DOF Robotic Arm Research Articles

3자유도 결합 팔을 가진 모듈형 로봇의 비평탄 지형 주행 알고리즘

본 논문에서는 미지 환경의 비평탄 지형에서 로봇 주행에 제한이 따르는 문제를 극복하기 위해 물리적 결합을 통한 모듈형 로봇의 이동성 향상 기법을 제안한다. 모듈형 로봇 시스템은 자가 결합 기구 장치, 3자유도 로봇 결합 팔, 모듈 로봇 플랫폼, 모션제어기, 주제어기로 구성하였으며, 로봇간 결합 및 협업을 위해 RGGR구조의 결합 메카니즘을 사용하였다. 또한 실내 GPS를 사용하여 로봇의 실시간 위치 및 진행 방향을 추정하고, 추정된 정보를 기반으로 로봇의 이동 경로와 방향을 제어하여 로봇간 물리적 결합을 수행하였다. 물리적 결합을 통한 모듈형 로봇의 비평탄 지형 주행 알고리즘의 성능을 검증하기 위해 세 대의 모듈형 로봇을 제작하여 실제 끊어진 도로와 단턱이 존재하는 환경에서 실험을 통해 검증하였다. In the paper, we propose an improved mobility method of modular robots by physical docking in the uneven environments. The modular robot system consists of autonomous docking device, 3 DOF robotic arm, motion controller, and main controller. Real-time location and direction of the robot are estimated using inner GPS and they are used to control direction and path of each robot for physical docking between modular robots. We design a navigation algorithm of modular robot using physical docking and cooperative navigation in the environment with broken road and low stair. The proposed method is verified by navigation experiments of three developed modular robots in the uneven environments.

ニューロサージカル ･ モーションベースの開発

Neurosurgical motion base is a 8 DOF robotic arm for positioning a surgical tool. In this paper, its overview, kinematics, prototype implementation, mechanical evaluation and clinical set-up test using a patient phantom model are described. The neurosurgical motion base is developed as a mount-type surgical robot on a conventional surgical head frame, therefore, it is possible to realize a quick installation. In our concept, the neurosurgical motion base holds and positions a cutting-edge surgical tool including a sensor feedback suction tool and a 3D endoscope. In the neurosurgical motion base, a 4 DOF active mechanism is introduced for precise positioning of a surgical tool, and a 4 DOF passive mechanism is introduced for adjusting the robot position on the head frame. By introducing a new 3 DOF parallel mechanism into the active part of neurosurgical motion base, high rigidity and accuracy can be realized, and also it is possible to locate all actuators on the base part for a capability of sterilization. From mechanical evaluations, it was revealed that accuracy and rigidity of the developed robot were 0.04 mm and 6.5 N/mm respectively. In addition, by an installation test on a patient phantom model, a feasibility of the developed robot was positively shown.

TH-C-BRC-08: Integration of Cone Beam CT Imaging and a Small Animal Conformal RT Device Using a 6DOF Robotic Arm

Purpose: To integrate cone beam computed tomography(CBCT)imaging technology into a small animal conformal radiation therapy device (SACRTD) using a six degrees of freedom (6DOF) high accuracy/precision robotic arm. Method and Materials: A 225kV dual focus (0.4mm and 3mm) X‐ray tube (GE Seifert Isovolt Titan) is used for both radiation and imaging. An industrial robot (Adept Viper S650) is used for positioning and motion of a small animal for targeted radiation delivery and CBCTimaging with positional repeatability of ±0.020 mm in XYZ direction and angular precision of ±0.2°. A series of 2D‐radiographic projection images were recorded in cone beam mode with a flat panel amorphous silicon detector (Perking Elmer), which captures 1024 by 1024 pixels images (200μm pixel size) at a frame rate of 7.5Hz. An open source CBCTreconstruction tool (OSCAR‐2, University of Toronto) using Feldkamp‐Davis‐Kress (FDK) filtered back projection algorithm was employed for CBCTimage reconstruction. Results: Initial testing of the CBCT system using phantoms and mice was successful. Radiographs were cropped and downsampled to a spatial resolution of 0.4mm. Reconstructed matrices had dimensions of 181×267×97 voxels with 0.3mm×0.3mm×0.3mm voxel spacing. With the current computer the entire acquisition and reconstruction procedure takes > 7 minutes. The OSCAR‐2 algorithm offers various reconstruction filters, but more sophisticated tools specific for small animal imaging are needed to improve the quality of CBCTimages. Conclusion: This work demonstrated that a 6DOF robot is an excellent device for moving/rotating a small laboratory animal for acquiring high spatial resolutionradiographs with the necessary accuracy and precision for successful CBCTreconstruction. It also demonstrated that an open source CBCT software tool could be adapted to work with the SACRTD. The integration of CBCTimaging into the SACRTD opens new research paths requiring image guided radiation delivery to specific structures/volumes in small animals.

