Joint Limit Avoidance Research Articles

Dealing With Constraints in Sensor-Based Robot Control

A framework is presented in this paper for the control of a multisensor robot under several constraints. In this approach, the features coming from several sensors are treated as a single feature vector. The core of our approach is a weighting matrix that balances the contribution of each feature, allowing the taking of constraints into account. The constraints are considered as additional features that are smoothly injected in the control law. Multisensor modeling is introduced for the design of the control law, drawing similarities with linear quadratic control. The main properties are exposed and we propose several strategies to cope with the main drawbacks. The framework is validated in a complex experiment, illustrating various aspects of the approach. The goal is the positioning of a six-DOF robot arm with 3-D visual servoing. The considered constraints are both eye-in-hand and eye-to-hand visibility, together with joint limit avoidance. The system is thus highly overdetermined, yet the task can be performed while ensuring several combinations of constraints.

An off-line programming system for robotic drilling in aerospace manufacturing

Off-line programming systems are essential tools for the effective use of robots in the manufacturing environment. This paper presents a dedicated off-line programming system for robotic drilling in aerospace manufacturing. Following a brief introduction of the system architecture, the paper discusses two major problems of off-line programming for robotic drilling, i.e., redundancy resolution and position correction. A new performance index is proposed for the combined requirements of singularity and joint-limit avoidance, followed by a discussion of the redundancy resolution scheme by using numerical optimization at the joint displacement level. A position correction method using measurement data of reference holes is developed for the enhancement of robotic drilling accuracy. Robot programs generated by using the developed system have been tested on a robotic drilling system, and the experimental results are provided.

Two-Loop Control of Redundant Manipulators: Analysis and Experiments on a 3-DOF Planar Arm

A redundant robot has more degrees of freedom (DOF) than those required to accomplish a given motion task. This fact allows the possibility of achieving an additional task, such as avoidance of joint limits or singularities, besides the primary one. Different criteria have been proposed in the literature for the selection of such a secondary task. This paper first recalls some of those criteria and then proposes a two-loop scheme for the motion control of redundant robots. In order to validate the proposed scheme, some experiments are carried out in a direct-drive redundant planar arm which has been designed and built in our laboratory.

Kinematic Design and Analysis of a 6-DOF Upper Limb Exoskeleton Model for a Brain-Machine Interface Study

The integration of the brain-machine interface (BMI) and the exoskeleton technique has the potential to promote the understanding of fundamental principles in neural control of movement, as well as to motivate a new generation of rehabilitation or power augmentation exoskeleton systems. In this paper, the kinematic design and development of a 6-DOF upper limb exoskeleton for a BMI study is presented. In order to achieve a singularity-free design of the shoulder complex, a 4-DOF shoulder complex model is proposed using one redundant DOF in contrast to the commonly used orthogonal triad model. The feasibility of both singularity and joint limit avoidance control is investigated based on the reachability analysis.

Intermediate Desired Value Approach for Task Transition of Robots in Kinematic Control

The task-based control framework is well established for its ability to generate complex behavior in versatile robots. When executing multiple complex tasks, continuous and stable transition among these tasks is one of the most important issues. In this paper, the problem of task transition is discussed to achieve continuous transitions between arbitrary tasks effectively. Instead of modifying the control laws, the design of intermediate desired values to be realized by existing controllers is proposed. The proposed approach can deal with arbitrary task sets, with or without priorities, for insertion and removal, and with priority rearrangement for hierarchical sets of tasks. The solution is generic and can be used for any type of transition. Two examples of uses include a time-driven transition to execute a given task schedule and a transition depending on the robot configuration to perform joint-limit avoidance behaviors. The performance of the algorithm is verified in simulations and on a physical robot.

