Redundancy Resolution Research Articles

A User Study on Kinesthetic Teaching of Redundant Robots in Task and Configuration Space

The recent advent of compliant and kinematically redundant robots poses new research challenges for human-robot interaction. While these robots provide a great degree of flexibility for the realization of complex applications, the flexibility gained generates the need for additional modeling steps and definition of criteria for redundancy resolution constraining the robot's movement generation. The explicit modeling of such criteria usually require experts to adapt the robot's movement generation subsystem. A typical way of dealing with this configuration challenge is to utilize kinesthetic teaching by guiding the robot to implicitly model the specific constraints in task and configuration space. We argue that current programming-by-demonstration approaches are not efficient for kinesthetic teaching of redundant robots and show that typical teach-in procedures are too complex for novice users. In order to enable non-experts to master the configuration and programming of a redundant robot in the presence of non-trivial constraints such as confined spaces, we propose a new interaction scheme combining kinesthetic teaching and learning within an integrated system architecture. We evaluated this approach in a user study with 49 industrial workers at HARTING, a medium-sized manufacturing company. The results show that the interaction concepts implemented on a KUKA Lightweight Robot IV are easy to handle for novice users, demonstrate the feasibility of kinesthetic teaching for implicit constraint modeling in configuration space, and yield significantly improved performance for the teach-in of trajectories in task space.

Online Sensor Information and Redundancy Resolution Based Obstacle Avoidance for High DOF Mobile Manipulator Teleoperation

Abstract High degrees of freedom (DOF) mobile manipulators provide more flexibility than conventional manipulators. They also provide manipulation operations with a mobility capacity and have potential in many applications. However, due to high redundancy, planning and control become more complicated and difficult, especially when obstacles occur. Most existing obstacle avoidance methods are based on off-line algorithms and most of them mainly focus on planning a new collision-free path, which is not appropriate for some applications, such as teleoperation and uses many system resources as well. Therefore, this paper presents an online planning and control method for obstacle avoidance in mobile manipulators using online sensor information and redundancy resolution. An obstacle contour reconstruction approach employing a mobile manipulator equipped with an active laser scanner system is also introduced in this paper. This method is implemented using a mobile manipulator with a seven-DOF manipulator and a four-wheel drive mobile base. The experimental results demonstrate the effectiveness of this method.

PARAS

Association rule mining is known to be computationally intensive, yet real-time decision-making applications are increasingly intolerant to delays. In this paper, we introduce the parameter space model, called PARAS. PARAS enables efficient rule mining by compactly maintaining the final rulesets. The PARAS model is based on the notion of stable region abstractions that form the coarse granularity ruleset space. Based on new insights on the redundancy relationships among rules, PARAS establishes a surprisingly compact representation of complex redundancy relationships while enabling efficient redundancy resolution at query-time. Besides the classical rule mining requests, the PARAS model supports three novel classes of exploratory queries. Using the proposed PSpace index, these exploratory query classes can all be answered with near real-time responsiveness. Our experimental evaluation using several benchmark datasets demonstrates that PARAS achieves 2 to 5 orders of magnitude improvement over state-of-the-art approaches in online association rule mining.

Mobile Parallel Manipulators, Modelling and Data-Driven Motion Planning

This paper provides a kinematic and dynamic analysis of mobile parallel manipulators (MPM). The study is conducted on a composed multi-degree of freedom (DOF) parallel robot carried by a wheeled mobile platform. Both positional and differential kinematics problems for the hybrid structure are solved, and the redundancy problem is solved using joint limit secondary criterion-based generalized-pseudo-inverse. A minimum time trajectory parameterization is obtained via cycloidal profile to initialize multi-objective trajectory planning of the MPM. Considered objectives include time energy minimization redundancy resolution and singularity avoidance. Simulation results illustrating the effectiveness of the proposed approach are presented and discussed.

