Task Space Trajectory Research Articles

An Extended Passive Motion Paradigm for Human-Like Posture and Movement Planning in Redundant Manipulators.

A major challenge in robotics and computational neuroscience is relative to the posture/movement problem in presence of kinematic redundancy. We recently addressed this issue using a principled approach which, in conjunction with nonlinear inverse optimization, allowed capturing postural strategies such as Donders' law. In this work, after presenting this general model specifying it as an extension of the Passive Motion Paradigm, we show how, once fitted to capture experimental postural strategies, the model is actually able to also predict movements. More specifically, the passive motion paradigm embeds two main intrinsic components: joint damping and joint stiffness. In previous work we showed that joint stiffness is responsible for static postures and, in this sense, its parameters are regressed to fit to experimental postural strategies. Here, we show how joint damping, in particular its anisotropy, directly affects task-space movements. Rather than using damping parameters to fit a posteriori task-space motions, we make the a priori hypothesis that damping is proportional to stiffness. This remarkably allows a postural-fitted model to also capture dynamic performance such as curvature and hysteresis of task-space trajectories during wrist pointing tasks, confirming and extending previous findings in literature.

Inverse dynamics and trajectory tracking control of a new six degrees of freedom spatial 3-RPRS parallel manipulator

Abstract. This paper presents the complete dynamic model of a new six degrees of freedom (DOF) spatial 3-RPRS parallel manipulator. The geometry parameters of the manipulator are optimized for a given constant orientation workspace. Further, a robust task-space trajectory tracking control is also designed for the manipulator along with a nonlinear disturbance observer. To demonstrate the efficacy and show the complete performance of the proposed controller, virtual prototype experiments are executed using one of the multibody dynamics software namely MSC Adams. The computer-based virtual prototype experiment results show that the manipulator tracking performance is satisfactory with the proposed control scheme. In addition, the controller parameter sensitivity and robustness analyses are also accomplished.

Robust task-space control of an autonomous underwater vehicle-manipulator system by PID-like fuzzy control scheme with disturbance estimator

This paper presents, a robust nonlinear proportional-integral-derivative (PID)-like fuzzy control scheme for a task-space trajectory tracking control of an autonomous underwater vehicle-manipulator system (AUVMS) employed for deep-sea intervention tasks. The effectiveness of the proposed control scheme is numerically demonstrated on a planar underwater vehicle manipulator system (consisting of an underwater vehicle and two link rotary (2R) serial planar manipulator). The actuator and sensor dynamics of the system are also incorporated in the dynamical model of an AUVMS. The proposed control law consists of two main parts: first part uses a feed forward term to reinforce the control activity with extravagance from known desired acceleration vector and carries an estimated perturbed term to compensate for the unknown effects namely external disturbances and unmodeled dynamics and the second part uses a PID-like fuzzy logic control as a feedback portion to enhance the overall closed-loop stability of the system. The primary objective of the proposed control scheme is to track the given end-effector task-space trajectory despite of external disturbances, system uncertainties and internal noises associated with the AUVMS system. To show the effectiveness of the proposed control scheme, comparison is made with linear and nonlinear PID controllers. Simulation results confirmed that with the proposed control scheme, the AUVMS can successfully track the given desired spatial trajectory and gives better and robust control performance.

Dual Integral Sliding Mode Control Loop for Mechanical Error Correction in Trajectory-Tracking of a Planar 3-PRP Parallel Manipulator

This paper presents a dual-loop control scheme based on an integral sliding mode control scheme for the task-space pose error correction in trajectory-tracking of a planar 3-PRP parallel manipulator due to mechanical inaccuracies. The proposed dual-loop control scheme uses redundant sensor feedback, i.e., individual active joint displacements, velocities (at the joint-space level) and, end-effector positions and orientation (at the task-space level) are obtained as feedback signals using appropriate sensors. Using the redundant feedback information, the actual pose errors of the end-effector are computed in the outer-loop (kinematic) control and rectified in joint-space inner-loop (dynamic) control to achieve the given desired task-space trajectory. To demonstrate the efficacy and show complete performance of the controllers, real-time experiments are executed on an in-house fabricated planar 3-PRP parallel manipulator. The experimentation results show that the manipulator tracing performance is considerably improved with the proposed dual-loop control scheme. In addition, the controller parameter sensitivity and robustness analyses are also accomplished.