Effects of rotation amplitude on arm movement when rotating a spherical object

Arm movements when rotating a spherical object were experimentally investigated. Twelve volunteers participated in the experiment and were asked to rotate a sphere for a large range of amplitude. Results showed that subjects anticipated their posture at the beginning of object manipulation even for low rotation amplitudes. The way of anticipation strongly depended on rotation direction. The end-state comfort hypothesis, effects of joint limits and principle of minimum work were examined for explaining motion control. The anticipation would ensure a better end-state comfort while avoiding joint limits in case of higher amplitude of object rotation. Meanwhile, it should not deteriorate the comfort at the beginning of manipulation too much. High postural variability for low rotation amplitude tasks suggested that there might exist a range of postures of similar level of comfort. These findings will be useful in developing human behaviour-based motion simulations for digital human. Practitioner Summary: Arm movement was investigated when rotating a spherical object with a large range of amplitude. The end-state comfort hypothesis, effects of joint limits and principle of minimum work were examined for explaining motion control. Results will be helpful for a better design of rotary controls and for developing motion simulation algorithms.

An iterative robot-image Jacobian approximation of image-based visual servoing for joint limit avoidance

In this paper, we propose a new method addressing the robot-image Jacobian approximation of image-based visual servoing (IBVS) for a redundant manipulator. The robot-image Jacobian is approximated iteratively and is a model free. A linearised model of the robot-image Jacobian is applied, based on the first order Taylor series approximation. A weighted least norm solution is induced in a pseudo inverse computation of the approximated robot-image Jacobian. The resulting control law then can be used for visual servoing tasks with joint limit avoidance capability using both a static target or moving target. The self-motion of the robot joints resolves the redundancy during visual servoing tasks when one or more joints are approaching their joint limits. A design and stability analysis of the proposed method is discussed in this paper. Simulated and real-time experiments using a 7 DOF PowerCube robot manipulator are conducted. The IBVS is configured using a monovision eye-in-hand system configuration. The system behaviour and performances of the proposed method are presented and analysed.

PREFACE: SPECIAL ISSUE ON CONTROL AND AUTOMATION

Automated systems are widely found in engineering applications. Industrial, transportation, energy, and service sectors are filled with such applications. Control is crucial in proper function of an automated system. The present special issue of the Journal of Control and Intelligent Systems is devoted to this subject. The concept of this special issue was conceived at the 8th IEEE International Conference on Control and Automation (ICCA 2010), which was held in Xiamen, China, on 9–11 June 2010. In view of the excellence of the papers presented at that conference, the guest editors identified a set of papers in Control and Automation. The authors of these papers were invited to prepare comprehensive and original papers on those topics for peer review and subsequent selection for the present Special Issue of Control and Intelligent Systems. On the basis of the peer review, eight papers were subsequently selected and further revised for inclusion in this Special Issue. The guest editors wish to thank the authors for their dedication and excellence in preparing these papers, and the reviewers for their valuable reviews. The first five papers of the Special Issue describe work on modelling and control of unmanned systems, ranging from robotic systems to satellite. In the first paper “Adaptive Robust Tracking Control of an Underwater-Vehicle Manipulator System (UVMS) with Sub-Region and SelfMotion Criteria,” Zool, Ismail and, Dunnigan present a new adaptive robust control scheme for an UVMS, which allows the tracking of the intersection of multiple local sub-regions that are assigned for each subsystem under the influence of modelling uncertainties as well as additive disturbances. In the presented adaptive control law, a least-squares estimation algorithm is utilized rather than gradient-type approach. The use of the self-motion due to the kinematically redundant system allows performance of multiple sub-tasks (e.g., maintaining manipulability and avoidance of mechanical joint limits). The stability analysis of the proposed controller is carried out using the Lyapunov-like function.

