Task-space Control Research Articles

Tip and vibration control of space robots using estimated flexible coordinates

This paper provides an extension to previous work on end-effector control of flexible space manipulators. Those works considered the use of a special output called the μ-tip rate for feedback control of desired end-effector trajectories with simultaneous vibration control. Implementation of this special output requires measurement of end-effector position or the use of flexible forward kinematics to determine it. For the latter, one requires measurements of the joint angles and flexible coordinates. The second of these is difficult to measure in space scenarios, so this paper looks at the use of an estimation scheme to approximate it and use it in a task-space control law. Multiple simulations are conducted to investigate the use of these approximated elastic coordinates in robustly controlling a one-link and two-link flexible manipulator with a payload mass. The error between desired and actual trajectory is calculated, and the results are juxtaposed with results from a joint-space feedback scheme. There is an emphasis on comparing the estimated elastic coordinates with the actual simulated coordinates. Using the estimated elastic coordinates to determine the end-effector location via forward kinematics, yielded similar results to when the actual elastic coordinates were used. Overall, the estimation equation used is shown to provide reasonable end-effector tracking results with the end-effector being able to track various types of trajectories.

Learning to Control Complex Robots Using High-Dimensional Body-Machine Interfaces.

When individuals are paralyzed from injury or damage to the brain, upper body movement and function can be compromised. While the use of body motions to interface with machines has shown to be an effective noninvasive strategy to provide movement assistance and to promote physical rehabilitation, learning to use such interfaces to control complex machines is not well understood. In a five session study, we demonstrate that a subset of an uninjured population is able to learn and improve their ability to use a high-dimensional Body-Machine Interface (BoMI), to control a robotic arm. We use a sensor net of four inertial measurement units, placed bilaterally on the upper body, and a BoMI with the capacity to directly control a robot in six dimensions. We consider whether the way in which the robot control space is mapped from human inputs has any impact on learning. Our results suggest that the space of robot control does play a role in the evolution of human learning: specifically, though robot control in joint space appears to be more intuitive initially, control in task space is found to have a greater capacity for longer-term improvement and learning. Our results further suggest that there is an inverse relationship between control dimension couplings and task performance.

Optimal robust online tracking control for space manipulator in task space using off-policy reinforcement learning

This study addresses the demands for adaptability, uncertainty management, and high performance in the control of space manipulators, and the inadequacies in achieving optimal control and handling external uncertainty in task space in previous research. Based on off-policy reinforcement learning, a model-free and time-efficient method for online robust tracking control in task space is devised. To address the complexity of dynamic equations in task space, a mixed-variable approach is adopted to transform the multivariable coupled time-varying problem into a single-variable problem. Subsequently, the optimal control policy is derived with the disturbance convergence, stability, and optimality of the control method being demonstrated. This marks the first instance of achieving robust optimal tracking control in task space for space manipulators. The efficacy and superiority of the presented algorithm are validated through simulation.

Development of 3D printed mecanum mobile manipulator and task-space control

Development of 3D printed mecanum mobile manipulator and task-space control

Gain scheduled task space control of multi DOF machine tools with non-linear parallel kinematics

Machine tools with multiple degrees of freedom and parallel kinematics offer tremendous opportunities for new kind of processes but at the same time also a lot of control challenges. They are subject to uncertainties in the form of kinematic errors, elasticities and thermal deflections. Their kinematic non-linearity requires the application of model-based control concepts whose full effectiveness can only be unlocked through controls in the task space. In particular, kinematic singularities occur in forming machines such as presses, which make closed-loop control at corresponding operating points considerably more difficult. In this paper, we use the example of a servo-driven press with three ram degrees of freedom to show how parallel kinematic machine tools can be controlled via inverse differential kinematics models in task space and to what extent the controls can benefit from scheduled gain factors. The results are validated both simulatively and experimentally. In the experimental tests, three-dimensional tool paths are traced that can be used to carry out orbital forging processes. Furthermore, the example of a punch hole rolling process demonstrates that the ram pose is controlled more accurately, which also has positive effects on the resulting workpiece geometry. The performance of the developed gain scheduling task space control is compared with that of a robust and non-robust control with static gains.

