Actual Robot Arm Research Articles

AdaptiX - A Transitional XR Framework for Development and Evaluation of Shared Control Applications in Assistive Robotics

With the ongoing efforts to empower people with mobility impairments and the increase in technological acceptance by the general public, assistive technologies, such as collaborative robotic arms, are gaining popularity. Yet, their widespread success is limited by usability issues, specifically the disparity between user input and software control along the autonomy continuum. To address this, shared control concepts provide opportunities to combine the targeted increase of user autonomy with a certain level of computer assistance. This paper presents the free and open-source AdaptiX XR framework for developing and evaluating shared control applications in a high-resolution simulation environment. The initial framework consists of a simulated robotic arm with an example scenario in Virtual Reality (VR), multiple standard control interfaces, and a specialized recording/replay system. AdaptiX can easily be extended for specific research needs, allowing Human-Robot Interaction (HRI) researchers to rapidly design and test novel interaction methods, intervention strategies, and multi-modal feedback techniques, without requiring an actual physical robotic arm during the early phases of ideation, prototyping, and evaluation. Also, a Robot Operating System (ROS) integration enables the controlling of a real robotic arm in a PhysicalTwin approach without any simulation-reality gap. Here, we review the capabilities and limitations of AdaptiX in detail and present three bodies of research based on the framework. AdaptiX can be accessed at https://adaptix.robot-research.de.

A Secure and Efficient Multi-Object Grasping Detection Approach for Robotic Arms

Robot grasping is one of the most important abilities of modern intelligent robots, especially industrial robots. However, most of the existing robot arm’s grasp detection work is highly dependent on their edge computing ability, and the safety problems in the process of grasp detection are not considered enough. In this paper, we propose a new robotic arm grasping detection model with an edge-cloud collaboration method. With the scheme of multi-object multi-grasp, our model improves the mission success ratio of grasping. The model can not only complete the compression of full-resolution images but also achieve image compression at a limited bit rate. The image compression ratio reaches 2.03%; the structural difference value is higher than 0.91, and our average detection speed reaches 13.62 fps. Furthermore, we have packaged our model as a functional package of the ROS operating system, which can be easily used in actual robotic arm operations. Our solution can be fully applied to other work of robots to promote the development of the field of robotics.

Cable-path optimization method for industrial robot arms

The production line engineer's task of designing the external path for cables feeding electricity, air, and other resources to robot arms is a labor-intensive one. As the motions of robot arms are complex, the manual task of designing their cable path is a time-consuming and continuous trial-and-error process. Herein, we propose an automatic optimization method for planning the cable paths for industrial robot arms. The proposed method applies current physics simulation techniques for reducing the person–hours involved in cable path design. Our method yields an optimal parameter vector (PV) that specifies the cable length and cable-guide configuration via filtering the candidate PV set through a cable-geometry simulation based on the mass–spring model. The proposed method offers two key features: 1) Increased computational efficiency via an optimization procedure that separates the entire cable into the cable segments. In the proposed method, the entire cable is segmented at the positions of the cable guides into several separate cable segments, and the PVs of the cable segments that satisfy the constraints of collision, stretch, and curvature radius are filtered into the local optimal PV set. The global optimal PV is obtained by finding the combination of the local optimal PVs which have the same guide configuration between the adjacent cable segments and have minimal total length of the adjacent cable segments. 2) Robustness to external disturbances, such as fluctuation in the physical properties of the cables and the accuracy of manually attaching the cables. The PVs of the local optimal PV sets are required to satisfy the above constraints, even if the cable length changes in the predefined range, which ensures the robustness of the obtained cable path. To verify the validity of the proposed method, we obtain the global optimal PVs by applying the method to several pick-and-place motions of a six-axis vertical articulated robot arm in our simulations and implement the cable paths on an actual robot arm based on the obtained PVs. Our results indicate that the proposed method can aid line engineers to efficiently design the cable paths along robot arms.

DESIGN OF A 3D-PRINTED THREE-CLAW ROBOTIC GRIPPER END-EFFECTOR

The development of a novel 3D-printed three-claw robotic gripper shall be described in this paper with the goal of incorporating various design considerations. Such considerations include the grip reliability and stability, grip force maximization, wide object grasping capability. Modularization of its components is another consideration that allows its parts to be easily machined and reusable. The design was realized by 3D printing using a combination of tough polylactic acid (PLA) material and thermoplastic polyurethane (TPU) material. In practice, additional tolerances were also considered for 3D printing of materials to compensate for possible expansion or shrinkage of the materials used to achieve the required functionality. The aim of the study is to explore the design and eventually deploy the three-claw robotic gripper to an actual robotic arm once its metal work fabrication is finished.