TU‐C‐BRD‐03: An Integrated Robotic‐Based Irradiation System for Small Animal Research

An integrated, image‐guided irradiation system for small animal research has been developed. The system is capable of precise, accurate, reproducible and quantifiable 3D conformal delivery of radiation dose distributions to organs/tumors. The main hardware components are: (1) A Seifert Isovolt Titan 225 kV X‐ray tube with beam collimation provided by a custom‐made variable diameter “cone” or a set of motor‐driven symmetric “jaws”, thereby allowing field sizes from 0.5 mm in diameter to 7 cm square field at ∼33.5 cm SSD. (2) A six‐degrees‐of‐freedom (6DOF) robotic arm (Adept Viper s650) was integrated for precise animal positioning/motion (repeatability of ±0.020 mm in XYZ direction and angular precision of ±0.2°). The system is housed in a custom 6 × 6 × 6 ft3 shielded enclosure inside a laboratory. When the beam is aimed horizontally to (3) a flat panel amorphous silicon detector (XRD 0820 CN3, Perkin Elmer, Fremont, CA) a series of 2D‐radiographs can be recorded while the robot rotates the animal. Each image is composed of 1024 by 1024 pixels with a 200 μm pixel size at a frame rate of 7.5 Hz. An open source cone beam computed tomography (CBCT) reconstruction tool (OSCAR‐2, University of Toronto) using the Feldkamp‐Davis‐Kress (FDK) filtered back projection algorithm was implemented for CBCT image reconstruction. Thus, targeting can be accomplished by the use of orthogonal radiographs and/or CBCT. A dose engine and CBCT‐based treatment planning are ongoing projects.Dosimetric measurements and preliminary animal experiments have demonstrated the basic capabilities of the system in terms of radiation dose, dose rate and precision targeting. The system has also been used successfully in experiments to detect the molecular signaling occurring after spatially fractionated radiation therapy (GRID) in vivo.A description of the system and a summary of experiments performed to date will be presented.Learning Objectives:1. Appreciate the challenges of developing a high precision 3D conformal irradiator for small animals.2. Learn about the main hardware components of the system and their integration.3. Understand the advantages of using a 6DOF robot for imaging (motion) and beam delivery (positioning).4. Learn about some of the potential research projects that such a system can make possible.This research was sponsored by the Arkansas Biosciences Institute and the Central Arkansas Radiation Therapy Institute.

Recursive Control of a 7 dof Robotic Arm

AbstractThis paper provides a short insight into tracking control of uncertain nonlinear plants with the focus on ease of application and simple parameter tuning. The advanced control scheme is based on recursive control laws which have been shown in the last decades to be efficient in control of robotic motion. The algorithm is recursive rapidly convergent and robust. The development of humanoid robotic arms within the collaborative research center (CRC588) requires a class of easy implementable and robust nonlinear controllers to accomplish manipulation tasks in permanently alternating scenarios. The ability of the controller is tested by simulation for several robotic arms with increasing dof and variable end–effector mass for given human arm movements provided from a motion capture system. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Fractional Fuzzy Adaptive Sliding-Mode Control of a 2-DOF Direct-Drive Robot Arm

This paper presents a novel parameter adjustment scheme to improve the robustness of fuzzy sliding-mode control achieved by the use of an adaptive neuro-fuzzy inference system (ANFIS) architecture. The proposed scheme utilizes fractional-order integration in the parameter tuning stage. The controller parameters are tuned such that the system under control is driven toward the sliding regime in the traditional sense. After a comparison with the classical integer-order counterpart, it is seen that the control system with the proposed adaptation scheme displays better tracking performance, and a very high degree of robustness and insensitivity to disturbances are observed. The claims are justified through some simulations utilizing the dynamic model of a 2-DOF direct-drive robot arm. Overall, the contribution of this paper is to demonstrate that the response of the system under control is significantly better for the fractional-order integration exploited in the parameter adaptation stage than that for the classical integer-order integration.

