Task-space Control Research Articles

Fuzzy Approximation-Based Task-Space Control of Robot Manipulators With Remote Center of Motion Constraint

The presence of unknown physical interaction between the patients’ body and surgical tool in laparoscopic surgery requires a secure end-effector positioning while assuring a reliable constraint motion. In this work, a task-space control approach based on fuzzy approximation is proposed for a teleoperated surgery scenario utilizing a serial redundant robot manipulator (7 degrees of freedom), the motions of which are constrained with respect to a point known as remote center of motion (RCM). The dynamical uncertainties due to the physical interaction are considered and estimated to maintain operational accuracy by introducing the decoupled adaptive fuzzy approximation technique. Experiments verify the presented approach’s effectiveness. Results indicate that with the proposed control method, the accuracy of the surgical operation is improved while the safety can be ensured for teleoperated surgery with RCM constraint.

Unified Method for Task-Space Motion/Force/Impedance Control of Manipulator With Unknown Contact Reaction Strategy

Nowadays, higher requirements are placed on the design of the controller when robots enter the factory and co-operate with human. In this letter, we propose a unified task space control method combined with the contact reaction strategy in null space of redundant manipulator, which can not only conveniently switch between different control objectives according to specific tasks, but also react to the unknown contact with compliance. Specifically, the unknown contact force applied on the manipulator’s body is online estimated based on recursive least square estimation law in joint space. Then, the closed-loop dynamics of different control objectives are unified in task space by a generalized target model, and a model-reaching law based on adaptive robust control is proposed to achieve the target model even if there exists an unknown contact. Meanwhile, the estimated contact force is not only compensated in the model-reaching control law to guarantee the correct task execution but also used for the null space impedance control design to ensure compliant behavior. The performance and stability of the proposed scheme are guaranteed and proved in theory. Experiments are conducted on a 7-DoF manipulator for the validation.

A Generalized Index for Fault-Tolerant Control in Operational Space Under Free-Swinging Actuation Failure

Actuation failure and fault-tolerant control under the actuation failure scenario have drawn more attention in accordance with the recent increasing demand for reliable robot control applications such for long-term and remote operation. The emergence of control torque loss, i.e., the free-swinging failure, is particularly challenging when the robot performs dynamic operational space tasks due to complexities stemming from redundancies in the kinematic structure as well as a dynamical disturbance in the under-actuated multi-body system. To reinforce robustness and accuracy of task-space control under the failure condition, this letter proposes a performance index, named generalized failure-susceptibility (GFS), which is formulated to render thorough dynamic and kinematic effects caused by the un-actuated joints. The GFS index is then exploited with the hierarchical task controller, where self-motion is controlled to minimize the index in real-time. Several experiments with a seven-degrees-of-freedom torque-controlled robot verify that the proposed control strategy with the GFS index effectively improves fault tolerance against anticipating actuation failure.

Task Space Model Predictive Control for Vineyard Spraying with a Mobile Manipulator

In this paper, a Model Predictive Control (MPC)-based approach for vineyard spraying is presented, able to adapt to different vine row structures and suitable for real-time applications. In the presented approach, the mobile base moves along a row of vines while the robotic arm controls the position and orientation of the spray nozzle. A reference lawnmower pattern trajectory is generated from the vine canopy description, with the aim of minimizing waste while ensuring vine coverage. MPC is used to compute the trajectory of the vehicle along the row and the manipulator tool trajectory, which follow the spray reference, while minimizing vehicle acceleration and tool displacement. The manipulator tool velocity commands provided by the MPC algorithm are tracked using task space control. The presented approach is evaluated in two experiments: a vineyard spraying scenario and an external evaluation scenario in an indoor environment equipped with the Optitrack camera system.

Adaptive Neural Task Space Control for Robot Manipulators With Unknown and Closed Control Architecture Under Random Vibrations

Robot manipulators are now used in various domains and environments, where they can be subjected to random vibrations. Random vibrations mainly affect the torque control signal, and a torque controller is therefore required to be designed for stabilization purposes. However, for security or intellectual property protection reasons, most commercialized robots are manufactured with unknown and inaccessible torque controller interface such that the user can only design a position/velocity controller. This paper proposes an adaptive task-space velocity controller free from the inner controller’s structure and exhibiting stochastic and deterministic disturbances rejection to deal with these issues. To deal with the unknown inner controller, the paper exploits the fact that most torque controllers use a velocity feedback term, and it considers the other terms as an unknown functions vector. To cope with random disturbances, it is demonstrated that the random excitation matrix can be linearly parameterized, and therefore, a direct adaptive method is constructed. Using radial basis function neural network (RBF NN), an indirect adaptive method is developed to cope with deterministic uncertainties. Through Lyapunov theory, the paper proves that all the closed-loop signals are bounded in probability. The effectiveness of the proposed approach is further demonstrated through simulation comparisons.

