Path Planning for Robotic Arms Based on an Improved RRT Algorithm

The burgeoning robotics industry has catalyzed significant strides in the development and deployment of industrial and service robotic arms, positioning path planning as a pivotal facet for augmenting their operational safety and efficiency. Existing path planning algorithms, while capable of delineating feasible trajectories, often fall short of achieving optimality, particularly concerning path length, search duration, and success likelihood. This study introduces an enhanced Rapidly-Exploring Random Tree (RRT) algorithm, meticulously designed to rectify the issues of node redundancy and the compromised path quality endemic to conventional RRT approaches. Through the integration of an adaptive pruning mechanism and a dynamic elliptical search strategy within the Informed RRT* framework, our algorithm efficiently refines the search tree by discarding branches that surpass the cost of the optimal path, thereby refining the search space and significantly boosting efficiency. Extensive comparative analysis across both two-dimensional and three-dimensional simulation settings underscores the algorithm’s proficiency in markedly improving path precision and search velocity, signifying a breakthrough in the domain of robotic arm path planning.

Modeling and Adaptive Neural Network Control for a Soft Robotic Arm With Prescribed Motion Constraints

This paper presents a dynamic model and performance constraint control of a line-driven soft robotic arm.The dynamics model of the soft robotic arm is established by combining the screw theory and the Cosserat theory.The unmodeled dynamics of the system are considered,and an adaptive neural network controller is designed using the backstepping method and radial basis function neural network.The stability of the closed-loop system and the boundedness of the tracking error are verified using Lyapunov theory.The simulation results show that our approach is a good solution to the motion constraint problem of the line-driven soft robotic arm.

Motor Imagery and Error Related Potential Induced Position Control of a Robotic Arm

The paper introduces an electroencephalography(EEG) driven online position control scheme for a robot arm by utilizing motor imagery to activate and error related potential(ErrP) to stop the movement of the individual links, following a fixed(pre-defined) order of link selection. The right(left)hand motor imagery is used to turn a link clockwise(counterclockwise) and foot imagery is used to move a link forward. The occurrence of ErrP here indicates that the link under motion crosses the visually fixed target position, which usually is a plane/line/point depending on the desired transition of the link across 3D planes/around 2D lines/along 2D lines respectively. The imagined task about individual link's movement is decoded by a classifier into three possible class labels: clockwise, counterclockwise and no movement in case of rotational movements and forward, backward and no movement in case of translational movements. One additional classifier is required to detect the occurrence of the ErrP signal, elicited due to visually inspired positional link error with reference to a geometrically selected target position. Wavelet coefficients and adaptive autoregressive parameters are extracted as features for motor imagery and ErrP signals respectively. Support vector machine classifiers are used to decode motor imagination and ErrP with high classification accuracy above 80%. The average time taken by the proposed scheme to decode and execute control intentions for the complete movement of three links of a robot is approximately33 seconds. The steady-state error and peak overshoot of the proposed controller are experimentally obtained as 1.1% and4.6% respectively.

Kinect-Based Motion Recognition Tracking Robotic Arm Platform

The development of artificial intelligence technology has promoted the rapid improvement of human-computer interaction. This system uses the Kinect visual image sensor to identify human bone data and complete the recognition of the operator’s movements. Through the filtering process of real-time data by the host computer platform with computer software as the core, the algorithm is programmed to realize the conversion from data to control signals. The system transmits the signal to the lower computer platform with Arduino as the core through the transmission mode of the serial communication, thereby completing the control of the steering gear. In order to verify the feasibility of the theory, the team built a 4-DOF robotic arm control system and completed software development. It can display other functions such as the current bone angle and motion status in real time on the computer operation interface. The experimental data shows that the Kinect-based motion recognition method can effectively complete the tracking of the expected motion and complete the grasping and transfer of the specified objects, which has extremely high operability.

Monocular Visual-Inertial and Robotic-Arm Calibration in a Unifying Framework

Reliable and accurate calibration for camera,inertial measurement unit(IMU)and robot is a critical prerequisite for visual-inertial based robot pose estimation and surrounding environment perception.However,traditional calibrations suffer inaccuracy and inconsistency.To address these problems,this paper proposes a monocular visual-inertial and robotic-arm calibration in a unifying framework.In our method,the spatial relationship is geometrically correlated between the sensing units and robotic arm.The decoupled estimations on rotation and translation could reduce the coupled errors during the optimization.Additionally,the robotic calibration moving trajectory has been designed in a spiral pattern that enables full excitations on 6 DOF motions repeatably and consistently.The calibration has been evaluated on our developed platform.In the experiments,the calibration achieves the accuracy with rotation and translation RMSEs less than 0.7°and 0.01 m,respectively.The comparisons with state-of-the-art results prove our calibration consistency,accuracy and effectiveness.

