Anthropomorphic Robot Manipulator Research Articles

Inverse Kinematics of an Anthropomorphic 6R Robot Manipulator Based on a Simple Geometric Approach for Embedded Systems

This manuscript presents an efficient algorithm for solving the inverse kinematics problem of a 6R robot manipulator to be deployed on embedded control hardware. The proposed method utilizes the geometric relationship between the end-effector and the base of the manipulator, resulting in a computationally efficient solution. The approach aims to minimize computational complexity and memory consumption while maintaining the accuracy and real-time performance demonstrated by simulations and verified by experimental results on an embedded system. Furthermore, the manipulator is analyzed in terms of singularities, limits, the workspace, and general solvability. Due to the simplicity of the algorithm, a platform-independent implementation is possible. As a result, the average calculation time is reduced by a factor of five to eight and the average error is decreased by a factor of fifty compared to a powerful analytical solver.

A Robust Visual Servoing Controller for Anthropomorphic Manipulators With Field-of-View Constraints and Swivel-Angle Motion: Overcoming System Uncertainty and Improving Control Performance

Human–robot collaboration has attracted significant attention in the industry due to the flexibility of humans and the accuracy of robots. Humanoid control of anthropomorphic robotic arms combined with visual servoing will enhance the intelligence of industrial robots. However, the robotic manipulator will introduce psychological discomfort to nearby humans, and the loss of visual features will induce visual servoing task failure. Aiming at these problems, this article proposes a humanoid control method based on visual servoing by utilizing the swivel angle derived from the human arm to realize the human-like behavior of anthropomorphic robot manipulators. To advance the visual servoing control performance, a function constraint is designed with the barrier Lyapunov function (BLF) to ensure that image features stay within the field of view (FoV). The sliding mode control (SMC) is combined with image-based visual servoing (IBVS) to dispose of the uncertainties of a seven-degree-of-freedom (7-DoF) redundant robot manipulator. The proposed algorithm is substantiated through comparison experiments based on the Sawyer robot and constructed visual servoing physical platform.

Super-Twisting Control for trajectory tracking of a four-degree of freedom anthropomorphic robot manipulator

This work solves the regulation and tracking trajectories tasks for four degrees of freedom anthropomorphic robot manipulators. Two controllers are considered: a Super-Twisting controller (ST) and a Proportional Derivative with dynamics compensation (PD+) control. This comparison is carried out through numeric simulation of the dynamic model in the presence of disturbances using Matlab-Simulink software. Also, the tuning procedure of each controller is shown, as well as the stability criteria used for each case. The tunning of the ST controller is done considering the effects produced by an unknown Lipschitz disturbance; this guarantees robustness against this kind of disturbance. The results of the ST controller show the rejection of the disturbance, allowing the correct trajectory tracking. An algorithm based on the inverse kinematics solution is used to generate trajectories and their interpretation in generalized coordinates corresponding to the manipulator’s joint positions obtained through the geometric approach. In addition, we show the workspace, the manipulator parameterization, and the manipulator dynamic model through the Euler-Lagrange motion equations.

An Incremental Learning Framework for Human-Like Redundancy Optimization of Anthropomorphic Manipulators

Recently, the human-like behavior on the anthropomorphic robot manipulator is increasingly accomplished by the kinematic model establishing the relationship of an anthropomorphic manipulator and human arm motions. Notably, the growth and broad availability of advanced data science techniques facilitate the imitation learning process in anthropomorphic robotics. However, the enormous dataset causes the labeling and prediction burden. In this article, the swivel motion reconstruction approach was applied to imitate human-like behavior using the kinematic mapping in robot redundancy. For the sake of efficient computing, a novel incremental learning framework that combines an incremental learning approach with a deep convolutional neural network is proposed for fast and efficient learning. The algorithm exploits a novel approach to detect changes from human motion data streaming and then evolve its hierarchical representation of features. The incremental learning process can fine-tune the deep network only when model drifts detection mechanisms are triggered. Finally, we experimentally demonstrated this neural network's learning procedure and translated the trained human-like model to manage the redundancy optimization control of an anthropomorphic robot manipulator (LWR4+, KUKA, Germany). This approach can hold the anthropomorphic kinematic structure-based redundant robots. The experimental results showed that our architecture could not only enhance the regression accuracy but also significantly reduce the processing time of learning human motion data.