Study on operational space control of a redundant robot with un-actuated joints: experiments under actuation failure scenarios

This paper analyzes operational space dynamics for redundant robots with un-actuated joints and reveals their highly nonlinear dynamic impacts on operational space control (OSC) tasks. Unlike conventional OSC approaches that partly address the under-actuated system by introducing rigid grasping or contact constraints, we deal with the problem even without such physical constraints which have been overlooked, yet it includes a wide range of applications such as free-floating robots and manipulators with passive joints or unwanted actuation failure. In addition, as an intuitive application example of the drawn result, an OSC is formulated as an optimization problem to alleviate the dynamics disturbance stemmed from the un-actuated joints and to satisfy other inequality constraints. The dynamic analysis and the proposed control method are verified by a number of numerical simulations as well as physical experiments with a 7-degrees-of-freedom robotic arm. In particular, we consider joint actuation failure scenarios that can be occurred at certain joints of a torque-controlled robot and practical case studies are performed with an actual redundant robot arm.

Learning, Improving, and Generalizing Motor Skills for the Peg-in-Hole Tasks Based on Imitation Learning and Self-Learning

We propose a framework based on imitation learning and self-learning to enable robots to learn, improve, and generalize motor skills. The peg-in-hole task is important in manufacturing assembly work. Two motor skills for the peg-in-hole task are targeted: “hole search” and “peg insertion”. The robots learn initial motor skills from human demonstrations and then improve and/or generalize them through reinforcement learning (RL). An initial motor skill is represented as a concatenation of the parameters of a hidden Markov model (HMM) and a dynamic movement primitive (DMP) to classify input signals and generate motion trajectories. Reactions are classified as familiar or unfamiliar (i.e., modeled or not modeled), and initial motor skills are improved to solve familiar reactions and generalized to solve unfamiliar reactions. The proposed framework includes processes, algorithms, and reward functions that can be used for various motor skill types. To evaluate our framework, the motor skills were performed using an actual robotic arm and two reward functions for RL. To verify the learning and improving/generalizing processes, we successfully applied our framework to different shapes of pegs and holes. Moreover, the execution time steps and path optimization of RL were evaluated experimentally.

Relationship Between the Order for Motor Skill Transfer and Motion Complexity in Reinforcement Learning

We propose a method to generate an order for learning and transferring motor skills based on motion complexity, then evaluate the order to learn motor skills of a task and transfer them to another task as a form of reinforcement learning (RL). Here, motion complexity refers to the complexity calculated from multiple motion trajectories of a task. To do this, multiple human demonstrations are extracted and clustered to calculate motion complexity and identify the motor skills involved in a task. The motion trajectories of the task are then used to calculate the motion complexity considering temporal entropy and spatial entropy. Finally, both orders [Simple-to-Complex] and [Complex-to-Simple] are generated to learn and transfer motor skills based on the motion complexities of multiple tasks. To evaluate these orders, two tasks [Drawing] and [Fitting] are performed using an actual robotic arm. To verify the learning and transfer processes, we apply our method to three different figures as well as to pegs and holes of three different shapes and analyze the experimental results. In addition, we provide guidelines for using the [Simple-to-Complex] and [Complex-to-Simple] orders in RL.

Sustainable and flexible industrial human machine interfaces to support adaptable applications in the Industry 4.0 paradigm

The Industry 4.0 paradigm introduced several technologies in the industrial manufacturing landscape, highlighting the Cyber Physical Systems, Internet of Things and Visual Computing as some of the key enabling technologies. The fast paced evolution of the technology has to be reflected in the common ground between the machines and the operators. The introduction of advanced, flexible and adaptable Human Machine Interfaces (HMI) will empower the operators with information and custom tools to be more productive in a safer way. This work presents totally functional hardware and a novel software architecture to build flexible advanced HMI interfaces that will provide adaptable and useful information to the operator of the machines. Industrial protocols are used to receive real-time data. A USB camera can be plugged to enable the utilisation of Computer Vision algorithms. A touch display shows adaptable interfaces composed of (a) 3D interactive graphics, (b) texts and 2D images, (c) the camera feed and (d) user interface controls to enable the interaction with the operators. The results present three use cases: (a) an HMI customised for 3D printers, (b) real-time motor control from the HMI and (c) a digital twin of a robotic arm receiving real-time data from the actual robotic arm. This work present data regarding consumption of the hardware for each use case under different work-load.