상지 외골격 로봇 제어를 위한 인체 팔 동작의 기구학 및 동역학적 분석 - 파트 1: 시스템 모델 및 기구학적 제한

To achieve synchronized motion between a wearable robot and a human user, the redundancy must be resolved in the same manner by both systems. According to the seven DOF (Degrees of Freedom) human arm model composed of the shoulder, elbow, and wrist joints, positioning and orientating the wrist in space is a task requiring only six DOFs. Due to this redundancy, a given task can be completed by multiple arm configurations, and thus there exists no unique mathematical solution to the inverse kinematics. This paper presents analysis on the kinematic and dynamic aspect of the human arm movement and their effect on the redundancy resolution of the human arm based on a seven DOF manipulator 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 different 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 for 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 two consecutive points along the task space trajectory. As a first step, the redundancy based on the kinematic criterion will be thoroughly studied based on the motion capture data analysis. Experimental results indicate that by using the proposed redundancy resolution criterion in the kinematic level, error between the predicted and the actual swivel angle acquired from the motor control system is less than five degrees.

Novel Recurrent Neural Network for Time-Varying Problems Solving [Research Frontier

By following the inspirational work of McCulloch and Pitts [1], lots of neural networks have been proposed, developed and studied for scientific research and engineering applications [2][18]. For instance, one classical neural network is Hopfield neural network (HNN) which was proposed by Hopfield in the early 1980s [2]. Another classical neural network is based on the error back-propagation (BP) algorithm, i.e., BP neural network, which was developed by Rumelhart, McClelland and others in the mid-1980s [3]. Generally speaking, according to the nature of connectivity, these neural networks can be classified into two categories: feedback neural networks (or termed recurrent neural networks, RNN) and feed forward neural networks. Recently, due to the in-depth research on neural networks, the artificial neural-dynamic approach based on RNN has been viewed as a powerful alternative to online solution of mathematical problems arising in numerous fields of science and engineering, such as matrix inversion in robots redundancy resolution (as an essential part of the pseudoinversetype solution) [16], [18].

A New Inequality-Based Obstacle-Avoidance MVN Scheme and Its Application to Redundant Robot Manipulators

This paper proposes a new inequality-based criterion/constraint with its algorithmic and computational details for obstacle avoidance of redundant robot manipulators. By incorporating such a dynamically updated inequality constraint and the joint physical constraints (such as joint-angle limits and joint-velocity limits), a novel minimum-velocity-norm (MVN) scheme is presented and investigated for robotic redundancy resolution. The resultant obstacle-avoidance MVN scheme resolved at the joint-velocity level is further reformulated as a general quadratic program (QP). Two QP solvers, i.e., a simplified primal-dual neural network based on linear variational inequalities (LVI) and an LVI-based numerical algorithm, are developed and applied for online solution of the QP problem as well as the inequality-based obstacle-avoidance MVN scheme. Simulative results that are based on PA10 robot manipulator and a six-link planar robot manipulator in the presence of window-shaped and point obstacles demonstrate the efficacy and superiority of the proposed obstacle-avoidance MVN scheme. Moreover, experimental results of the proposed MVN scheme implemented on the practical six-link planar robot manipulator substantiate the physical realizability and effectiveness of such a scheme for obstacle avoidance of redundant robot manipulator.

Integrated sliding-mode algorithms in robot tracking applications

An integrated solution based on sliding mode ideas is proposed for robotic trajectory tracking. The proposal includes three sliding-mode algorithms for speed auto-regulation, path conditioning and redundancy resolution in order to fulfill velocity, workspace and C-space constraints, respectively. The proposed method only requires a few program lines and simplifies the robot user interface since it directly deals with the fulfillment of the constraints to find a feasible solution for the robot trajectory tracking in a short computation time. The proposed approach is evaluated in simulation on the freely accessible 6R robot model PUMA-560, for which the main features of the method are illustrated. Highlights� We develop an integrated solution for robotic trajectory tracking. � We use sliding mode algorithms to fulfill velocity, workspace and C-space constraints. � The approach can be implemented in a few program lines of a microprocessor.