Fuzzy MTEJ controller with integrator for control of underactuated manipulators

Abstract This paper presents control of underactuated robot manipulators in task space utilizing novel algorithm called fuzzy modified transpose effective Jacobian (MTEJ) with integrator term which ensures tracking trajectory in task space with high-quality performance. Although non-model base MTEJ have been introduced before and evaluated for underactuated robots, this method has poor operation against non-linear factors in actuators and joints observed in practical tests like deadband, backlash or Coulomb damping. The contributions of this paper are in twofold. First, to introduce improved MTEJ algorithm with additional integrator term that is efficient to eliminate effects of mentioned factors or other source of steady state error (SSE). Second, using new fuzzy rules to manage its terms to stabilize system with better control properties, the controller is used to make the endeffector to both track a predefined trajectory or set on an exact point. Global stabilization of this control method is proved. Simulation and experimental results are offered which compared tracking performance of the improved fuzzy MTEJ with integrator to other methods for both tracking and point to point (P.T.P) control. Outcomes of these experiments reveal privileges of using fuzzy improved MTEJ in various areas like removing SSE with better control characteristics and low computational efforts.

Adaptive Control of Robot Manipulators With Uncertain Kinematics and Dynamics

In this paper, we investigate the adaptive control problem for robot manipulators with both the uncertain kinematics and dynamics. We propose two adaptive control schemes to realize the objective of task-space trajectory tracking irrespective of the uncertain kinematics and dynamics. The proposed controllers have the desirable separation property, and we also show that the first adaptive controller with appropriate modifications can yield improved performance, without the expense of conservative gain choice. The performance of the proposed controllers is shown by numerical simulations.

Task Space Trajectory Tracking Control of Robot Manipulators with Uncertain Kinematics and Dynamics

To improve the tracking precision of robot manipulators’ end-effector with uncertain kinematics and dynamics in the task space, a new control method is proposed. The controller is based on time delay estimation and combines with the nonsingular terminal sliding mode (NTSM) and adaptive fuzzy logic control scheme. Kinematic parameters are not exactly required with the consideration of kinematic uncertainties in the controller. No dynamic models or numerous parameters of the robot manipulator system are required with the use of TDE. Thus, the controller is simple structure and suitable for practical applications. Furthermore, errors caused by time delay estimation are compensated by the adaptive fuzzy nonsingular terminal sliding mode scheme. The simulation is performed on a 2-DOF robot manipulator with three cases in the task space. The results show that the proposed controller provides faster convergence rate and higher tracking precision than TDE based NTSM and improved TDE based NTSM controller.

요청한 작업 경로에 따른 매니퓰레이터의 기구학적 변수 선정을 위한 군집 지능 기반 최적 설계

요청한 작업 경로에 따른 매니퓰레이터의 기구학적 변수 선정을 위한 군집 지능 기반 최적 설계

Robust Task Space Trajectory Tracking Control of Robotic Manipulators

This work deals with the problem of the accurate task space trajectory tracking subject to finite-time convergence. Kinematic and dynamic equations of a redundant manipulator are assumed to be uncertain. Moreover, globally unbounded disturbances are allowed to act on the manipulator when tracking the trajectory by the end-effector. Furthermore, the movement is to be accomplished in such a way as to reduce both the manipulator torques and their oscillations thus eliminating the potential robot vibrations. Based on suitably defined task space non-singular terminal sliding vector variable and the Lyapunov stability theory, we propose a class of chattering-free robust controllers, based on the estimation of transpose Jacobian, which seem to be effective in counteracting both uncertain kinematics and dynamics, unbounded disturbances and (possible) kinematic and/or algorithmic singularities met on the robot trajectory. The numerical simulations carried out for a redundant manipulator of a SCARA type consisting of the three revolute kinematic pairs and operating in a two-dimensional task space, illustrate performance of the proposed controllers as well as comparisons with other well known control schemes.

Geometrical Invariance and Smoothness Maximization for Task-Space Movement Generation

In human motor control studies, end-effector (e.g., hand) trajectories have been successfully modeled using optimization principles. Yet, it remains unclear how such trajectories are updated when the end-effector or task goals are perturbed. Here, we present an approach to human and robotic task-level trajectory planning and modification using geometrical invariance and optimization, allowing to adapt learned movements to a priori unknown boundary conditions. The optimization criterion represents a tradeoff between smoothness (minimum jerk) and accuracy (jerk–accuracy model). We show that planning maximally smooth movements allows recovery from perturbations by superimposing specific affine orbits on maximally smooth preplanned trajectories. The generated trajectories are compared with those resulting from other recent approaches used in robotics. Finally, we discuss conditions for affine invariance of maximally smooth task-space trajectories. Possible applications of this study to both human motor control and robotics research studies are discussed.