Predefined-time control for free-floating space robots in task space

This work mainly studies the position and attitude tracking control of a free-floating space robot. With the attitude represented in modified Rodrigues parameters (MRPs), a task-space controller with predefined-time stability is developed considering the external disturbance. The tuning parameters of a predefined-time controller can be formulated as functions of the prescribed upper bound of the stabilization time. Based on the backstepping technique and a novel predefined-time stabilizing function, a predefined-time control scheme is designed for the space robot system. Moreover, to avoid ’explosion of terms’, an auxiliary variable is introduced such that the controller is independent of the derivative of the virtual control law. Numerical simulations are presented to demonstrate the effectiveness of the proposed method.

Model-based joint and task space control strategies for a kinematically redundant parallel manipulator

AbstractOne alternative to overcome the presence of singularities within Parallel Manipulators’ workspace is kinematic redundancy. This design alternative can be realized by adding an extra active joint to a kinematic chain. Due to this addition, the IKM presents an infinite number of solutions requiring a redundancy resolution scheme. Moreover, Parallel Manipulators’ control may require complex strategies due to their coupled and complex dynamic and kinematic relations. In this work, a model-free, a joint space computed torque, and a hybrid joint-task-space computed torque control strategies are experimentally compared for a kinematically redundant parallel manipulator. The latter is a novel strategy that requires the measurement of the end-effector’s pose, which is performed by an eye-to-hand limited frame rate camera. The impact of up to three kinematic redundancy levels is also experimentally evaluated using prepositioning and ongoing positioning redundancy resolution schemes. The data are assessed by evaluating a prescribed trajectory executed using a planar kinematically redundant parallel manipulator. These results indicate that kinematic redundancy can not only be used as an alternative design for reducing the presence of singular regions, as claimed in the literature, but also be used along with model-based control strategies for improving dynamic performance and accuracy of parallel manipulators.

Adaptive Task-Space Force Control for Humanoid-to-Human Assistance

We envision a humanoid robot to serve as a source of additional motion-support forces in assistance for frail persons. In this context, we present a control strategy for a humanoid to adaptively regulate its assistive force contribution. First, we identify a human model torque control for optimal execution of a priori known motion task from sample recordings of this task performed by a healthy individual. We utilize the identified model in the proposed position discrepancy based observer of the human torque contribution, the unknown and unmeasurable variable. We propose an experience-based human contribution model learning strategy that allows to improve the human contribution estimate from trial-to-trial. The target assistive torque contribution is then calculated as the difference between the optimal torque required for the motion task and the estimated human contribution. The target assistive torque is integrated into a multi-robot quadratic programming task-space controller to compute the desired interaction force required for the robot to supply the necessary assistive torque for the human model. We use the non-optimal recordings of the human motion task to emulate human frailty and apply our adaptive force control strategy to demonstrate the results of a humanoid successfully assisting the simulated human model to restore the optimal motion task performance.

Self-Adjusting Fuzzy Logic Based Control of Robot Manipulators in Task Space

End effector tracking control of robot manipulators subject to dynamical uncertainties is the main objective of this article. Direct task space control that aims minimizing the end effector tracking error directly is preferred. In the open loop error system, the vector that depends on uncertain dynamical terms is modeled via a fuzzy logic network and a self-adjusting adaptive fuzzy logic component is designed as part of the nonlinear proportional derivative based control input torque. The stability of the closed-loop system is investigated via Lyapunov based arguments and practical tracking is proven. The viability of the proposed control strategy is shown with experimental results. Extensions to uncertain Jacobian case and kinematically redundant robots are also presented.

Task-Space Admittance Controller with Adaptive Inertia Matrix Conditioning

Whenrobots physically interact with the environment, compliant behaviors should be imposed to prevent damages to all entities involved in the interaction. Moreover, during physical interactions, appropriate pose controllers are usually based on the robot dynamics, in which the ill-conditioning of the joint-space inertia matrix may lead to poor performance or even instability. When the control is not precise, large interaction forces may appear due to disturbed end-effector poses, resulting in unsafe interactions. To overcome these problems, we propose a task-space admittance controller in which the inertia matrix conditioning is adapted online. To this end, the control architecture consists of an admittance controller in the outer loop, which changes the reference trajectory to the robot end-effector to achieve a desired compliant behavior; and an adaptive inertia matrix conditioning controller in the inner loop to track this trajectory and improve the closed-loop performance. We evaluated the proposed architecture on a KUKA LWR4+ robot and compared it, via rigorous statistical analyses, to an architecture in which the proposed inner motion controller was replaced by two widely used ones. The admittance controller with adaptive inertia conditioning presents better performance than with a controller based on the inverse dynamics with feedback linearization, and similar results when compared to the PID controller with gravity compensation in the inner loop.