Self-Configuring Robot Path Planning With Obstacle Avoidance via Deep Reinforcement Learning

This letter proposes a hybrid control methodology to achieve full body collision avoidance in anthropomorphic robot manipulators. The proposal improves classical motion planning algorithms by introducing a Deep Reinforcement Learning (DRL) approach trained ad hoc for performing obstacle avoidance, while achieving a reaching task in the operative space. More specifically, a switching mechanism is enabled whenever a condition of proximity to the obstacles is met, thus conferring to the dual-mode architecture a self-configuring capability in order to cope with objects unexpectedly invading the workspace. The proposal has been finally tested relying on a realistic robot manipulator simulated in a V-REP environment.

Deep Neural Network Approach in Human-Like Redundancy Optimization for Anthropomorphic Manipulators

Human-like behavior has emerged in the robotics area for improving the quality of Human-Robot Interaction (HRI). For the human-like behavior imitation, the kinematic mapping between a human arm and robot manipulator is one of the popular solutions. To fulfill this requirement, a reconstruction method called swivel motion was adopted to achieve human-like imitation. This approach aims at modeling the regression relationship between robot pose and swivel motion angle. Then it reaches the human-like swivel motion using its redundant degrees of the manipulator. This characteristic holds for most of the redundant anthropomorphic robots. Although artificial neural network (ANN) based approaches show moderate robustness, the predictive performance is limited. In this paper, we propose a novel deep convolutional neural network (DCNN) structure for reconstruction enhancement and reducing online prediction time. Finally, we utilized the trained DCNN model for managing redundancy control a 7 DoFs anthropomorphic robot arm (LWR4+, KUKA, Germany) for validation. A demonstration is presented to show the human-like behavior on the anthropomorphic manipulator. The proposed approach can also be applied to control other anthropomorphic robot manipulators in industry area or biomedical engineering.

Online human-like redundancy optimization for tele-operated anthropomorphic manipulators

Robot human-like behavior can enhance the performance of human–robot cooperation with prominently improved natural interaction. This also holds for redundant robots with an anthropomorphic kinematics. In this article, we translated human ability of managing redundancy to control a seven degrees of freedom anthropomorphic robot arm (LWR4+, KUKA, Germany) during tele-operated tasks. We implemented a nonlinear regression method—based on neural networks—between the human arm elbow swivel angle and the hand target pose to achieve an anthropomorphic arm posture during tele-operation tasks. The method was assessed in simulation and experiments were performed with virtual reality tracking tasks in a lab environment. The results showed that the robot achieves a human-like arm posture during tele-operation, and the subjects prefer to work with the biologically inspired robot. The proposed method can be applied in control of anthropomorphic robot manipulators for tele-operated collaborative tasks, such as in factories or in operating rooms.

A teleoperated control approach for anthropomorphic manipulator using magneto-inertial sensors.

In this paper we propose and validate a teleoperated control approach for an anthropomorphic redundant robotic manipulator, using magneto-inertial sensors (IMUs). The proposed method allows mapping the motion of the human arm (used as the master) on the robot end-effector (the slave). We record arm movements using IMU sensors, and calculate human forward kinematics to be mapped on robot movements. In order to solve robot kinematic redundancy, we implemented different algorithms for inverse kinematics that allows imposing anthropomorphism criteria on robot movements. The main objective is to let the user to control the robotic platform in an easy and intuitive manner by providing the control input freely moving his/her own arm and exploiting redundancy and anthropomorphism criteria in order to achieve human-like behaviour on the robot arm. Therefore, three inverse kinematics algorithms are implemented: Damped Least Squares (DLS), Elastic Potential (EP) and Augmented Jacobian (AJ). In order to evaluate the performance of the algorithms, four healthy subjects have been asked to control the motion of an anthropomorphic robot arm (i.e. the Kuka Light Weight Robot 4+) through four magneto-inertial sensors (i.e. Xsens Wireless Motion Tracking sensors - MTw) positioned on their arm. Anthropomorphism indices and position and orientation errors between the human hand pose and the robot end-effector pose were evaluated to assess the performance of our approach.

Design of an Integral Suboptimal Second-Order Sliding Mode Controller for the Robust Motion Control of Robot Manipulators

The formulation of an integral suboptimal second-order sliding mode ((ISSOSM) control algorithm, oriented to solve motion control problems for robot manipulators, is presented in this paper. The proposed algorithm is designed so that the so-called reaching phase, normally present in the evolution of a system controlled via the sliding mode approach, is reduced to a minimum. This fact makes the algorithm more suitable to be applied to a real industrial robot, since it enhances its robustness, by extending it also to time intervals during which the classical sliding mode is not enforced. Moreover, since the algorithm generates second-order sliding modes, while the model of the controlled electromechanical system has a relative degree equal to one, the control action actually fed into the plant is continuous, which provides a positive chattering alleviation effect. The assessment of the proposal has been carried out by experimentally testing it on a COMAU SMART3-S2 anthropomorphic industrial robot manipulator. The satisfactory experimental results, also compared with those obtained with a standard proportional-derivative controller and with the original suboptimal algorithm, confirm that the new algorithm can actually be used in an industrial context.