Adaptive Neuro-Fuzzy Inference System based control of six DOF robot manipulator

The dynamics of robot manipulators are highly nonlinear with strong couplings existing between joints and are frequently subjected to structured and unstructured uncertainties. Fuzzy Logic Controller can very well describe the desired system behavior with simple if-then relations owing the designer to derive if-then rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). This paper presents the control of six degrees of freedom robot arm (PUMA Robot) using Adaptive Neuro Fuzzy Inference System (ANFIS) based PD plus I controller. Numerical simulation using the dynamic model of six DOF robot arm shows the effectiveness of the approach in trajectory tracking problems. Comparative evaluation with respect to PID, Fuzzy PD+I controls are presented to validate the controller design. The results presented emphasize that a satisfactory tracking precision could be achieved using ANFIS controller than PID and Fuzzy PD+I controllers Abstract The dynamics of robot manipulators are highly nonlinear with strong couplings existing between joints and are frequently subjected to structured and unstructured uncertainties. Fuzzy Logic Controller can very well describe the desired system behavior with simple if-then relations owing the designer to derive if-then rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). This paper presents the control of six degrees of freedom robot arm (PUMA Robot) using Adaptive Neuro Fuzzy Inference System (ANFIS) based PD plus I controller. Numerical simulation using the dynamic model of six DOF robot arm shows the effectiveness of the approach in trajectory tracking problems. Comparative evaluation with respect to PID, Fuzzy PD+I controls are presented to validate the controller design. The results presented emphasize that a satisfactory tracking precision could be achieved using ANFIS controller than PID and Fuzzy PD+I controllers Abstract The dynamics of robot manipulators are highly nonlinear with strong couplings existing between joints and are frequently subjected to structured and unstructured uncertainties. Fuzzy Logic Controller can very well describe the desired system behavior with simple if-then relations owing the designer to derive if-then rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). This paper presents the control of six degrees of freedom robot arm (PUMA Robot) using Adaptive Neuro Fuzzy Inference System (ANFIS) based PD plus I controller. Numerical simulation using the dynamic model of six DOF robot arm shows the effectiveness of the approach in trajectory tracking problems. Comparative evaluation with respect to PID, Fuzzy PD+I controls are presented to validate the controller design. The results presented emphasize that a satisfactory tracking precision could be achieved using ANFIS controller than PID and Fuzzy PD+I controllers

Study on Grasping Motion of Multi DOF Robot Arm and Hand with Dynamic Visual Servo System

Study on Grasping Motion of Multi DOF Robot Arm and Hand with Dynamic Visual Servo System

Comparison of null-space and minimal null-space control algorithms

SUMMARYThis paper deals with the stability of null-space velocity control algorithms in extended operational space for redundant robots. We compare the performance of the control algorithm based on the minimal null-space projection and generalized-inverse-based projection into the Jacobian null-space. We show how the null-space projection affects the performance of the null-space tracking algorithm. The results are verified with the simulation and real implementation on a redundant mobile robot composed of 3 degrees of freedom (DOFs) mobile platform and 7-DOF robot arm.

554 逐次検索法による6自由度マニピュレータの逆運動学

Analitical solution of the inverse kinematics has been found only for some particular structures of 6-DOF serial manipulator. We propose the Method of Sequential Retrieval by modifying the Effective Solution for Inverse Kinematics to solve the inverse kinematics for a 6-DOF robot arm which has not been analytically solved yet. We confirmed that our method can find some solutions which can not be solved by the Effective Solution for Inverse Kinematics. Moreover, we presented the effectiveness of our method by comparing its convergence speed with that of the Newton-Raphson Method.

