problem for highly elastic joint robots with nonlinear spring characteristics. These control schemes allow compliant behavior of the robot's TCP to be specified with respect to a reference trajectory. A key aspect of the presented framework is that it enables the adoption of rigid joint controllers as well as concepts underlying the original stability analysis. We believe that our framework presents an important step toward unifying the control design for rigid and articulated soft robots

Practical Approach to Characterize Realistic Motor Dynamics for Robotic Simulation Independent of the Use Case

Incorporating realistic actuator dynamics in robotic simulations is key to a successful simulation-to-reality transfer. But real actuation chains are often complex and impossible to model with analytical methods alone. Although it is feasible to reverse-engineer the actuator dynamics from hardware measurements, this requires the completed robotic system to be already available. To enable the inclusion of realistic actuator dynamics in robot models also during the design phase or for initial controller tuning, this work presents an alternative hands-on approach for actuator characterization. Based on actuator measurements taken independently of the overall system integration, a model expression for the actuator is derived. This can be added to the simulation of any robotic system. To showcase this concept, we present the workflow for a robotic leg with a Series Elastic Actuation chain. We create a simulation of the leg incorporating the derived actuator model and show its validity through comparison with analogous hardware. The observed motor and link dynamics of both cases show close correspondence without increasing the needed computation times with respect to a simulation without actuation. Thus, the proposed method offers a promising approach to include realistic actuator dynamics during the design and development process of robotic applications

Elastic Structure Preserving Impedance (ESPi) Control for Compliantly Actuated Robots

We present a new approach for Cartesian impedance control of compliantly actuated robots with possibly nonlinear spring characteristics. It reveals a remarkable stiffness and damping range in the experimental evaluation. The most interesting contribution, is the way the desired closed-loop dynamics is designed. Our control concept allows to add a desired stiffness and damping directly on the end-effector, while leaving the system structure intact. The intrinsic inertial and elastic properties of the system are preserved. This is achieved by introducing new motor coordinates that reflect the desired spring and damper terms. Theoretically, by means of additional motor inertia shaping it is possible to make the end-effector interaction behavior with respect to external loads approach, arbitrarily close, the interaction behavior that is achievable by classical Cartesian impedance control on rigid robots. The physically motivated design approach allows for an intuitive understanding of the resulting closed-loop dynamics. We perform a passivity and stability analysis on the basis of al physically motivated storage and Lyapunov function

Kinematic transfer learning of sampling distributions for manipulator motion planning

Recent research has shown that guiding sampling-based planners with sampling distributions, learned from previous experiences via density estimation, can significantly decrease computation times for motion planning. We propose an algorithm that can estimate the density from the experiences of a robot with different kinematic structure, on the same task. The method allows to generalize collected data from one source manipulator to similarly designed target manipulators, significantly reducing the computation time for new queries for the target manipulator. We evaluate the algorithm in two experiments, including a constrained manipulation task with five different collaborative robots, and show that transferring information can significantly decrease planning time

Mixture of experts on Riemannian manifolds for visual-servoing fixtures

Adaptive Virtual Fixtures (VFs) for teleoperation often rely on visual inputs for online adaptation. State estimation from visual detections is never perfect, and thus affects the quality and robustness of adaptation. It is therefore important to be able to quantify how uncertain an estimation from vision is. This can, for example, inform on how to modulate a fixture's stiffness to decrease the physical force a human operator has to apply. Furthermore, the target of a manipulation operation might not be known from the beginning of the task, which creates the need for a principled way to add and remove fixtures when possible targets appear in the robot workspace. In this paper we propose an on-manifold Mixture of Experts (MoE) model that synthesizes visual-servoing fixtures while elegantly handling full pose detection uncertainties and 6D teleoperation goals in a unified framework. An arbitration function allocating the authority between multiple vision-based fixtures arises naturally from the MoE formulation. We show that this approach allows a teleoperator to insert multiple printed circuit boards (PCBs) with high precision without requiring the manual design of VFs to guide the robot motion. An exemplary video visualizing the probability distribution resulting from our model is available at: https://youtu.be/GKMQvbJ5Oz

