A Comparative Experimental Study of Multi-Tasking Tracking and Interaction Control on a Torque-Controlled Humanoid Robot

Multi-tasking control exploits kinematic redundancy of robots to attain several control objectives at the same time. To properly coordinate the subtasks according to their importance, they are usually stacked into a prioritized hierarchy. In this work, two passivity-based multi-tasking control strategies developed in our recent work that feature strict prioritization and mathematically proved stability properties, are experimentally compared with a state-of-the-art method using feedback linearization on a torque-controlled humanoid robot. The conducted experimental study aims at providing insights into the practical properties of the controllers in real-world scenarios whence the robot has to execute a mixture of trajectory tracking and physical interaction tasks

Passive Decoupled Multi-Task Controller for Redundant Robots

Kinematic redundancy in robots makes it possible to execute several control tasks simultaneously. As some tasks are usually more important than others, it is reasonable to dynamically decouple them in order to ensure their execution in a hierarchical way or even without any interference at all. The most widely used technique is to decouple the system by feedback linearization. However, that requires actively shaping the inertia and consequently modifying the natural dynamics of the robot. Here we propose a passivity-based multi-task tracking controller that preserves these inertial properties but fully compensates for task-space cross-couplings using external force feedback. Additionally, three formal proofs are provided: uniform exponential stability for trajectory tracking, passivity during physical interaction, and input-to-state-stability. The controller is validated in simulations and experiments and directly compared with the hierarchical PD+ approach and the feedback linearization. The proposed approach is well suited for safe physical human-robot interaction and dynamic trajectory tracking if measurements or estimations of the external forces are available

Adaptive Tracking Control with Uncertainty-aware and State-dependent Feedback Action Blending for Robot Manipulators

Adaptive control can significantly improve tracking performance of robot manipulators subject to modeling errors in dynamics. In this letter, we propose a new framework combining the composite adaptive controller using a natural adaptation law and an extension of the adaptive variance algorithm (AVA) for controller blending. The proposed approach not only automatically adjusts the feedback action to reduce the risk of violating actuator constraints but also anticipates substantial modeling errors by means of an uncertainty measure, thus preventing severe performance deterioration. A formal stability analysis of the closed-loop system is conducted. The control scheme is experimentally validated and directly compared with baseline methods on a torque-controlled KUKA LWR IV+

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

Research Progress and Trends in Metabolomics of Fruit Trees

Metabolomics is an indispensable part of modern systems biotechnology, applied in the diseases’ diagnosis, pharmacological mechanism, and quality monitoring of crops, vegetables, fruits, etc. Metabolomics of fruit trees has developed rapidly in recent years, and many important research results have been achieved in combination with transcriptomics, genomics, proteomics, quantitative trait locus (QTL), and genome-wide association study (GWAS). These research results mainly focus on the mechanism of fruit quality formation, metabolite markers of special quality or physiological period, the mechanism of fruit tree’s response to biotic/abiotic stress and environment, and the genetics mechanism of fruit trait. According to different experimental purposes, different metabolomic strategies could be selected, such as targeted metabolomics, non-targeted metabolomics, pseudo-targeted metabolomics, and widely targeted metabolomics. This article presents metabolomics strategies, key techniques in metabolomics, main applications in fruit trees, and prospects for the future. With the improvement of instruments, analysis platforms, and metabolite databases and decrease in the cost of the experiment, metabolomics will prompt the fruit tree research to achieve more breakthrough results

Toward Seamless Transitions Between Shared Control and Supervised Autonomy in Robotic Assistance

Assistive robots aim to help humans with impairments execute motor tasks in everyday household environments. Controlling the end-effector of such robots directly, for instance with a joystick, is often cumbersome. Shared control methods, like Shared Control Templates (SCTs) [1] , have therefore been proposed to provide support for robotic control. Moreover, depending on factors such as workload, system trust or engagement, users may like to freely adjust the level of autonomy, for instance by letting the robot complete a task by itself. In this letter, we present a concept for adjustable autonomy in the context of robotic assistance. We extend the SCT approach with an automatic control module that allows the user to switch between Shared Control and Supervised Autonomy at any time during task execution. As both support modes use the same action representation, transitions are seamless. We show the capabilities of this approach in a set of daily living tasks with our wheelchair-mounted robot EDAN and our humanoid robot Rollin? Justin. We highlight how automatic execution benefits from SCT features, like task-related constraints and whole-body control