腱駆動ロボットの作業に適した関節剛性のＧＡによる学習

A tendon-driven robot mechanism allows us to control the joint stiffness independantly of the joint torques. Generally, the optimal joint stiffness for desired tasks cannot be found easily. So we adopt a Radial Basis Function Network (RBFN) to describe the trajectory of the stiffness matrix, and modify the parameters using Genetic Algorithm (GA) to find the optimal trajectories. As typical tasks that have impulsive disturbance forces, we choose ball hitting and receiving tasks. The optimal joint stiffness for the hitting task gives the ball the fastest initial speed after the hit, on the other hand, the one for the receiving gives the slowest speed. We use a 2 DOF robotic arm driven with 6 tendons, because we can adjust all of elements of the joint stiffness matrix independently of the joint torques. After modifying conventional GA to fit it for real robot experiments, we make two kinds of experiments given above and show the results.

Control of a Macro-Micro Robot System Using Manipulability of the Micro Robot

A 3’DOF robot finger is fixed to the end-effector of a 6’DOF robot arm to construct a redundant macro-micro robot system that is able to emulate the finger-arm system of a human being. However, determining joint angles and velocities of a redundant robot system in a natural way remains a difficult issue. This paper describes a method to control the system so as to generate common motions emulating the hand-arm movement of a human. To achieve this purpose, the finger is primarily supposed to complete delicate local motions while the arm moves to assist the finger when the manipulability of the finger is too low and the system is about to miss the desired target. The effectiveness on motion generation and the relation between kinetic energy and manipulability by using the proposed method has been experimentally demonstrated and discussed.

Application of a neural network to the generation of a robot arm trajectory

We propose a neural network model generating a robot arm trajectory. The developed neural network model is based on a recurrent-type neural network (RNN) model calculating the proper arm trajectory based on data acquired by evaluation functions of human operations as the training data. A self-learning function has been added to the RNN model. The proposed method is applied to a 2-DOF robot arm, and laboratory experiments were executed to show the effectiveness of the proposed method. Through experiments, it is verified that the proposed model can reproduce the arm trajectory generated by a human. Further, the trajectory of a robot arm is successfully modified to avoid collisions with obstacles by a self-learning function.

A Combined Solution of the Inverse Kinematic Task in the Vicinity of the Singularities

In the case of the traditional differential approach of solving the inverse kinematic task, depending on the desired trajectory that may approach, reach, and cross the boundaries of non-singular domains or domains of different degree of singularity, certain joint velocities may take huge values. Within the singularity it may happen that the desired task cannot exactly be solved, and that this imprecise solution has one or more degree of freedom in its ambiguity. The motion planner provided with end-point position and orientation data with respect to the workshop’s Cartesian frame does not have satisfactory information for locally resolving ambiguity. The rather modern approaches follow global way of thinking and apply Genetic Algorithms or similar evolutionary methods requiring huge computational power. The here presented approach is a compromise between these essentially different cases. For non-singular points it applies a fast algorithm to the differential solution, while near and in the singularities the Complex Algorithm combining the primary (that is tracking the prescribed data as precisely as possible) and secondary (complementary aspects applied in ambiguity resolution) requirements in an efficient way. The method is illustrated by calculations for particular trajectories in the case of a 6 DOF robot arm.

Smart Optimization of Machine Systems Using Hierarchical Genotype Representations

Design problems for machine products are generally hierarchically expressed. With conventional product optimization methods, however, it is difficult to concurrently optimize all design variables of portions within such hierarchical structures. This paper proposes a design optimization method using genetic algorithms containing hierarchical genotype representations, so that the hierarchical structures of machine system designs are exactly expressed through genotype coding, and optimization can be concurrently conducted for all of the hierarchical structures. Crossover and mutation operations for manipulating the hierarchical genotype representations are also developed. The proposed method is applied to a machine-tool structural design and a 2 DOF robot arm design to demonstrate its effectiveness.

Origins and violations of the 2/3 power law in rhythmic three-dimensional arm movements.