A Single Network Adaptive Critic-Based Redundancy Resolution Scheme for Robot Manipulators

This paper proposes an adaptive critic-based real-time redundancy resolution scheme for kinematic control of a redundant manipulator. The kinematic control of the redundant manipulator is formulated as a discrete-time input affine system, and then, an optimal real-time redundancy resolution scheme is proposed. The optimal control law is derived in real time using an adaptive critic framework. The proposed single network adaptive critic-based methodology defines the additional task in terms of integral cost function which results in global optimal solution. With adaptive critic-based optimal control scheme, the optimal redundancy resolution is achieved in real time without the computation of inverse, which makes the method computationally efficient. The problem formulation as proposed in this paper is first of its kinds. Furthermore, the real-time optimal redundancy resolution using an integral cost function has been comprehensively solved, which is also a novel contribution. The proposed scheme is tested in both simulation and experiment on a seven-degree-of-freedom PowerCube manipulator from Amtec Robotics.

Modeling of a mobile manipulator for redundancy resolution

Purpose Recently, various applications of robotic systems to construction sites have been tried. In this research, a mobile platform-based serial manipulator, the so called ‘mobile manipulator’, is employed for construction tasks such as material handling, beam assembly, welding, and so on. Generally, a serial manipulator has 6 DOF (degrees of freedom). The mobile manipulator suggested in this paper adds 2 or 3 DOF, so that there are a total of 8 or 9 DOF. In 3-dimensional space, the minimum DOF for manipulation is 6. A manipulator that has more DOF than 6 is called a manipulator with ‘redundancy’. While a manipulator with redundancy like the proposed mobile manipulator has several advantages such as large flexibility, singularity avoidance, easy obstacle avoidance, and so on, the redundancy resolution for optimal manipulation is not easy. In this research, modeling of the mobile manipulator for redundancy resolution is studied. Method Since the suggested mobile manipulator is a MIMO(multi-input multi-output) robot system with 8 or 9 links, a state-space model is derived for redundancy resolution. Analysis of kinematics and dynamics for a serial manipulator and a mobile platform is performed. An integration of the serial manipulator and the mobile platform is used. Results & Discussion In this research, a state-space model for the proposed mobile manipulator is derived. This model enables redundancy resolution for optimal solution of 8 or 9 DOF-robot system. In order to solve the redundancy resolution problem, a linear quadratic method, which is one of the conventional optimal control methods, or a reinforcement learning method can be used.

Detection and resolution of semantic inconsistency and redundancy in an automatic ontology merging system

In recent years, researchers have been developing algorithms for the automatic mapping and merging of ontologies to meet the demands of interoperability between heterogeneous and distributed information systems. But, still state-of-the-art ontology mapping and merging systems is semi-automatic that reduces the burden of manual creation and maintenance of mappings, and need human intervention for their validation. The contribution presented in this paper makes human intervention one step more down by automatically identifying semantic inconsistencies in the early stages of ontology merging. We are detecting semantic heterogeneities that occur due to conflicts among the set of Generalized Concept Inclusions, Property Subsumption Criteria, and Constraint Satisfaction Mechanism in local heterogeneous ontologies, which become obstacles for the generation of semantically consistent global merged ontology. We present several algorithms to detect such semantic inconsistencies based on subsumption analysis of concepts and properties in local ontologies from the list of initial mappings. We provide ontological patterns for resolving these inconsistencies automatically. This results global merged ontology free from `circulatory error in class/property hierarchy', `common class between disjoint classes/properties', `redundancy of subclass/subproperty of relations' and other types of `semantic inconsistency' errors. Experiments on the real ontologies show that our algorithms save time and cost of traversing local ontologies, improve system's performance by producing only consistent accurate mappings, and reduce the users' dependability for ensuring the satisfiability of merged ontology.