Staged Development of Robot Skills: Behavior Formation, Affordance Learning and Imitation with Motionese

Inspired by infant development, we propose a three staged developmental framework for an anthropomorphic robot manipulator. In the first stage, the robot is initialized with a basic reach-and- enclose-on-contact movement capability, and discovers a set of behavior primitives by exploring its movement parameter space. In the next stage, the robot exercises the discovered behaviors on different objects, and learns the caused effects; effectively building a library of affordances and associated predictors. Finally, in the third stage, the learned structures and predictors are used to bootstrap complex imitation and action learning with the help of a cooperative tutor. The main contribution of this paper is the realization of an integrated developmental system where the structures emerging from the sensorimotor experience of an interacting real robot are used as the sole building blocks of the subsequent stages that generate increasingly more complex cognitive capabilities. The proposed framework includes a number of common features with infant sensorimotor development. Furthermore, the findings obtained from the self-exploration and motionese guided human-robot interaction experiments allow us to reason about the underlying mechanisms of simple-to-complex sensorimotor skill progression in human infants.

A Supervisory Sliding Mode Control Approach for Cooperative Robotic System of Systems

This paper deals with the formulation of a supervisory sliding mode (SM) control approach oriented to deal with the interesting class of system of systems of robotic nature. This class of systems is characterized by the fact of being inherently distributed, cooperative, and, possibly, heterogeneous. In this paper, we propose a modular and composable approach relying on basic modules featuring a multilevel functional architecture, including a supervisor and a couple of hybrid position/force control schemes associated with a couple of cooperative robotic manipulators. In principle, the overall robotic system we are referring to can be viewed as a collection of basic modules of that type. In this paper, we focus on the design of the basic module. The hybrid position/force control schemes therein included are based on position and force controllers. The proposed position and force controllers are of SM type, to assure suitable robustness to perform a satisfactory trajectory tracking even in presence of unavoidable modeling uncertainties and external disturbances. The verification and the validation of our proposal have been performed by simulating the supervisor and the hybrid control scheme applied to one of the two robotic manipulators while experimentally testing the position control on the other arm. The experimental part of the tests has been carried out on a COMAU SMART3-S2 anthropomorphic industrial robotic manipulator.

English

The Robot kinematics deals with the position of links which are connected with the help of joints, which gives the position and orientation of every link present in the robot manipulator to place the end-effector to the required target. A kinematic model of the anthropomorphic robot manipulator was developed using multibody dynamic software called MSC. Automated Dynamic Analysis of Mechanical Systems (ADAMS) which represents the human arm. The shoulder joint is considered as spherical joint and elbow joint as a revolute joint. Kinematic equations and Software model is developed to compare and verify the working of the robot manipulator. The Workspace of the robot manipulator was traced to show the maximum and minimum working area of the manipulator. The workspace of the robot manipulator is traced in MSC. ADAMS to show the working region of the manipulator.

Laser triangulation sensor and six axes anthropomorphic robot manipulator modelling for the measurement of complex geometry products

A laser triangulation sensor and six-axis manipulator mathematical modeling for the measurement of complex geometry parts will be outlined in the article. The integrated system model is showed covering the laser triangulation sensor, the geometry of the manipulator and the relative positions of the components during the measurement process. The method to calibrate the sensor and relate the positions of the sensor and the robot is explained in this work. The measurement process is carried out with the parameters calculated with this method and the nominal parameters of the robot. Finally, plane surfaces with holes and termination of tubes placed in different orientations will be measured; the accuracy of the integrated system (sensor and robot) will be presented as well.