ℒ2 performance control of robot manipulators with kinematics, dynamics and actuator uncertainties

Summary This paper deals with the task-space trajectory tracking control problem of robot manipulators. An improved adaptive backstepping controller is proposed to deal with the uncertainties in kinematics, dynamics, and actuator modeling. To avoid the explosion of computation in conventional backstepping techniques, a modified dynamic surface control algorithm is proposed, which guarantees the asymptotic convergence rather than the uniformly ultimately boundedness of tracking errors in conventional dynamic surface control methods. Furthermore, the expression of the ℒ2 norm of tracking errors is explicitly derived in relation to the controller parameters, which provides instructions on tuning controller parameters to adjust the system performance. Moreover, the passivity structure of the designed adaptation law is thoroughly analyzed. Simulation of a free-floating space robot is used to verify the effectiveness of the proposed control strategy in comparison with the conventional tracking control schemes. Copyright © 2016 John Wiley & Sons, Ltd.

Constrained Multilegged Robot System Modeling and Fuzzy Control With Uncertain Kinematics and Dynamics Incorporating Foot Force Optimization

This paper studies the optimal distribution of feet forces and control of multilegged robots with uncertainties in both kinematics and dynamics. First, a constrained dynamics for multilegged robots and the constrained environment model are established by considering both kinematic and dynamic uncertainties. Under an external wrench for multilegged robots, the foot forces and moments of the supporting legs can be formulated as quadratic programming problems subject to linear and nonlinear constraints. The neurodynamics of recurrent neural network is developed for foot force optimization. For the obtained optimized tip-point force and the motion of legs, we propose a hybrid task-space trajectory and force tracking based on fuzzy system and adaptive mechanism that are used to compensate for the external perturbation, kinematics, and dynamics uncertainties. The tracking of task-space trajectory and constraint force is achieved under unknown dynamical parameters, constraints, and disturbances. Extensive simulations have been provided to verify the effectiveness of the proposed scheme.

Task Space Control of an Autonomous Underwater Vehicle Manipulator System by Robust Single-Input Fuzzy Logic Control Scheme

In this paper, a robust single-input fuzzy logic control Robust Single Input Fuzzy Logic Controller (RSIFLC) scheme is proposed and applied for task-space trajectory control of an autonomous underwater vehicle manipulator system (AUVMS) employed for underwater manipulation tasks. The effectiveness of the proposed control scheme is numerically demonstrated on a planar underwater vehicle manipulator system [consisting of an underwater vehicle and a two link rotary (2R) serial planar manipulator]. The actuator and sensor dynamics of the system are also incorporated in the dynamical model of an AUVMS. The proposed control law consists of a feedforward term to exaggerate the control activity with immoderation from the known desired acceleration vector and an estimated perturbed term to compensate for the unknown effects namely external disturbances and unmodeled dynamics as a first part and a single-input fuzzy logic control as a feedback portion to enhance the overall closed-loop stability of the system as a second part. The primary objective of the proposed control scheme is to track the given end-effector task space trajectory despite of external disturbances, system uncertainties, and internal noises associated with the AUVMS. To show the efficacy of the proposed control scheme, comparison is made with conventional fuzzy logic control (CFLC), sliding mode control (SMC), and proportional–integral–derivative (PID) controllers. Simulation results confirmed that with the proposed control scheme, the AUVMS can successfully track the given desired spatial trajectory and gives better and robust control performance.

Online Biped Walking Pattern Generation with Contact Consistency

In this paper, a novel online biped walking gait pattern generating method with contact consistency is proposed. Generally, it’s desirable that there is no foot-ground slipping during biped walking. By treating the hip of the biped robot as a linear inverted pendulum (LIP), a foot placement controller that takes the contact consistency into account is proposed to tracking the desired orbit energy. By selecting the hip’s horizontal locomotion as the parameter, the trajectories in task space for walking are planned. A task space controller without calculating the inversion of inertial matrix is presented. Simulation experiments are implemented on a virtual 5-link point foot biped robot. The results show the effectiveness of the walking pattern generating method which can realize a stable periodic gait cycle without slipping and falling even suffering a sudden disturbance.

Spatiotemporal representation of 3D hand trajectory based on beta-elliptic models

The aim of this paper was to model the hand trajectory during grasping by an extension in 3D of the 2D written language beta-elliptic model. The interest of this model is that it takes into account both geometric and velocity information. The method relies on the decomposition of the task space trajectories in elementary bricks. The latter is characterized by a velocity profile modelled with beta functions and a geometry modelled with elliptic shapes. A data base of grasping movements has been constructed and the errors of reconstruction were assessed (distance and curvature) considering two variations of the beta-elliptic model (‘quarter ellipse’ and ‘two tangents points’ method). The results showed that the method based on two tangent points outperforms the quarter ellipse method with average and maximum relative errors of 2.73% and 8.62%, respectively, and a maximum curvature error of 9.26% for the former. This modelling approach can find interesting application to characterize the improvement due to a rehabilitation or teaching process by a quantitative measurement of hand trajectory parameters.