Safe and compliant control of redundant robots using superimposition of passive task-space controllers

Animals are capable of robust and reliable control in unstructured environments, where they effortlessly overcome the uncertainty of interaction and are capable of exploiting singularities. These conditions are a well-known challenge for robots due to the limitations of projected dynamics, which requires accurate modelling and is susceptible to singularities. This work proposes a compliant passive control method for redundant manipulators based on a superimposition of multiple passive task-space controllers in a hierarchy without requiring any knowledge of the robot dynamics. The proposed control framework of passive controllers is inherently stable, numerically well-conditioned (as no matrix inversions are required), and computationally inexpensive (as no optimisation is used). This method leverages and introduces a novel stiffness profile for a recently proposed passive controller with smooth transitions between the divergence and convergence phases making it particularly suitable when multiple passive controllers are combined through superimposition. The experimental results demonstrate that the proposed method achieves sub-centimetre tracking performance during demanding dynamic tasks with fast-changing references, while remaining safe to interact with and robust to singularities. The data further show that the robot can fully take advantage of the redundancy to maintain the primary task accuracy while compensating for unknown environmental interactions, which is not possible from current frameworks that require accurate contact information.

Hierarchical control of manipulator with null-space compliance at the kinematic level

This article proposes a hierarchical control framework with null-space impedance at the kinematic level. The proposed framework aims to overcome the limitations of the prior methods for null-space compliance which rely on the dynamics of the manipulator. Two task-space control laws and a null-space control law at the kinematic level are designed to improve task-space errors and exhibit compliant joint motion behavior. The task-space’s end-effector force tracking uses an adaptive variable impedance control approach to enhance tracking performance. Experimental results conducted on a 7R manipulator demonstrate that the proposed control framework is capable of improving task performance when interaction forces act on the manipulator body.

Task space control of the robot manipulators with adaptive fuzzy global fast terminal sliding mode control in presence of dynamic and kinematic uncertainties

SummaryBasic features such as convergence time and speed, number‐action control coefficients, free chattering, and proof of stability are significant in the design process and sliding mode control (SMC) efficiency. In this article, we propose an adaptive fuzzy global fast terminal SMC (AFGFTSMC) to handle the mentioned features in the task space control of the robot manipulator in the presence of dynamic and kinematic uncertainties. First, perturbed joint space dynamic equations of the system are transferred to task space, and a broad range of uncertainties are considered there. Then, a global fast terminal SMC (GFTSMC) is proposed for robot manipulators in task space, in which a flexible sliding surface improves the convergence time. Next, to have an intelligent adjustment of the sliding surface coefficients, which leads to a much faster convergence rate, a fuzzy approximator with just seven fuzzy rules is presented. In the following, to access the boundaries of the existing uncertainties, an adaptive fuzzy approximator is proposed, which has five fuzzy rules and only one adaptive law, increases the system's robustness, and eliminates the effect of chattering. Mathematical proof shows that the task space closed‐loop control system under the proposed AFGFTSMC and in the presence of dynamic and kinematic uncertainties has a finite‐time global asymptotic stability. The theoretical evidence and simulation results, which are conducted on a 2‐link robot manipulator, confirm the good efficiency of the proposed controller.

Compact Design and Task Space Control of a Robotic Transcatheter Delivery System for Mitral Valve Implant.