Redundancy Resolution of the Human Arm and an Upper Limb Exoskeleton

The human arm has 7 degrees of freedom (DOF) while only 6 DOF are required to position the wrist and orient the palm. Thus, the inverse kinematics of an human arm has a nonunique solution. Resolving this redundancy becomes critical as the human interacts with a wearable robot and the inverse kinematics solution of these two coupled systems must be identical to guarantee an seamless integration. The redundancy of the arm can be formulated by defining the swivel angle, the rotation angle of the plane defined by the upper and lower arm around a virtual axis that connects the shoulder and wrist joints. Analyzing reaching tasks recorded with a motion capture system indicates that the swivel angle is selected such that when the elbow joint is flexed, the palm points to the head. Based on these experimental results, a new criterion is formed to resolve the human arm redundancy. This criterion was implemented into the control algorithm of an upper limb 7-DOF wearable robot. Experimental results indicate that by using the proposed redundancy resolution criterion, the error between the predicted and the actual swivel angle adopted by the motor control system is less then 5°.

A General User-Oriented Framework for Holonomic Redundancy Resolution in Robotic Manipulators Using Task Augmentation

Redundant robotic manipulators under kinematic control may exhibit unpredictable behaviors at the joint level. When the end effector describes a closed trajectory, the joint angles may not return to their initial values. Likewise, final configuration in the joint space may depend on the trajectory that is followed by the end effector. In this paper, a complete parameterization of holonomic redundancy resolution techniques that avoid these problems is proposed. The flexibility of the proposed approach is discussed. In particular, it is shown that the selection of the redundancy resolution criterion is totally decoupled from the implementation of a closed-loop inverse kinematics (CLIK) algorithm. Any user-defined redundancy resolution criterion can, thus, be enforced. Potentialities of this new methodology are experimentally verified on an industrial robot in a case study where functional redundancy occurs and is applied in simulation on a 7-degree-of-freedom (7-DOF) anthropomorphic manipulator.

Effective parameter range for equivalence of velocity-level and acceleration-level redundancy resolution schemes

This Letter shows the equivalence of the velocity-level and acceleration-level redundancy resolution schemes by using so-called Zhang et al.ʼs neural-dynamic method. In addition, the equivalence requires that a design parameter be sufficiently large. But how large should it be? The effective range of such a design parameter is thus determined by means of the 2%-difference rule and the runtime of the computer-simulations.

Different-level two-norm and infinity-norm minimization to remedy joint-torque instability/divergence for redundant robot manipulators

To remedy the joint-torque instability/divergence phenomenon arising in the conventional infinity-norm torque-minimization (INTM) scheme, and prevent the occurrence of high joint-velocity and joint-acceleration as well, a different-level bi-criteria minimization scheme is proposed and investigated in this paper for redundant robot manipulators with physical constraints (e.g., joint-angle limits, joint-velocity limits and joint-acceleration limits) considered. Such a scheme combines the minimum two-norm joint-velocity and infinity-norm joint-torque solutions via a weighting factor, which guarantees the final joint-velocity of the motion to be near zero (more acceptable for engineering application). In addition, the different-level scheme is reformulated as a general quadratic program (QP) and resolved at the joint-acceleration level. Computer-simulation results based on the PUMA560 robot manipulator further demonstrate the effectiveness and advantages of the proposed different-level bi-criteria minimization scheme for robotic redundancy resolution.

Automated Milling Path Tracking and CAM-ROB Integration for Industrial Redundant Manipulators

The present paper explores the industrial capabilities of a CAM-ROB system implementation based on a commercial CAD/CAM system (NX™) for an industrial robotic workcell of eight joints, committed to the rapid prototyping of 3D CAD-defined models. The workcell consists of a KUKA™ KR15/2 manipulator assembled on a linear track and synchronized with a rotary table. A redundancy resolution scheme is developed to deal with the redundancies due to the additional joints of the robot, plus the one from the symmetry axis of the milling tool. During the path tracking, the use of these redundancies is optimized by adjusting two performance criterion vectors related to singularity avoidance and maintenance of a preferred reference posture, as secondary tasks to be done. In addition, two suitable fuzzy inference engines adjust the weight of each joint in these tasks. The developed system is validated in a real prototyping of a carving.