상지 외골격 로봇 제어를 위한 인체 팔 동작의 기구학 및 동역학적 분석 - 파트 2: 제한조건의 선형 결합

The redundancy resolution of the seven DOF (Degree of Freedom) upper limb exoskeleton is key to the synchronous motion between a robot and a human user. According to the seven DOF human arm model, positioning and orientating the wrist can be completed by multiple arm configurations that results in the non-unique solution to the inverse kinematics. This paper presents analysis on the kinematic and dynamic aspect of the human arm movement and its effect on the redundancy resolution of the seven DOF human arm model. The redundancy of the arm is expressed mathematically by defining the swivel angle. The final form of swivel angle can be represented as a linear combination of two different swivel angles achieved by optimizing two cost functions based on kinematic and dynamic criteria. The kinematic criterion is to maximize the projection of the longest principal axis of the manipulability ellipsoid of the human arm on the vector connecting the wrist and the virtual target on the head region. The dynamic criterion is to minimize the mechanical work done in the joint space for each of two consecutive points along the task space trajectory. The contribution of each criterion on the redundancy was verified by the post processing of experimental data collected with a motion capture system. Results indicate that the bimodal redundancy resolution approach improved the accuracy of the predicted swivel angle. Statistical testing of the dynamic constraint contribution shows that under moderate speeds and no load, the dynamic component of the human arm is not dominant, and it is enough to resolve the redundancy without dynamic constraint for the realtime application.

Fuzzy weighted subtask controller for redundant manipulator

SUMMARYWe propose a fuzzy weighted subtask controller for a redundant robot manipulator. To expand the feasibility of the inverse kinematic solution, we introduce a weighted pseudo-inverse that changes the null-space of the Jacobian. The weights of elements in the pseudo-inverse are obtained using fuzzy rules that are related to the null-space velocity tracking error. With the pseudo-inverse, we develop a task space controller to track a desired task space trajectory and subtask control input. We propose a weighted subtask controller for multiple subtasks. The results of a simulation and experiment using a seven-degree-of-freedom whole arm manipulator robot show the effectiveness of the proposed controller with multiple subtasks.

Indirect robot model learning for tracking control

Learning task-space tracking control on redundant robot manipulators is an important but difficult problem. A main difficulty is the non-uniqueness of the solution: a task-space trajectory has multiple joint-space trajectories associated, therefore averaging over non-convex solution space needs to be done if treated as a regression problem. A second class of difficulties arise for those robots when the physical model is either too complex or even not available. In this situation machine learning methods may be a suitable alternative to classical approaches. We propose a learning framework for tracking control that is applicable for underactuated or non-rigid robots where an analytical physical model of the robot is unavailable. The proposed framework builds on the insight that tracking problems are well defined in the joint task- and joint-space coordinates and consequently predictions can be obtained via local optimization. Physical experiments show that state-of-the art accuracy can be achieved in both online and offline tracking control learning. Furthermore, we show that the presented method is capable of controlling underactuated robot architectures as well.

Smooth Trajectory Planning for 3-UPU Parallel Manipulator

Exciting the mechanical resonance mode and vibration caused by non-smooth trajectories can cause some failure in the structure of the robot and its actuators. This work proposes a smooth trajectory planning for 3 degree of freedom (dof) 3-upu parallel manipulator. We have used optimization method to define the smooth trajectory for this robot. In this paper an objective function has been used which contain a term related to the total execution time and a term corresponding to the integral of squared jerk. Some constraints have been applied to the problem as the input of our system. Task space and joint space trajectory are then considered as the output. In this method optimization techniques such as genetic algorithm and fmincon (function of MatLabTM ) have been investigated. Six via points have been considered and B-splines have been used to present kinematic quantities.

Hybrid fuzzy position/force control by adaptive network-based fuzzy inference system for robot manipulator mounted on oscillatory base

This study considered the hybrid position/force control of robots with an oscillatory base. The objective when controlling such a system is to control the contact force between the environment and the object in the constrained directions. The auxiliary control input laws with the ANFIS tuning methodology were successfully applied to robot manipulators mounted on an oscillatory base to provide hybrid position/force control. Linguistic fuzzy information from human knowledge was incorporated into the fuzzy system, which was equipped with a training algorithm. The advantage of the proposed control scheme is that only select control inputs are needed to select control inputs for task space trajectory tracking. In other words, such a system does not require the redesign of the vibration damping controller. Tracking performance comparison is also presented for the proposed control scheme and the other existing control techniques. It is shown that the proposed fuzzy control scheme offers several implementation advantages such as less steady-state errors, reduced effect of overshoot, and a fast convergent rate in real-time verification. The experimental results of this study confirmed the efficiency of the proposed control design and its feasibility for use in various mechanical systems, including mobile robots, gantry cranes, underwater robots, and other dynamic systems mounted on oscillatory bases and used to perform constrained motion tasks.