A DoS Attacks Detection Aglorithm Based on Snort-BASE for Robotic Arm Control Systems

In response to the frequent safety accidents of industrial robots, this paper designs and implements a safety detection system for robot control. It can perform real-time security detection of robot operations on industrial production lines to improve the security and reliability of robot control systems. This paper designs and implements a robot control system based Snort-BASE for real-time online detection of DoS attacks. The system uses a six-degree-of-freedom robotic arm as an example, uses Snort to record the network communication data of the robot arm control system in real time, and filters the network traffic through self-defined rules, and then uses the BASE analysis platform to achieve security analysis of the network traffic. The solution verifies the effectiveness of online real-time detection of attacks and visualisation of attack records by designing simulated robotic arm and real robotic arm attack experiments respectively, thus achieving the security of network communication of the robot remote control system.

Modeling a Controller for an Articulated Robotic Arm

The precise control upon each degree of freedom of a robotic arm is a great challenge in implementing industrial work. This paper aims to design a novel controller for an automated robotic arm. A discrete Proportional Integral Derivative (PID) control technique is being used to replace the complex electronic circuitry, which would greatly reduce the cost and size of the controller. DC motors will be controlled on the basis of Closed-loop System using an avr (Atmega 16/32) microcontroller. Transfer functions have been derived for mathematical modeling of the system through which the stability of the system can be evaluated prior to fabrication.

基于ARM的婴幼儿催眠成长智能机器人设计

装置介绍了一种基于ARM的多种智能控制机械臂达到促进婴幼儿睡眠、心理健康成长和缓解母亲压力的效果,其结构由报警单元、烘干装置、机械臂仿真装置、摄像单元、温湿度及压力采集单元、光电传感器、婴儿床等组成。该装置由手机APP手动或自动的对六个舵机全方位的精准控制,使机械臂可以多姿态的运转并呈现仿真人手臂摇摆姿势的循环,机械臂外壳采用热感的慢回弹材料,高度仿真人手臂,为婴儿提供柔和真实的支撑。设计新颖、安全且性能强。

Research on the Robotic Arm Control System Based on the Reconfiguration Technology

In the development of the robotic arm in recent decades, it has been realized to operate the robotic arm according to the design of the software program to complete the corresponding specific work. It can not only work in an environment where people can't work, but also can maintain long hours of the work, with the low failure rate. With the rapid development of the robotic arm research technology, the robotic arm has been widely used in aerospace, medicine, industrial production, anti-terrorism, and explosion and service industries. Due to the strict quality restriction, the rigidity of the robotic arm is low, and there are geometric errors and flexible deformation errors. In order to improve its performances, it is necessary to reduce the end positioning error of the robotic arm by means of the restructuring technology.

Robotic Smart Prosthesis Arm with BCI and Kansei/Kawaii/Affective Engineering Approach.Pt I: Quantum Soft Computing Supremacy

A description of the design stage and results of the development of the conceptual structure of a robotic prosthesis arm is given.As a result,a prototype of man-made smart prosthesis on a 3D printer as well as a foundation for computational intelligence presented.The application of soft computing technology(the first step of IT)allows to extract knowledge directly from the physical signal of the electroencephalogram,as well as to form knowledge-based intelligent robust control of the lower performing level taking into account the assessment of the patient’s emotional state.The possibilities of applying quantum soft computing technologies(the second step of IT)in the processes of robust filtering of electroencephalogram signals for the formation of mental commands of robotic prosthetic arm discussed.Quantum supremacy benchmark of intelligent control simulation demonstrated.

基于ARM-STM32的智能消杀作业机器人平台及终端

本文介绍了一种基于STM32的智能消杀作业机器人,其核心部件包括智能无人车载底盘、消杀喷雾模块、红外体温监测模块、装载模块、无人机追踪模块和STM32单片机,可接收来自移动平台、专业控制设备等多种平台的控制指令。该机器人实现了对公共区域的远程监控及无人化自动作业,具有自主、智能、便捷、高效的优点,有效地解放了人力,提高了工作效率,降低了工作人员的感染风险,具有良好的使用前景。

Development and Proof-of-Concept Study of a Novel Intraoperative Surgical Planning Tool for Robotic Arm-Assisted Total Knee Arthroplasty