CAM-Rob postprocessor based on a fuzzified redundancy resolution scheme

This work highlights the applicability of different redundancy resolution schemes to the postprocessing stage from a computer-aided manufacturing (CAM) system to an industrial redundant workcell. The inverse kinematic problem for redundant manipulators is not straightforward and, therefore, it is commonly solved using an iteratively approach based on redundant resolution schemes at the velocity level. In this work, two conceptions of redundancy resolution schemes are evaluated and a novel fuzzy inference system is developed to improve the performance during the toolpath tracking in order to avoid singularities and to maintain a preferred reference posture. For this purpose, the fuzzy inference engine properly adjusts the weight of each joint in the calculation of the performance criterion vectors. The proposed approach is validated in the real prototyping of a windmill blade mold using a KUKA KR15/2 manipulator mounted on a linear track and synchronized with a rotary table. To the authors’ knowledge, the proposed method and the results shown are novel in the context of postprocessing techniques from CAM systems to industrial robots devoted to milling works. With the same guidelines, the postprocessor programmed inside the CAM system is expected to be easily applicable not only to other industrial robots, but also for different applications such as welding or painting labors.

Model-based control of redundantly actuated parallel manipulators in redundant coordinates

Model-based control of parallel kinematics machines (PKM) relies on computationally efficient formulations in terms of a set of independent joint coordinates. Since PKM models are commonly expressed in terms of actuator or end-effector coordinates the models are not valid at input- or output-singularities, respectively. Moreover input-singularities limit the motion range of PKM. Actuation redundancy is a means to increase the singularity-free range of motion. However, due to the redundancy only a subset of the actuator coordinates constitute independent coordinates. This subset corresponds to the actuator coordinates of the non-redundant PKM, which does generally not constitute proper minimal coordinates for the entire workspace. Hence a redundantly actuated PKM (RA-PKM) controlled by a model-based controller in terms of minimal coordinates would exhibit the same limitations as the non-redundant PKM. One approach to tackle this problem is to switch between different minimal coordinates, i.e., different motion equations are used within the controller.In this contribution a computed torque and augmented PD control scheme in redundant coordinates is proposed, as an alternative to coordinate switching, and applied to the control of redundantly actuated PKM. That is, no minimal coordinates are selected. This novel formulation is numerically robust and does not suffer from input- or output-singularities. Even more the formulation is always valid except at configuration space singularities. For the redundancy resolution within the inverse dynamics the pseudoinverse of a rank deficient matrix is required, for which an explicit formulation is presented. For both controllers exponential trajectory tracking is shown. Experimental results are reported for a planar 2 DOF RA-PKM.

An Analytic-Iterative Redundancy Resolution Scheme for Cable-Driven Redundant Parallel Manipulators

In this paper, redundancy resolution of a cable-driven parallel manipulator is performed through an analytic-iterative scheme. The redundancy resolution scheme is formulated as a convex optimization problem with inequality constraints that are imposed by manipulator structure and cable dynamics. The Karush-Kuhn-Tucker theorem is used to analyze the optimization problem and to draw an analytic-iterative solution for it. Subsequently, a tractable and iterative search algorithm is proposed to implement the redundancy resolution of such redundant manipulators. Furthermore, it is shown through simulations that the worst case and average elapsed time that is required to implement the proposed redundancy resolution scheme in a closed-loop implementation is considerably less than that of other numerical optimization methods.

기구학적 여유도와 ZMP 구속 조건을 이용한 매니퓰레이터의 동작 계획

This work deals with development of effective redundancy resolution algorithms for the motion control of manipulator. Differently from the typical kinematically redundant robots that are attached to the fixed ground, the ZMP condition should be taken into account in the manipulator motion in order to guarantee the system stability. In this paper, a new motion planning algorithm for redundant manipulator not fixed to the ground is introduced. A sequential redundancy resolution algorithm is proposed, which ensures the ZMP (Zero Moment Point) stability, the planned operational motion, and additional sub-criteria such as joint limit index. A geometric constraint equation derived by reshaping the existing ZMP equation enables one to employ the sequential redundancy algorithm. The feasibility of the proposed algorithm is verified by simulating a redundant manipulator model.