Mitral regurgitation (MR) is one of the most common valvular abnormalities, and the gold-standard for treatment is surgical mitral valve repair/replacement. Most patients with severe MR are over the age of 75, which makes open-heart surgery challenging. Thus, minimally invasive surgeries using transcatheter approaches are gaining popularity. This paper proposes the next generation of a robotic transcatheter delivery system for the mitral valve implant that focuses on the design of the actuation system, modeling, and task space control. The proposed actuation system is compact while still enabling bidirectional torsion, bending, and prismatic joint motion. A pulley structure is employed to actuate the torsion and bending joints using only one motor per joint in conjunction with an antagonistic passive spring to reduce tendon slack. The robotic transcatheter is also optimized to increase its stability and reduce bending deflection. An inverse kinematics model (with an optimization algorithm), singularity analysis method, and joint hysteresis and compensation model are developed and verified. Finally, a task space controller is also proposed. Experiments, including trajectory tracking and demonstrations of the robot motion in an ex vivo porcine heart and a phantom heart through a tortuous path are presented.

Hybrid vision/strain-based control strategy for a parallel manipulator with flexible links

Parallel manipulators may present higher speed/acceleration ratios and energy efficiency when compared with serial manipulators. In addition, reducing their components’ inertia can further improve performance. However, this design alternative might yield vibrations requiring novel joint and task space control strategies. While the former involves precise models, the latter might require adequate computation vision schemes. These requirements impose critical challenges. This manuscript proposes a hybrid control strategy and experimentally investigates using a 3RRR prototype with flexible links. This hybrid strategy comprises two control loops: position-based visual servo and strain-based feedback control schemes. The strain-based loop uses a sliding mode control and the time derivative of the signals obtained by strain gauges installed in the flexible links. Compared with the position-based visual servo scheme, the hybrid strategy can considerably attenuate the vibrations since the approach reduced the overshoot response by 36% and the maximum value of the Euclidean error on position by 38%, on average.

Trajectory tracking control of a pneumatically actuated continuum manipulator in the presence of obstacles by using terminal sliding mode control

This paper proposes an efficient trajectory planning and dynamic tracking controller scheme for a pneumatic continuum manipulator under the effect of material hysteresis. First of all, generalized governing nonlinear dynamic equations in the form of partial differential equations for pneumatic continuum manipulator dynamics are developed by using discrete Cosserat-rod theory, where the manipulator material hysteresis is modeled by using a fractional order Bouc–Wen model. Then, the trajectory planning for the end-effector of a continuum manipulator is proposed, which accounts for the static obstacles in the workspace and the Jacobian singularity. Subsequently, an adaptive terminal sliding mode controller for the joint space control combined with a simple PI controller for task space control is proposed. The proposed controller guarantees exponential convergence of the manipulator tip positional error in finite time, even in the existence of external disturbances and model uncertainties, without any need for prior knowledge of their upper bounds. Finally, the proposed controller is applied to a two-segment continuum manipulator, the trunk of Robotino-XT, through numerical simulations and the performance gain over two controllers proposed in the literature for similar pneumatic continuum manipulators is demonstrated.

Task space control of ram poses of multipoint Servo Presses

Robotic manipulators are known for their numerous degrees of freedom with which they can manipulate objects in task space. With the help of model-based control concepts, it is possible to precisely closed-loop control their pose, thus meeting high positioning accuracy requirements. As a result, they are ideally suited for carrying out flexible activities in manufacturing processes in which freely programmable tool paths are required, such as pick and place, welding or assembly tasks. If high forces have to be applied in manufacturing processes, as is the case in bulk forming processes, robots can no longer be used due to low stiffness and moderate force provision. The development of flexible forming machines with several degrees of freedom overcomes this problem. Machine tools such as the 3D Servo Press have three ram degrees of freedom and can provide forces of up to 1600 kN, but require highly accurate position control in task space. For this reason, this paper shows how control concepts from robotics can be applied to machine tools with high stiffness, high provided forces and multiple degrees of freedom. The functionality of the control is validated using the example of upsetting tests in which off-center forces are applied to the ram and the control counteracts tilting of the ram.