Altered Posterior Cerebellar Lobule Connectivity With Perigenual Anterior Cingulate Cortex in Women With Primary Dysmenorrhea

Objectives: This study aimed to investigate the potential connectivity mechanism between the cerebellum and anterior cingulate cortex (ACC) and the cerebellar structure in primary dysmenorrhea (PDM).Methods: We applied the spatially unbiased infratentorial template (SUIT) of the cerebellum to obtain anatomical details of cerebellar lobules, upon which the functional connectivity (FC) between the cerebellar lobules and ACC subregions was analyzed and the gray matter (GM) volume of cerebellar lobules was measured by using voxel-based morphometry (VBM) in 35 PDM females and 38 age-matched healthy females. The potential relationship between the altered FC or GM volume and clinical information was also evaluated in PDM females.Results: PDM females showed higher connectivity between the left perigenual ACC (pACC) and lobule vermis_VI, between the left pACC and left lobule IX, and between right pACC and right cerebellar lobule VIIb than did the healthy controls. Compared with healthy controls, no altered GM volume was found in PDM females. No significant correlation was found between altered cerebellum–ACC FC and the clinical variables in the PDM females.Conclusion: PDM females have abnormal posterior cerebellar connectivity with pACC but no abnormal structural changes. ACC–cerebellar circuit disturbances might be involved in the PDM females

Whole-Body Teleoperation and Shared Control of Redundant Robots with Applications to Aerial Manipulation

This paper introduces a passivity-based control framework for multi-task time-delayed bilateral teleoperation and shared control of kinematically-redundant robots. The proposed method can be seen as extension of state-of-the art hierarchical whole-body control as it allows for some of the tasks to be commanded by a remotely-located human operator through a haptic device while the others are autonomously performed. The operator is able to switch among tasks at any time without compromising the stability of the system. To enforce the passivity of the communication channel as well as to dissipate the energy generated by the null-space projectors used to enforce the hierarchy among the tasks, the Time-Domain Passivity Approach (TDPA) is applied. The efficacy of the approach is demonstrated through its application to the DLR Suspended Aerial Manipulator (SAM) in a real telemanipulation scenario with variable time delay, jitter, and package loss

Passivation of a Hierarchical Whole-Body Controller for Humanoid Robots

Humanoid robots for households and service robotic applications are essentially required to be capable of performing multiple tasks in unstructured, unpredictable environments where the robots share their workspace with humans. In this context, guaranteeing a safe and stable interactive behavior during task execution is of paramount importance for these humanoid robots. For the regulation case, by the use of torque control methods that take the active control of the interactive behavior into account such as compliance control, the passivity of the robot with respect to its connected environment is ensured, which is a necessary condition for a stable interaction between the robot and any passive environment. The state-of-the-art multi-objective compliance controller, that is able to implement a strict hierarchy with an arbitrary number of task levels, applies compliance controllers on individual levels for task execution. However, the null space projections used to achieve task hierarchies inevitably destroy the passivity property of the original compliance controller. In this thesis, the source of activity of the classical controller as well as the decoupled closed-loop system are analyzed. Based on the knowledge of the clarified source of activity, an energy-tank passivation method for the classical controller with a two-level task hierarchy is extended to the case of a task hierarchy with an arbitrary number of task levels. Two variations of the energy-tank passivation approaches are presented: the local-energy-tank approach and the global-energy-tank approach. Formal proofs of passivity of the overall system with both approaches are provided. Proofs of additional approach-specific passivity properties are also given. Simulations with a planar manipulator and experiments with the humanoid robot Rollin’ Justin of the DLR validate the theoretical results

Adaptive Passivity-based Multi-Task Tracking Control for Robotic Manipulators

Adaptive control of robot manipulators based on the use of a sliding variable and the passivity property of the dynamic equations was originally designed and successfully applied to task tracking in joint space few decades ago. Surprisingly, no extension is available to date for the multi-task case such that the controller tends towards enforcing strict task priorities as the parameters tend to their real values. Given the importance of both multi-task control and adaptive algorithms, an approach that deals with this situation has an important impact in the robotic field. This letter provides a solution to this problem leveraging on our recent formulation of hierarchical multi-task impedance control for trajectory tracking and on geometric methods for model identification. Experiments are used to validate the stability analysis