Fuzzy Rule Formulation Algorithm for Fuzzy Logic-Based 6-DOF Robot Arm Controller

A fuzzy logic-based controller with fuzzy rule formulation algorithm on software and actual control for a 6-DOF robotic arm was implemented. A robotic arm with 4-DOF attached a 2-DOF gripper serves as the testing platform. The actual robotic arm was characterized and the parameters are used for the simulator to mimic actual response. The fuzzy logic controller is then implemented to the simulated robotic arm and was then implemented to the actual robotic arm to control its movement. The new features are as follows: (1) Implementation of simulated weight and frictional effects of dynamic robot arm movement, (2) formulation and justification of reduced fuzzy rules for control and (3) addition of a path-partitioning algorithm to further enhance the dynamic movement of the robotic arm.

A novel Morse code-inspired method for multiclass motor imagery brain–computer interface (BCI) design

Motor imagery (MI)-based brain–computer interfaces (BCIs) allow disabled individuals to control external devices voluntarily, helping us to restore lost motor functions. However, the number of control commands available in MI-based BCIs remains limited, limiting the usability of BCI systems in control applications involving multiple degrees of freedom (DOF), such as control of a robot arm. To address this problem, we developed a novel Morse code-inspired method for MI-based BCI design to increase the number of output commands. Using this method, brain activities are modulated by sequences of MI (sMI) tasks, which are constructed by alternately imagining movements of the left or right hand or no motion. The codes of the sMI task was detected from EEG signals and mapped to special commands. According to permutation theory, an sMI task with N-length allows 2×(2N−1) possible commands with the left and right MI tasks under self-paced conditions. To verify its feasibility, the new method was used to construct a six-class BCI system to control the arm of a humanoid robot. Four subjects participated in our experiment and the averaged accuracy of the six-class sMI tasks was 89.4%. The Cohen's kappa coefficient and the throughput of our BCI paradigm are 0.88±0.060 and 23.5bits per minute (bpm), respectively. Furthermore, all of the subjects could operate an actual three-joint robot arm to grasp an object in around 49.1s using our approach. These promising results suggest that the Morse code-inspired method could be used in the design of BCIs for multi-DOF control.

Adaptive LSH based on the particle swarm method with the attractor selection model for fast approximation of Gaussian process regression

Gaussian process regression (GPR) is one of the non-parametric methods and has been studied in many fields to construct a prediction model for highly non-linear system. It has been difficult to apply it to a real-time task due to its high computational cost but recent high-performance computers and computationally efficient algorithms make it possible. In our previous work, we derived a fast approximation method for GPR using a locality-sensitive hashing (LSH) and product of experts model, but its performance depends on the parameters of the hash functions used in LSH. Hash functions are usually determined randomly. In this research, we propose an optimization method for the parameters of hash functions by referring to a swarm optimization method. The experimental results show that accurate force estimation of an actual robotic arm is achieved with high computational efficiency.

Solution for Ill-Posed Inverse Kinematics of Robot Arm by Network Inversion

In the context of controlling a robot arm with multiple joints, the method of estimating the joint angles from the given end-effector coordinates is called inverse kinematics, which is a type of inverse problems. Network inversion has been proposed as a method for solving inverse problems by using a multilayer neural network. In this paper, network inversion is introduced as a method to solve the inverse kinematics problem of a robot arm with multiple joints, where the joint angles are estimated from the given end-effector coordinates. In general, inverse problems are affected by ill-posedness, which implies that the existence, uniqueness, and stability of their solutions are not guaranteed. In this paper, we show the effectiveness of applying network inversion with regularization, by which ill-posedness can be reduced, to the ill-posed inverse kinematics of an actual robot arm with multiple joints.