The 2/3 power law, the nonlinear relationship between tangential velocity and radius of curvature of the end-effector trajectory, is thought to be a fundamental constraint of the central nervous system in the formation of rhythmic endpoint trajectories. However, studies on the 2/3 power law have been confined largely to planar drawing patterns of relatively small size. With the hypothesis that this strategy overlooks nonlinear effects that are constitutive in movement generation, the present experiments tested the validity of the power law in elliptical patterns that were not confined to a planar surface and which were performed by the unconstrained 7-degrees of freedom (DOF) arm, with significant variations in pattern size and workspace orientation. Data were recorded from five human subjects where the seven joint angles and the endpoint trajectories were analyzed. Additionally, an anthropomorphic 7-DOF robot arm served as a "control subject" whose endpoint trajectories were generated on the basis of the human joint angle data, modeled as simple harmonic oscillations. Analyses of the endpoint trajectories demonstrate that the power law is systematically violated with increasing pattern size, in both exponent and the goodness of fit. The origins of these violations can be explained analytically based on smooth, rhythmic trajectory formation and the kinematic structure of the human arm. We conclude that, in unconstrained rhythmic movements, the power law seems to be a by-product of a movement system that favors smooth trajectories, and that it is unlikely to serve as a primary movement-generating principle. Our data rather suggest that subjects employed smooth oscillatory pattern generators in joint space to realize the required movement patterns.

A Planning System for Object Reconfiguration with Robotic Hand and the Real Time Realization of the Motion

An object manipulation algorithm for a three-fingered dextrous robotic hand mounted on a 6 DOF robotic arm is proposed. The paper is divided into two parts. The first part describes a (currently) off-line planning system for object reconfiguration design based on simulated annealing, while the second part deals with the real time realization of the determined motion path. The presented model-based motion planner algorithm for object re-configuration uses simulated annealing (SA) algorithm for generating the relative motion between the object and the end-points of the fingers.

Object Reconfiguration with Multiple Robotic Arms Using An Additional Static Contact Surface

An algorithm of manipulation of a geometrically constrained object with multiple 6 DOF robotic arms in 3D is proposed. The motion of the end effectors relative to the object can be pure sliding and pure rolling. In the algorithm a static surface of the environment (working table, etc.) can be applied to make contact with the object, so an additional contact point may be used to help the manipulation. The presented model-based motion planner algorithm for object reconfiguration uses simulated annealing (SA) algorithm for generating the relative motion between the object and the end-points of the manipulators.

Application of Lie Algebras to Visual Servoing

A novel approach to visual servoing is presented, which takes advantage of the structure of the Lie algebra of affine transformations. The aim of this project is to use feedback from a visual sensor to guide a robot arm to a target position. The target position is learned using the principle of ‘teaching by showing’ in which the supervisor places the robot in the correct target position and the system captures the necessary information to be able to return to that position. The sensor is placed in the end effector of the robot, the ‘camera-in-hand’ approach, and thus provides direct feedback of the robot motion relative to the target scene via observed transformations of the scene. These scene transformations are obtained by measuring the affine deformations of a target planar contour (under the weak perspective assumption), captured by use of an active contour, or snake. Deformations of the snake are constrained using the Lie groups of affine and projective transformations. Properties of the Lie algebra of affine transformations are exploited to provide a novel method for integrating observed deformations of the target contour. These can be compensated with appropriate robot motion using a non-linear control structure. The local differential representation of contour deformations is extended to allow accurate integration of an extended series of small perturbations. This differs from existing approaches by virtue of the properties of the Lie algebra representation which implicitly embeds knowledge of the three-dimensional world within a two-dimensional image-based system. These techniques have been implemented using a video camera to control a 5 DoF robot arm. Experiments with this implementation are presented, together with a discussion of the results.

KARES: Intelligent wheelchair-mounted robotic arm system using vision and force sensor

KARES, an acronym of KAIST Rehabilitation Engineering System, is the intelligent rehabilitation system with a 6 DOF robotic arm mounted on the powered wheelchair. It is developed to assist the disabled and the elderly for the independent activities. Human-machine interaction is a key issue of design for intelligent systems such as KARES. A special attention is paid in bestowing certain degree of autonomy to the robotic sub-system since the direct control of the robotic arm takes a high cognitive load on the user part while physically disabled persons may have difficulties in dexterously operating a joystick or push buttons for delicate movements. To perceive environment, one color vision sensor and one force/torque sensor are mounted on the end-effector of the robotic arm of KARES. To test the system, four basic tasks are defined as picking up a cup on the table, picking up a pen on the floor, moving an object to the user's face, and operating a switch on the wall. These tasks are performed autonomously in a semi-structured environment.