Multi-Phase Multi-Modal Haptic Teleoperation

Virtual Fixtures facilitate teleoperation, for instance by guiding the human operator. Developing these Virtual Fixtures in tasks with tight tolerances remains challenging. Fixtures with a high stiffness allow for more precise guidance, whereas a lower stiffness is required to allow for corrections. We observed that many assembly operations can be split into different phases - approaching, positioning, in-contact manipulation - each with different accuracy requirements. Therefore, we propose to use multi-modal fixtures, satisfying the different requirements of these phases: i.e. a position-based Trajectory Fixture for approaching and a more accurate Visual Servoing Fixture for the positioning phase. A state estimation and arbitration component ensures smooth transitions between the fixtures to provide optimal support for the operator and to achieve global availability paired with local precision at the same time. It also allows a high stiffness to be used throughout, thus achieving good guidance for all phases. The approach is validated in an application from a space scenario, consisting of the assembly of a CubeSat subsystem. The empirical results from a pilot study on this task show that our approach is faster and requires less interaction force from the operator than the baseline method

Unified Motion Planner for Walking, Running, and Jumping Using the Three-Dimensional Divergent Component of Motion

Running and jumping are locomotion modes that allow legged robots to rapidly traverse great distances and overcome difficult terrain. In this article, we show that the 3-D divergent component of motion (3D-DCM) framework, which was successfully used for generating walking trajectories in previous works, retains its validity and coherence during flight phases, and, therefore, can be used for planning running and jumping motions. We propose a highly efficient motion planner that generates stable center-of-mass (CoM) trajectories for running and jumping with arbitrary contact sequences and time parametrizations. The proposed planner constructs the complete motion plan as a sequence of motion phases that can be of different types: stance, flight, transition phases, etc. We introduce a unified formulation of the CoM and DCM waypoints at the start and end of each motion phase, which makes the framework extensible and enables the efficient waypoint computation in matrix and algorithmic form. The feasibility of the generated reference trajectories is demonstrated by extensive whole-body simulations with the humanoid robot TORO

Model Predictive Control Applied to Different Time-scale Dynamics of Flexible Joint Robots

Modern Lightweight robots are constructed to be collaborative, which often results in a low structural stiffness compared to conventional rigid robots. Therefore, the controller must be able to handle the dynamic oscillatory effect mainly due to the intrinsic joint elasticity. Singular perturbation theory makes it possible to decompose the flexible joint dynamics into fast and slow subsystems. This model separation provides additional features to incorporate future knowledge of the joint level dynamical behavior within the controller design using the Model Predictive Control (MPC) technique. In this study, different architectures are considered that combine the method of Singular Perturbation and MPC. For Singular Perturbation, the parameters that influence the validity of using this technique to control a flexible-joint robot are investigated. Furthermore, limits on the input constraints for the future trajectory are considered with MPC. The position control performance and robustness against external forces of each architecture are validated experimentally for a flexible joint robot. The experimental validation shows superior performance in practice for the presented MPC framework, especially respecting the actuator torque limits

Adapting Highly-Dynamic Compliant Movements to Changing Environments: A Benchmark Comparison of Reflex- vs. CPG-Based Control Strategies

To control highly-dynamic compliant motions such as running or hopping, vertebrates rely on reflexes and Central Pattern Generators (CPGs) as core strategies. However, decoding how much each strategy contributes to the control and how they are adjusted under different conditions is still a major challenge. To help solve this question, the present paper provides a comprehensive comparison of reflexes, CPGs and a commonly used combination of the two applied to a biomimetic robot. It leverages recent findings indicating that in mammals both control principles act within a low-dimensional control submanifold. This substantially reduces the search space of parameters and enables the quantifiable comparison of the different control strategies. The chosen metrics are motion stability and energy efficiency, both key aspects for the evolution of the central nervous system. We find that neither for stability nor energy efficiency it is favorable to apply the state-of-the-art approach of a continuously feedback-adapted CPG. In both aspects, a pure reflex is more effective, but the pure CPG allows easy signal alteration when needed. Additionally, the hardware experiments clearly show that the shape of a control signal has a strong influence on energy efficiency, while previous research usually only focused on frequency alignment. Both findings suggest that currently used methods to combine the advantages of reflexes and CPGs can be improved. In future research, possible combinations of the control strategies should be reconsidered, specifically including the modulation of the control signal's shape. For this endeavor, the presented setup provides a valuable benchmark framework to enable the quantitative comparison of different bioinspired control principles