ADAPTIVE ROBUST TRACKING CONTROL OF AN UNDERWATER VEHICLE-MANIPULATOR SYSTEM WITH SUB-REGION AND SELF-MOTION CRITERIA

This paper proposes an adaptive robust control scheme for an Underwater-Vehicle Manipulator System (UVMS). The proposed controller enables the tracking of the intersection of multiple local sub-regions that are assigned for each subsystem of a UVMS under the influence of modelling uncertainties as well as additive disturbances. The presence of variable ocean currents creates hydrodynamic forces and moments that are not well-known or predictable, even though they are bounded. Therefore, the control task of tracking a prescribed sub-region trajectory is challenging due to these additive bounded disturbances. In the presented adaptive control law, a least-squares estimation algorithm is utilized rather than gradient-type approach. The use of the self-motion due to the kinematically redundant system allows performance of multiple subtasks (e.g., maintaining manipulability and avoidance of mechanical joint limits). The asymptotically sub-region and sub-task tracking are ensured using the proposed control law despite the parametric uncertainty of the UVMS and external additive disturbances. The stability analysis is carried out using the Lyapunov-type approach. The simulation results illustrate the validity of the proposed control scheme.

Joint Constrained Differential Inverse Kinematics Algorithm for Serial Manipulators

This paper presents a methodology for avoiding joint limits of robot manipulators in the solution of the differential inverse kinematics problem. A nonlinear transformation is applied to the joint limits, leading to a new kinematics formulation defined in the space of the transformed variables. The introduced transformation ensures that the joint variables stay between joint limits. Optimality conditions are converted into the transformed joint space, and closed-loop differential inverse kinematics is applied. The effectiveness of the introduced method is demonstrated on a simulational example.

Heuristic solution for constrained 7-DOF motion planning in 3D scanning application

This paper proposes a heuristic algorithm for near-optimal handling of the redundancy in robot mechanisms, applied to a 3D scanning platform consisting of a 6-DOF articulated robot arm which moves a laser sensor around a workpiece placed on a rotary table. The proposed method handles both static constraints, like avoiding joint limits, kinematic singularities or collisions, modelled using a configuration map viewed as a grayscale image, and dynamic constraints like velocity and acceleration. The algorithm is compared with Dijkstra algorithm, which gives the optimal solution, but is very slow, and with two other heuristics which are very fast, but give suboptimal solutions.

Kinematic self retargeting: A framework for human pose estimation

This paper presents a model-based, Cartesian control theoretic approach for estimating human pose from a set of key features points (key-points) detected using depth images obtained from a time-of-flight imaging device. The key-points represent positions of anatomical landmarks, detected and tracked over time based on a probabilistic inferencing algorithm that is robust to partial occlusions and capable of resolving ambiguities in detection. The detected key-points are subsequently kinematically self retargeted, or mapped to the subject’s own kinematic model, in order to predict the pose of an articulated human model at the current state, resolve ambiguities in key-point detection, and provide estimates of missing or intermittently occluded key-points. Based on a standard kinematic and mesh model of a human, constraints such as joint limit avoidance, and self-penetration avoidance are enforced within the retargeting framework. Effectiveness of the algorithm is demonstrated experimentally for upper and full-body pose reconstruction from a small set of detected key-points. On average, the proposed algorithm runs at approximately 10 frames per second for the upper-body and 5 frames per second for whole body reconstruction on a standard 2.13 GHz laptop PC.

Inverse Kinematics and Control of a 7-DOF Redundant Manipulator Based on the Closed-Loop Algorithm

Closed-loop inverse kinematics (CLIK) algorithm mostly resolves the redundancy at the velocity level. In this paper we extend the CLIK algorithm to the acceleration level to meet some applications that require the joint accelerations. The redundancy resolutions at the velocities and acceleration levels via pseudoinverse method are analyzed respectively. The objective function of joint limits avoidance(JLA) is combined into the redundancy resolution as an optimization approach of the null space motion. A seven-DOF redundant manipulator is designed to do the computer simulations and the real experiments are carried out on a Powercube modular manipulator. Their results demonstrated the effectiveness of the proposed algorithm.