A Passive Decoupling Mechanism for Misalignment Compensation in Master-Slave Teleoperation

Teleoperated robots are commonly used in minimally invasive surgery as they can control surgical instruments at a distance. An operator sends the motion command via a master console, which must convert these into suitable slave instrument actuator inputs for intuitive interaction. However, most master-slave systems available to date use incremental task-space control and clutching, which introduces a discontinuity and orientation misalignment between the master control handle and slave instrument, with a consequent impact on task performance. In this article, we proposed a new master manipulator design to compensate for misalignment mechanically. The modular gimbal consists of a passive decoupling mechanism and a wrist locking feature. After describing the mechanisms and its kinematic configuration, we report on a comparative study under controlled conditions, developed to measure the end effector orientation in both compensated and non-compensated scenarios. The results demonstrate that the compensated master console maintains a near constant end effector orientation over the workspace during clutching, showing great promise as a solution to this outstanding open challenge in master-slave manipulation.

Self-Tuning Fuzzy Task Space Controller for Puma 560 Robot

Since in most robot applications the desired paths are determined in task space or Cartesian space, it is important to control the robot arm in task space. In this paper, a fuzzy controller with modifiable scaling factors is proposed to control the robot end-effector in task space. The controller is a fuzzy system with a mechanism to change the scaling factors when the error is bounded under a predetermined value. The controller is designed in joint space and is developed to work space by using inverse Jacobian strategy. The simulations results on Puma 560 robot manipulator illustrate the high performance of the presented control method.

Walking task space control using time delay estimation based sliding mode of position Controlled NAO biped robot

This work proposes a simple technique to implement in real-time a nonlinear robust controller on a humanoid NAO robot that does not have direct drive joints. The key trick consists of designing a time delay estimation based sliding mode controller without any prior knowledge of the robot’s dynamics to alleviate the heavy computations since the on-board processor has low computational power and to deal with the effect of the uncertainties. Then, the calculated torque inputs will be converted to position controller for the servo actuated NAO robot using an appropriate transformer. The proposed method will ensure in addition to high precision tracking a fast convergence during the reaching phase. The proposed control architecture is validated through experimental results on a real NAO robot.

Dynamic and Versatile Humanoid Walking via Embedding 3D Actuated SLIP Model With Hybrid LIP Based Stepping

In this paper, we propose an efficient approach to generate dynamic and versatile humanoid walking with non-constant center of mass (COM) height. We exploit the benefits of using reduced order models (ROMs) and stepping control to generate dynamic and versatile walking motion. Specifically, we apply the stepping controller based on the Hybrid Linear Inverted Pendulum Model (H-LIP) to perturb a periodic walking motion of a 3D actuated Spring Loaded Inverted Pendulum (3D-aSLIP), which yields versatile walking behaviors of the 3D-aSLIP, including various 3D periodic walking, fixed location tracking, and global trajectory tracking. The 3D-aSLIP walking is then embedded on the fully-actuated humanoid via the task space control on the COM dynamics and ground reaction forces. The proposed approach is realized on the robot model of Atlas in simulation, wherein versatile dynamic motions are generated.

Adaptive-Gains Enforcing Constraints in Closed-Loop QP Control

In this letter, we revisit an open problem of constraints formulation in the context of task-space control frameworks formulated as quadratic programs. In most inverse dynamics implementations, the decision variables are: robot joints acceleration, interaction forces (mostly physical contacts), and robot torques. Nevertheless, many constraints, like distance and velocity bounds, are not written originally in terms of one of these decision variables. Previous work proposed solutions to formulate and enforce joint limits constraints. Yet, none of them worked properly in closed-loop, specifically when bounds are reached or when they are time-varying. First, we show that constraints like collision avoidance, bounds of center of mass, constraints on field-of-view, Cartesian and velocity bounds on a given link... are written in a generic class. Then, we formulate such a class of constraints with gain-parameterized ordinary differential inequality. An adaptive-gain method enforces systematically such class of constraints, and results on a stable behavior when their bounds (even when they vary with time) are reached in closed-loop. Experimental results performed on a humanoid robot validate our solution on a large panel of constraints.