정리정돈용 서비스 로봇 플랫폼의 구현 연구

In this paper, a Smart Home Service Robot, McBot II, which performs mess-cleanup function etc. in house, is designed much more optimally than other service robots. It is newly developed in much more practical system than McBot I which we had developed two years ago. One characteristic attribute of mobile platforms equipped with a set of dependent wheels is their omni- directionality and the ability to realize complex translational and rotational trajectories for agile navigation in door. An accurate coordination of steering angle and spinning rate of each wheel is necessary for a consistent motion. This paper develops trajectory controller of 3-wheels omni-directional mobile robot using fuzzy azimuth estimator. A specialized anthropomorphic robot manipulator which can be attached to the housemaid robot McBot II, is developed in this paper. This built-in type manipulator consists of both arms with 4 DOF (Degree of Freedom) each and both hands with 3 DOF each. The robotic arm is optimally designed to satisfy both the minimum mechanical size and the maximum workspace. Minimum mass and length are required for the built-in cooperated-arms system. But that makes the workspace so small. This paper proposes optimal design method to overcome the problem by using neck joint to move the arms horizontally forward/backward and waist joint to move them vertically up/down. The robotic hand, which has two fingers and a thumb, is also optimally designed in task-based concept. Finally, the good performance of the developed McBot II is confirmed through live tests of the mess-cleanup task.

홈 서비스 로봇을 위한 전용 머니퓰레이터의 구현에 관한 연구

Abstract A specialized anthropomorphic robot manipulator which can be attached to the housemaid robot McBot II, is developed In this paper. This built-in type manipulator consists of both arms with 3 DOF (Degree of Freedom) each and both hands with 3 DOF each. The robotic arm is optimally designed to satisfy both the minimum mechanical size and the maximum workspace. Minimum mass and length are required for the built-in cooperated-arms system. But that makes the workspace so small. This paper proposes optimal design method toovercome the problem by using neck joint to move the arms horizontally forward/backward and waist joint to move them vertically up/down. The robotic hand, which has two fingers and a thumb, is also optimally designed in task-based concept. Finally, the good performance of the developed manipulator is confirmed through live test of tasks. Key Words : Robotic Arm, Robotic Hand, Task-Based Design, Built-In Manipulator, Optimization 본 논문은 2011년도 교육과학기술부의 재원으로 한국연구재단 지역혁신인력양성사업의 지원을 받아 수행된 연구임 .

Autonomous robot manipulator-based exploration and mapping system for bridge maintenance

This paper presents a system for Autonomous eXploration to Build A Map (AXBAM) of an unknown, 3D complex steel bridge structure using a 6 degree-of-freedom anthropomorphic robot manipulator instrumented with a laser range scanner. The proposed algorithm considers the trade-off between the predicted environment information gain available from a sensing viewpoint and the manipulator joint angle changes required to position a sensor at that viewpoint, and then obtains collision-free paths through safe, previously explored regions. Information gathered from multiple viewpoints is fused to achieve a detailed 3D map. Experimental results show that the AXBAM system explores and builds quality maps of complex unknown regions in a consistent and timely manner.

System and software safety analysis for the ERA control computer

The European Robotic Arm (ERA) is a seven degrees of freedom relocatable anthropomorphic robotic manipulator system, to be used in manned space operation on the International Space Station, supporting the assembly and external servicing of the Russian segment. The safety design concept and implementation of the ERA is described, in particular with respect to the central computer's software design. A top–down analysis and specification process is used to down flow the safety aspects of the ERA system towards the subsystems, which are produced by a consortium of companies in many countries. The user requirements documents and the critical function list are the key documents in this process. Bottom–up analysis (FMECA) and test, on both subsystem and system level, are the basis for safety verification. A number of examples show the use of the approach and methods used.

A comparative study of neural network structures in identification of nonlinear systems

This paper investigates the identification of nonlinear systems by neural networks. As the identification methods, Feedforward Neural Networks (FNN), Radial Basis Function Neural Networks (RBFNN), Runge–Kutta Neural Networks (RKNN) and Adaptive Neuro Fuzzy Inference Systems (ANFIS) based identification mechanisms are studied and their performances are comparatively evaluated on a three degrees of freedom anthropomorphic robotic manipulator.

Sliding mode control of an electrically powered industrial robot

The paper considers the multivariable control of anthropomorphic robot manipulators, and its objective is to develop a control technique which exploits the full potential of the performance of robot arms. In its ideal form, the computed torque technique achieves the desired performance in nonlinear time-varying systems. However, its stability cannot be guaranteed, and its performance deteriorates rapidly with the pres ence of disturbances and parametric uncertainties. The variable structure control strategy which can guarantee stability, given bounded parametric uncertainty, is used as an additional input to rectify the uncertainties in the estimated control model of the computed torque technique. The benefits of the combined computed torque/variable structure controller are demonstrated practically on an electrically powered industrial robot, and the performance of the proposed controller is compared to that of the computed torque technique.