Robust tracking control of kinematically redundant robot manipulators subject to multiple self-motion criteria

SUMMARYIn this study, we consider a model based robust control scheme for kinematically redundant robot manipulators that also enables the use of self motion of the manipulator to perform multiple sub-tasks (e.g., maintaining manipulability, avoidance of mechanical joint limits, and obstacle avoidance). The controller proposed ensures uniformly ultimately bounded end-effector and sub-task tracking despite the parametric uncertainty associated with the dynamic model. A Lyapunov based approach has been utilized in the controller design and extension to a non minimum set of parameters for orientation representation has been presented to illustrate the flexibility of the approach. Extensive simulation studies performed initially on a 3 link planar robot arm (for the planar case) and on a six degree of freedom (DOF) Puma type robot arm (for the 3D case with quaternion feedback) are presented to demonstrate the capabilities and the performance of the controller. The results were then experimentally tested on an actual Puma 560 robot to illustrate the feasibility of the proposed method.

Joint-Limit Avoidance and Kinetic-Energy Minimization in Manipulators Having Surplus Joints

Joint-Limit Avoidance and Kinetic-Energy Minimization in Manipulators Having Surplus Joints

Analytical Inverse Kinematic Computation for 7-DOF Redundant Manipulators With Joint Limits and Its Application to Redundancy Resolution

This paper proposes an analytical methodology of inverse kinematic computation for 7 DOF redundant manipulators with joint limits. Specifically, the paper focuses on how to obtain all feasible inverse kinematic solutions in the global configuration space where joint movable ranges are limited. First, a closed-form inverse kinematic solution is derived based on a parameterization method. Second, how the joint limits affect the feasibility of the inverse solution is investigated to develop an analytical method for computing feasible solutions under the joint limits. Third, how to apply the method to the redundancy resolution problem is discussed and analytical methods to avoid joint limits are developed in the position domain. Lastly, the validity of the methods is verified by kinematic simulations.

ADAPTIVE NON-LINEAR TRACKING CONTROL OF KINEMATICALLY REDUNDANT ROBOT MANIPULATORS1

Past research efforts have focused on the end-effector tracking control of redundant robots because of their increased dexterity over their non-redundant counterparts. This work utilizes an adaptive full-state feedback quaternion-based controller developed in [2] and focuses on the design of a general sub-task controller. This sub-task controller does not affect the position and orientation tracking control objectives, but instead projects a preference on the configuration of the manipulator based on sub-task objectives such as the following: singularity avoidance, joint limit avoidance, bounding the impact forces, and bounding the potential energy.

Robust Control for High-Speed Visual Servoing Applications

This paper presents a new control scheme for visual servoing applications. The approach employs quadratic optimization, and explicitly handles both joint position, velocity and acceleration limits. Contrary to existing techniques, our method does not rely on large safety margins and slow task execution to avoid joint limits, and is hence able to exploit the full potential of the robot. Furthermore, our control scheme guarantees a well-defined behavior of the robot even when it is in a singular configuration, and thus handles both internal and external singularities robustly. We demonstrate the correctness and efficiency of our approach in a number of visual servoing applications, and compare it to a range of previously proposed techniques.

Analytical inverse kinematics with body posture control

AbstractThis paper presents a novel whole‐body analytical inverse kinematics (IK) method integrating collision avoidance and customizable body control for animating reaching tasks in real‐time. Whole‐body control is achieved with the interpolation of pre‐designed key body postures, which are organized as a function of the direction to the goal to be reached. Arm postures are computed by the analytical IK solution for human‐like arms and legs, extended with a new simple search method for achieving postures avoiding joint limits and collisions. In addition, a new IK resolution is presented that directly solves for joints parameterized in the swing‐and‐twist decomposition. The overall method is simple to implement, fast, and accurate, and therefore suitable for interactive applications controlling the hands of characters. The source code of the IK implementation is provided. Copyright © 2007 John Wiley & Sons, Ltd.

Implementation and Kinematic Control of a Hyper-redundant Mobile Manipulator System

Redundant or hyper-redundant mobile manipulator can give lots of assistance to astronauts in space station. The design and implementation of a hyper redundant mobile manipulator system are described, which is composed of an 8 DOF module robot and a 1 DOF motorized rail. Inverse kinematic resolution of the system is discussed and one simplified control method based on joint limit avoidance and configuration optimization is proposed. Simulation and experimental results are presented.