Multi-agent reinforcement learning for redundant robot control in task-space

Task-space control needs the inverse kinematics solution or Jacobian matrix for the transformation from task space to joint space. However, they are not always available for redundant robots because there are more joint degrees-of-freedom than Cartesian degrees-of-freedom. Intelligent learning methods, such as neural networks (NN) and reinforcement learning (RL) can learn the inverse kinematics solution. However, NN needs big data and classical RL is not suitable for multi-link robots controlled in task space. In this paper, we propose a fully cooperative multi-agent reinforcement learning (MARL) to solve the kinematic problem of redundant robots. Each joint of the robot is regarded as one agent. The fully cooperative MARL uses a kinematic learning to avoid function approximators and large learning space. The convergence property of the proposed MARL is analyzed. The experimental results show that our MARL is much more better compared with the classic methods such as Jacobian-based methods and neural networks.

Data-driven learning for robot control with unknown Jacobian

Unlike most control systems, kinematic uncertainty is present in robot control systems in addition to dynamic uncertainty. The use of different types of external sensors in various configurations also results in different sensory transformation or Jacobian matrices and thus leads to different kinematic models. Currently, there is no systematic theoretical framework in developing data-driven neural network (NN) learning and control methods for task-space tracking control of robots with unknown kinematics and dynamics. The existing NN controllers are limited to either dynamic control or kinematic control without considering the interaction between the inner control loop and the outer control loop. In this paper, a NN based data driven offline learning algorithm and an online learning controller are proposed, which are combined in a complementary way. The proposed task-space control algorithms can be implemented on robotic systems with closed control architecture by considering the interaction with the inner control loop. Theoretical analyses are presented to show the stability of the systems and experimental results are presented to illustrate the performance of the proposed learning algorithms.

Adaptive Fuzzy-Region-Based Control of Euler–Lagrange Systems With Kinematically Singular Configurations

Singularity issue has long been a concern of the task-space control design for Euler-Lagrange systems. In classical task-space controls, robots are often assumed to operate in the task space, where singularities do not exist. Such an assumption limits their potential applications in various workspaces. To address the potential singularity issue associated with Euler-Lagrange systems, this article proposes an adaptive fuzzy-region-based control for Euler-Lagrange systems with kinematically singular configurations. Singular regions are described by the potential energy function. The proposed controller includes a joint-space control, which is active when the system approaches singular regions, and a task-space control, which is used to track the desired trajectory. Therefore, the system can smoothly transit from singular regions to nonsingular regions or can achieve singularity avoidance during the tracking task. In order to achieve singularity avoidance while reducing control effort, the coefficients of the potential energy function are adjusted dynamically based on the designed fuzzy system. Rigorous analysis shows that singularity issues can be properly handled, and the asymptotic stability of the system is ensured. Experiments are conducted to demonstrate the effectiveness of the proposed controller.

Task Space Control of Articulated Robot Near Kinematic Singularity: Forward Dynamics Approach

In this study, a forward dynamics-based control (FDC) framework is proposed for task space control of a non-redundant robot manipulator. The FDC framework utilizes forward dynamic robot simulation and an impedance controller to solve the inverse kinematics problem. For the practical use of the proposed control framework, the accuracy, robustness, and stability of robot motion are considered. Taking advantage of the stability of the implicit Euler method, a high-gain PD controller enables accurate end-effector pose tracking in the task space without losing stability even near the kinematic singularities. Also, the robustness of the controller is enhanced by borrowing the structure of the nonlinear robust internal-loop compensator. Lastly, the selective joint damping injection and spring force saturation are applied to the impedance controller so that the robot motion can always stay within the given dynamic constraints. This study suggests a new, effective solution for the kinematic singularity problem of non-redundant robot manipulators.

Stiff and safe task-space position and attitude controller for robotic manipulators

This paper proposes a stiff and safe task-space position and attitude control scheme for robotic manipulators. This study extends the work of Kikuuwe et al’s. (2006) velocity-bounding proxy-based sliding mode control by explicitly addressing the attitude part. The proposed controller has a Jacobian-based structure, which realizes smooth trajectories when the desired attitude is far rotated from the actual attitude. It also imposes arbitrary magnitude limits on the end-effector velocity, angular velocity, and each actuator force without sacrificing a stiffness, which is the same level as a high-gain PID position control below the limits. The benefit of the proposed controller becomes apparent after the robot yields to external forces due to force saturations, when the robot makes contact with obstacles. In such a situation, if the external forces disappear, the controller generates overdamped resuming motion from large tracking errors. The proposed controller can be expected to enhance the safety of robotic applications for the human–robot interaction. The proposed method is validated by experiments employing a six-degree of freedom industrial manipulator.