Task space control for on-orbit space robotics using a new ROS-based framework

This paper proposes several task space control approaches for complex on-orbit high degrees of freedom robots. These approaches include redundancy resolution and take the non-linear dynamic model of the on-orbit robotic systems into account. The suitability of the proposed task space control approaches is explored in several on-orbit servicing operations requiring visual servoing tasks of complex humanoid robots. A unified open-source framework for space-robotics simulations, called OnOrbitROS, is used to evaluate the proposed control systems and compare their behaviour with state-of-the-art existing ones. The adopted framework is based on ROS and includes and reproduces the principal environmental conditions that eventual space robots and manipulators could experience in an on-orbit servicing scenario. The architecture of the different software modules developed and their application on complex space robotic systems is presented. Efficient real-time implementations are achieved using the proposed OnOrbitROS framework. The proposed controllers are applied to perform the guidance of a humanoid robot. The robot dynamics are integrated into the definition of the controllers and an analysis of the results and practical properties are described in the results section.

An adaptive continuous sliding mode feedback linearization task space control for robot manipulators

Two main domains have been followed; joint space control and operational (task) space control for robot manipulators. Task space control is simpler than joint space control as it avoids tackling the robot inverse kinematics which is very complex and challenging. However, in order to design robust and accurate controllers in operational space, both kinematics and dynamics uncertainties must be considered. Additionally, singularities that may occur while mapping between joint space and task space should be addressed. To tackle these issues, a new adaptive task space control approach is developed in this paper. The developed approach is able to overcome uncertainties and mitigate the chattering that commonly occur during the control process. The introduced control system has been proven to be globally stable using a designed Lyapunov function. The simulation results show that the suggested model is able to control the robot manipulators efficiently even in the existence of external disturbances.

Damping Ratio Prediction for Redundant Cartesian Impedance-Controlled Robots Using Machine Learning Techniques

Implementing impedance control in Cartesian task space or directly at the joint level is a popular option for achieving desired compliance behavior for robotic manipulators performing tasks. The damping ratio is an important control criterion for modulating the dynamic response; however, tuning or selecting this parameter is not easy, and can be even more complicated in cases where the system cannot be directly solved at the joint space level. Our study proposes a novel methodology for calculating the local optimal damping ratio value and supports it with results obtained from five different scenarios. We carried out 162 different experiments and obtained the values of the inertia, stiffness, and damping matrices for each experiment. Then, data preprocessing was carried out to select the most significant variables using different criteria, reducing the seventeen initial variables to only three. Finally, the damping ratio values were calculated (predicted) using automatic regression tools. In particular, five-fold cross-validation was used to obtain a more generalized model and to assess the forecasting performance. The results show a promising methodology capable of calculating and predicting control parameters for robotic manipulation tasks.

A neural flexible PID controller for task-space control of robotic manipulators.

This paper proposes an adaptive robust Jacobian-based controller for task-space position-tracking control of robotic manipulators. Structure of the controller is built up on a traditional Proportional-Integral-Derivative (PID) framework. An additional neural control signal is next synthesized under a non-linear learning law to compensate for internal and external disturbances in the robot dynamics. To provide the strong robustness of such the controller, a new gain learning feature is then integrated to automatically adjust the PID gains for various working conditions. Stability of the closed-loop system is guaranteed by Lyapunov constraints. Effectiveness of the proposed controller is carefully verified by intensive simulation results.

Humanoid Loco-Manipulations Using Combined Fast Dense 3D Tracking and SLAM With Wide-Angle Depth-Images

To efficiently achieve complex humanoid loco-manipulation tasks in industrial contexts, we propose a combined vision-based tracker-localization interplay integrated as part of a task-space whole-body optimization control. To achieve good perception complementarity between manipulation and localization, a new fast dense 3D model-based tracking using wide-angle depth image is developed and used in conjunction with a simultaneous localization and mapping software. Our approach allows humanoid robots, targeted for industrial manufacturing, to manipulate and assemble large-scale objects while walking. It is assessed with experiments consisting in rolling and assembling in an unwinder a heavy and wide bobbin using bimanual grasping and bipedal locomotion at a time. This experimental use-case is found in some large-scale manufacturing where bobbins are enrolled with various materials (cables, papers, rubbers, etc.). The same experiments are made using two different humanoid robots of the same family. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper aims at deploying humanoid robots in large-scale manufacturing industries. We consider non-added value tasks related to transporting large tools or objects such as large bobbins by means of locomanipulation skills, similarly to human workers. We developed a task-space control framework that has been successfully applied in the aircraft industry. In the frame of a current collaboration with other major industrial sectors, we enhanced our control framework to interplay between SLAM and visual tracking to realize robust loco-manipulation tasks. Our approach can be applied and ported to any humanoid robot or bi-manual wheeled mobile robots with minor programming effort as the software is made open. Preliminary experiments with two different humanoids and use-cases suggest that our approach is feasible. In future research, we will address the problem of performance to reach at least human-speed in the execution of locomanipulation tasks in large-scale industry and automation contexts.