1A1-B03 口腔内の動作を用いたロボットアーム制御用インタフェースの開発

Recently, some power assistance systems are developed to help human daily life. Such systems directly reinforce man's physical strength mainly to assist elder people. Unlike such attitude, we suggest different concept to expand man's physical body. More specifically, we are considering third arm. If we obtain the third arm and control it smoothly, new motions can be generated and impossible tasks get possible. In this paper, we developed interface to control a robot arm as our third arm. This interface is a controller device input by human mouse motion, because mouse is a body part having high degree of freedom next to hands. In the experiment, we controlled the actual robot arm using our developed interface. Finally it was confirmed that our proposed interface works effectively.

1A1-H23 導電性糸の編み込みによる伸縮可能な柔軟ニットセンサ外装の試作と評価(触覚と力覚)

Soft stretchable sensor exterior has possibility to enable robots to conduct natural and safe behaviors using whole-body tactile sensing. Although some soft stretchable sensor using electro-conductive strings have been studied so far, knitted structures used in their studies are simple and limited one. Therefore, we tested more stretchable and sensitive sensor structure using electro-conductive yarn. Furthermore, band-type stretchable sensor exterior is prototyped and evaluated by putting it on an actual robot's arm.

2P2-G06 マルチエージェントシステム構築用ツールキットを用いた分散制御手法シミュレータの開発(進化・学習とロボティクス)

This study deals with development of simulator for the distributed robot control system. In the distributed robot control system, active parts of a robot arm such as motors and links are modeled as software components which make performance according to states of other components based on exchanging messages. This paper reports the development of the simulator for evaluating the distributed robot control system and estimating possibilities for actual robot arm control system, and case studies of redundant robot arm applications.

1A1-K02 インピーダンス制御を用いたトルクフリーロボットアームに関する研究(医療・福祉ロボティクス・メカトロニクス)

This paper gives an outline of the torque-free robot arm using impedance control. A mechanism of this robot arm is noninterference constant inertia arm of two D.O.F. Impedance control is applied to the control system of the robot arm. Because of torque-free, and operator can move the robot arm by more less force. The robot arm can be applied to a surgical robot. For example, a surgeon can move the robot arm holding and endoscope by his hand when an minimally invasive surgery is performed. We will simulate the control system and experiment on actual robot arm.

FORCEFREE CONTROL WITH INDEPENDENT COMPENSATION FOR INDUSTRIAL ARTICULATED ROBOT ARM

In this paper, the concept of forcefree control is extended to realize flexible motion of industrial articulated robot arms under the virtual operational circumstances such as free of inertia, friction and gravity through independent compensation of inertia, friction and gravity. The forcefree control with independent compensation was verified with an experimental study of an actual industrial robot arm.

High Speed and High Accuracy Control of Industrial Articulated Robot Arms with Jerk Restraint by Spline Interpolated Taught Data

In industrial robot arms, high speed and high accurate operation is required. However in case of high speed operation, it often arises high jerk, i, e., rapid change of acceleration. Jerk causes deterioration of control performance such as vibration of a tip of a robot arm. It is, therefore, important to reduce jerk during robot arm operation. In this research, spline interpolation is used to reduce jerk under torque and speed constraints. Effectiveness of the proposed method was assured by experimental results and simulation results of an actual robot arm.

産業用多関節ロボットアームに対するカセンサレスのフォースフリー制御

This paper realized forcefree control of industrial articulated robot arm without external addition of force sensors. The forcefree control enables a motion of the industrial articulated robot arm as if it were in circumstance of non-gravity and non-friction condition. External force is detected by a torque monitor which is usually used in the servo controller of the industrial robot arm. The effectiveness of the proposed force sensor-less forcefree control was assured by simulation and experimental results of an actual industrial robot arm.

Assistant force compensation for hand movement of patients by using exogenous signals and/or neurophysiological signals

Because functional diseases of the brain can cause disabilities related to human movement control, a compensation method was developed for improving the performance of hand movements. The compensation for human hand movements can be carried out by adding an assistant force that is generated from artificial equipment attached to a human arm. From the experiment on visual target tracking, it was found that the tracking trajectory was adequately represented by a dynamic model of the motion of an articulated industrial robot arm, and the different abilities for movement control among healthy people and patients were classified by different model parameters as position loop gain, velocity loop gain, and response delay. Dynamic force compensation was approached by considering the different control features of the patients. The effectiveness of the proposed compensation method was verified in a simulation study on an actual industrial robot arm. A human-machine interface, e.g., a brain-computer interface (BCI), for realizing the control of artificial equipment to compensate for human hand movements is also presented and discussed.