Torque-controlled Manipulators Research Articles

Non-linearity Measure for POMDP-based Motion Planning

Motion planning under uncertainty is essential for reliable robot operation. Despite substantial advances over the past decade, the problem remains difficult for systems with complex dynamics. Most state-of-the-art methods perform search that relies on a large number of forward simulations. For systems with complex dynamics, this generally requires costly numerical integrations, which significantly slows down the planning process. Linearization-based methods have been proposed that can alleviate the above problem. However, it is not clear how linearization affects the quality of the generated motion strategy, and when such simplifications are admissible. To answer these questions, we propose a non-linearity measure, called Statistical-distance-based Non-linearity Measure (SNM), that can identify where linearization is beneficial and where it should be avoided. We show that when the problem is framed as the Partially Observable Markov Decision Process, the value difference between the optimal strategy for the original model and the linearized model can be upper-bounded by a function linear in SNM. Comparisons with an existing measure on various scenarios indicate that SNM is more suitable in estimating the effectiveness of linearization-based solvers. To test the applicability of SNM in motion planning, we propose a simple online planner that uses SNM as a heuristic to switch between a general and a linearization-based solver. Results on a car-like robot with second order dynamics and 4-DOFs and 7-DOFs torque-controlled manipulators indicate that SNM can appropriately decide if and when a linearization-based solver should be used.

Contact State Estimation for Peg-in-Hole Assembly Using Gaussian Mixture Model

Recently, the robotic assembly has been expanded into an unstructured environment. This environment includes uncertainties that may cause unexpected situations such as a failure of the assembly. Such problems can be prevented or monitored by a robust contact state (CS) estimation method. In that sense, the paper suggests a CS estimation method that contains a torque indicator, a position/velocity indicator, and a CS discriminator. Using joint torque of manipulators and position/velocity of the end-effector, a Gaussian Mixture Model (GMM) builds each indicator by reflecting on two properties of measured data, i.e., non-stationary behavior and correlation among the data. The indicators play a role to indicate the corresponding sensor state. The discriminator is defined by rules which combine the results of the indicators, allowing a robust CS estimation to be achieved. In this respect, the proposed method has a distinct advantage over existing distance-based clustering methods which ignore probabilistic properties or correlation among measured data. The performance of the estimation is demonstrated through experiments with torque-controlled manipulators and commercial prefabricated furniture.

Enabling impedance-based physical human–multi–robot collaboration: Experiments with four torque-controlled manipulators

Robotics research into multi-robot systems so far has concentrated on implementing intelligent swarm behavior and contact-less human interaction. Studies of haptic or physical human-robot interaction, by contrast, have primarily focused on the assistance offered by a single robot. Consequently, our understanding of the physical interaction and the implicit communication through contact forces between a human and a team of multiple collaborative robots is limited. We here introduce the term Physical Human Multi-Robot Collaboration (PHMRC) to describe this more complex situation, which we consider highly relevant in future service robotics. The scenario discussed in this article covers multiple manipulators in close proximity and coupled through physical contacts. We represent this set of robots as fingers of an up-scaled agile robot hand. This perspective enables us to employ model-based grasping theory to deal with multi-contact situations. Our torque-control approach integrates dexterous multi-manipulator grasping skills, optimization of contact forces, compensation of object dynamics, and advanced impedance regulation into a coherent compliant control scheme. For this to achieve, we contribute fundamental theoretical improvements. Finally, experiments with up to four collaborative KUKA LWR IV+ manipulators performed both in simulation and real world validate the model-based control approach. As a side effect, we notice that our multi-manipulator control framework applies identically to multi-legged systems, and we execute it also on the quadruped ANYmal subject to non-coplanar contacts and human interaction.

A force-sensorless approach to collision detection based on virtual powers

A collision detection approach for torque-controlled manipulators is proposed to detect and isolate a collision in an unknown environment without using external sensors. A set of artificial indexes physically representing instantaneous powers are introduced. A contact link can be detected and isolated by finding the smallest power index based on full knowledge of the link parameters. However, it is difficult to precisely obtain the link parameters. Therefore, we propose a robust way in which the collision detection and isolation processes are separated. A collision can be detected by comparing a power-based index with a unique threshold, and a contact link can be isolated by comparing power indexes without any additional threshold. The statistical simulation results using a 6 degree of freedom (DOF) spatial manipulator show the performance of the proposed approach in an ideal situation. Furthermore, the statistical experimental results using the 2- and 3-DOF planar manipulators validate the robustness of the proposed approach.

Toward fully autonomous mobile manipulation for industrial environments

This work presents a concept for autonomous mobile manipulation in industrial environments. Utilizing autonomy enables an unskilled human worker to easily configure a complex robotics system in a setup phase before carrying out fetch and carry operations in the execution phase. In order to perform the given tasks in real industrial production sites, we propose a robotic system consisting of a mobile platform, a torque-controlled manipulator, and an additional sensor head. Multiple sensors are attached which allow for perception of the environment and the objects to be manipulated. This is essential for coping with uncertainties in real-world application. In order to provide an easy-to-use and flexible system, we present a modular software concept which is handled and organized by a hierarchical flow control depending on the given task and environmental requirements. The presented concept for autonomous mobile manipulation is implemented exemplary for industrial manipulation tasks and proven by real-world application in a water pump production site. Furthermore, the concept has also been applied to other robotic systems and other domains for planetary exploration with a rover.