Dynamic Task Space Control Enables Soft Manipulators to Perform Real‐World Tasks

Dynamic motions are a key feature of robotic arms, enabling them to perform tasks quickly and efficiently. A majority of real‐world soft robots rely on a quasistatic control approach, without taking dynamic characteristics into consideration. As a result, robots with this restriction move slowly and are not able to adequately deal with forces, such as handling unexpected perturbations or manipulating objects. A dynamic approach to control and modeling would allow soft robots to move faster and handle external forces more efficiently. In previous studies, different aspects of modeling, dynamic control, and physical implementation have been examined separately, while their combination has yet to be thoroughly investigated. Herein, the accuracy of the dynamic model of the multisegment continuum robot is improved by adding new elements that include variable stiffness and actuation behavior. Then, this improved model is integrated with state‐of‐the‐art system identification and dynamic task space control and experiments are performed to validate this combination on a real‐world manipulator. As a means of encouraging future research on dynamic control for soft robotic manipulators, the source code is made available for modeling, control, and system identification, along with the recipes for fabricating the manipulator.

Modeling and Synchronized Control of a Dual-Drive “Checkerboard” Gantry With Composite Adaptive Feedforward and RISE Feedback

In this article, the design of a novel dual-drive “checkerboard” gantry is introduced with symmetric layout, rigid structure, and decoupled X–Y motion. However, its double redundantly actuated configuration and the strong interaxis coupling force challenges the coordinate motion and real-time force distribution between parallel axes. With consideration of stiffness of bearings, the dynamical model revealing the interaxis coupling effect due to asymmetric load from the moving platform is established. Thereby, the force allocation between pairs of parallel axes is naturally solved by the upcoming controller design. To compensate disturbance due to both structural and unstructural uncertainties, a 2-degree-of-freedom control scheme is proposed. It takes both tracking error and the model prediction error to update the dynamical model in real time, thus achieves an effective feedforward control without setting up the deadzone. Meanwhile, a rule is developed to tune the proportional-integral-derivative feedback term in the task-space control from its joint-space counterpart. Additionally, the robust integral of signum of error term is proposed with gain adaptation so that bandwidth of control is fine adjusted according to the velocity tracking error. Real-time experiments on the actual testbed indicate that both improvement of planar motion precision and prevention of parameter drifting over a large workspace are successfully achieved compared with the conventional control methods.

Task-space control for industrial robot manipulators with unknown inner loop control architecture

The operational space control of a robot manipulator using external sensors requires stabilizing the compound system {external sensors - outer controller - inner controller - robot manipulator}. The user must access the inner controller to reshape it to achieve this stabilization. Due to intellectual property protection purposes, most industrial robots have an unknown or inaccessible inner controller. Therefore, it is tricky to design a stable control scheme. To solve this problem, an adaptive radial basis function neural network (RBF NN) outer controller is proposed, which approximates the inner controller’s dynamics to eliminate its effect in the closed-loop. An inherent property for RBF NN is used to reduce the number of adaptive parameters. Since this technique introduces approximation errors, it is included in the control scheme, a term that constrains the system to converge rapidly to the performances prescribed by the user. It is proved that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB) through Lyapunov theory. The effectiveness of the proposed approach is verified through simulation comparisons and experimental studies.