Gesteme-free context-aware adaptation of robot behavior in human-robot cooperation.

Cooperative robotics is receiving greater acceptance because the typical advantages provided by manipulators are combined with an intuitive usage. In particular, hands-on robotics may benefit from the adaptation of the assistant behavior with respect to the activity currently performed by the user. A fast and reliable classification of human activities is required, as well as strategies to smoothly modify the control of the manipulator. In this scenario, gesteme-based motion classification is inadequate because it needs the observation of a wide signal percentage and the definition of a rich vocabulary. In this work, a system able to recognize the user's current activity without a vocabulary of gestemes, and to accordingly adapt the manipulator's dynamic behavior is presented. An underlying stochastic model fits variations in the user's guidance forces and the resulting trajectories of the manipulator's end-effector with a set of Gaussian distribution. The high-level switching between these distributions is captured with hidden Markov models. The dynamic of the KUKA light-weight robot, a torque-controlled manipulator, is modified with respect to the classified activity using sigmoidal-shaped functions. The presented system is validated over a pool of 12 näive users in a scenario that addresses surgical targeting tasks on soft tissue. The robot's assistance is adapted in order to obtain a stiff behavior during activities that require critical accuracy constraint, and higher compliance during wide movements. Both the ability to provide the correct classification at each moment (sample accuracy) and the capability of correctly identify the correct sequence of activity (sequence accuracy) were evaluated. The proposed classifier is fast and accurate in all the experiments conducted (80% sample accuracy after the observation of ∼450ms of signal). Moreover, the ability of recognize the correct sequence of activities, without unwanted transitions is guaranteed (sequence accuracy ∼90% when computed far away from user desired transitions). Finally, the proposed activity-based adaptation of the robot's dynamic does not lead to a not smooth behavior (high smoothness, i.e. normalized jerk score <0.01). The provided system is able to dynamic assist the operator during cooperation in the presented scenario.

Passivation of Projection-Based Null Space Compliance Control Via Energy Tanks

A manipulator with kinematic redundancy w.r.t. a main task enables to perform an additional, subordinate task in its null space. The classical hierarchy-based approach is to apply dynamically consistent null space projections such that this lower-priority task does not disturb the main task. The major problem is that the control task hierarchy is established at the expense of the passivity of the system, which causes problems in terms of stability, robustness, and safety. Here, we extend this classical approach and passivate the closed-loop system by means of an energy tank, which is filled by the dissipated power of the active damping on both hierarchy levels. We validate the method in simulations and experiments on a torque-controlled manipulator. The concept is predestined for physical human-robot interaction and safety-relevant applications, where passivity must be guaranteed.

Design of a torque-controlled manipulator to analyse the admittance of the wrist joint

This paper describes the design of a torque-controlled manipulator to identify the dynamics of the wrist joint. With torque disturbances, the subject can actively control the joint angle, giving a natural task. The application of a hybrid haptic controller guarantees linearity over a large bandwidth and adjustable virtual dynamics. The haptic controller has a bandwidth of 50 Hz, meaning that the virtual dynamics are realistically felt at up to 50 Hz. To let the subject ‘feel’ the torque, disturbances as well as possible the apparent, or virtual, dynamics of the device must be small. The minimal apparent inertia of the device is 1.6 g m 2, which is of the same order as for a normal wrist, and the minimal damping and stiffness are negligible. To judge the accuracy of the manipulator, loads of known physical properties were attached and their parameters were quantified. The parameters of the loads were estimated with a maximum error of 5%. As the eigenfrequency of a (co)-contracted human wrist is approximately 15 Hz, the 50-Hz bandwidth of the haptic device is sufficient to measure all relevant dynamics of the human wrist. With this device, the dynamics of the human wrist joint can be measured under varying virtual dynamics, as well as the effect of neurological dysfunction on human motor control, for example.

Adaptive Compliant Motion Control for Dexterous Manipulators

This article presents two adaptive schemes for compliant mo tion control of dexterous manipulators. The first scheme is developed using an adaptive impedance control approach for torque-controlled manipulators, whereas the second strategy is an adaptive admittance controller for position-controlled manipulators. The proposed controllers are very general and computationally efficient, as they do not require knowledge of the manipulator dynamic model or parameter values of the manipulator or the environment and are implemented without calculation of the inverse dynamics or inverse kinematic trans formation. It is shown that the control strategies are globally stable in the presence of bounded disturbances and that in the absence of disturbances the ultimate bound on the size of the system errors can be made arbitrarily small. The capabilities of the proposed control schemes are illustrated through both computer simulations and laboratory experiments with a dex terous Robotics Research Corporation seven-degrees-of-freedom (DOF) manipulator.

Decentralized adaptive compliance control of robot manipulators

This paper presents two decentralized adaptive schemes for controlling the end-effector compliance of robot manipulators. The first scheme is developed using an adaptive impedance control approach for torque-controlled manipulators, while the second strategy is an adaptive admittance controller for position-controlled manipulators. The proposed controllers are very general and computationally efficient since they do not require knowledge of the manipulator dynamic model or parameter values of the manipulator or the environment. It is shown that the control strategies are globally stable in the presence of bounded disturbances, and that in the absence of disturbances the ultimate bound on the size of the system errors can be made arbitrarily small. The capabilities of the proposed control schemes are illustrated though laboratory experiments with a Robotics Research Corporation seven degree-of-freedom (DOF) manipulator.