Background: Intraoperative surgical planning tools (ISPTs) used in current-generation robotic arm-assisted total knee arthroplasty (RTKA) systems (such as Navio® and MAKO®) involve employment of postoperative passive joint balancing. This results in improper ligament tension, which may negatively impact joint stability, which, in turn, may adversely affect patient function after TKA. Methods: A simulation-enhanced ISPT (SEISPT) that provides insights relating to postoperative active joint mechanics was developed. This involved four steps: 1) validation of a multi-body musculoskeletal model;2) optimization of the validated model;3) use of the validated and optimized model to derive knee performance equations (KPEs), which are equations that relate implant component characteristics to implant component biomechanical responses;and 4) optimization of the KPEs with respect to these responses. In a proof-of-concept study, KPEs that involved two com- ponent biomechanical responses that have been shown to strongly correlate with poor proprioception (a common patient complaint post-TKA) were used to calculate optimal positions and orientations of the femoral and tibial components in the TKA design implanted in one subject (as reported in a publicly-available dataset). Results: The differences between the calculated implant positions and orientations and the corresponding achieved values for the implant components in the subject were not similar to component position and orientation errors reported in biomechanical literature studies involving Navio® and MAKO®. Also, we indicate how SEISPT could be incorporated into the surgical workflow of Navio® with minimal disruption and increase in cost. Conclusion: SEISPT is a plausible alternative to current-gen- eration ISPTs.

Error Modeling and Sensitivity Analysis of a Parallel Robot with SCARA(Selective Compliance Assembly Robot Arm) Motions

Parallel robots with SCARA（selective compliance assembly robot arm） motions are utilized widely in the field of high speed pick-and-place manipulation. Error modeling for these robots generally simplifies the parallelogram structures included by the robots as a link. As the established error model fails to reflect the error feature of the parallelogram structures, the effect of accuracy design and kinematic calibration based on the error model come to be undermined. An error modeling methodology is proposed to establish an error model of parallel robots with parallelogram structures. The error model can embody the geometric errors of all joints, including the joints of parallelogram structures. Thus it can contain more exhaustively the factors that reduce the accuracy of the robot. Based on the error model and some sensitivity indices defined in the sense of statistics, sensitivity analysis is carried out. Accordingly, some atlases are depicted to express each geometric error’s influence on the moving platform’s pose errors. From these atlases, the geometric errors that have greater impact on the accuracy of the moving platform are identified, and some sensitive areas where the pose errors of the moving platform are extremely sensitive to the geometric errors are also figured out. By taking into account the error factors which are generally neglected in all existing modeling methods, the proposed modeling method can thoroughly disclose the process of error transmission and enhance the efficacy of accuracy design and calibration.

Applying Neural Network Architecture for Inverse Kinematics Problem in Robotics

One of the most important problems in robot kinematics and control is, finding the solution of Inverse Kinematics. Inverse kinematics computation has been one of the main problems in robotics research. As the Complexity of robot increases, obtaining the inverse kinematics is difficult and computationally expensive. Traditional methods such as geometric, iterative and algebraic are inadequate if the joint structure of the manipulator is more complex. As alternative approaches, neural networks and optimal search methods have been widely used for inverse kinematics modeling and control in robotics This paper proposes neural network architecture that consists of 6 sub-neural networks to solve the inverse kinematics problem for robotics manipulators with 2 or higher degrees of freedom. The neural networks utilized are multi-layered perceptron (MLP) with a back-propagation training algorithm. This approach will reduce the complexity of the algorithm and calculation (matrix inversion) faced when using the Inverse Geometric Models implementation (IGM) in robotics. The obtained results are presented and analyzed in order to prove the efficiency of the proposed approach.

A Hybrid Brain-Computer Interface for Closed-Loop Position Control of a Robot Arm

Brain-Computer interfacing(BCI)has currently added a new dimension in assistive robotics.Existing braincomputer interfaces designed for position control applications suffer from two fundamental limitations.First,most of the existing schemes employ open-loop control,and thus are unable to track positional errors,resulting in failures in taking necessary online corrective actions.There are examples of a few works dealing with closed-loop electroencephalography(EEG)-based position control.These existing closed-loop brain-induced position control schemes employ a fixed order link selection rule,which often creates a bottleneck preventing time-efficient control.Second,the existing brain-induced position controllers are designed to generate a position response like a traditional firstorder system,resulting in a large steady-state error.This paper overcomes the above two limitations by keeping provisions for steady-state visual evoked potential(SSVEP)induced linkselection in an arbitrary order as required for efficient control and generating a second-order response of the position-control system with gradually diminishing overshoots/undershoots to reduce steady-state errors.Other than the above,the third innovation is to utilize motor imagery and P300 signals to design the hybrid brain-computer interfacing system for the said application with gradually diminishing error-margin using speed reversal at the zero-crossings of positional errors.Experiments undertaken reveal that the steady-state error is reduced to 0.2%.The paper also provides a thorough analysis of the stability of the closed-loop system performance using the Root Locus technique.

Design and Development of a Competitive Low-Cost Robot Arm with Four Degrees of Freedom

The main focus of this work was to design, develop and implementation of competitively robot arm with en- hanced control and stumpy cost. The robot arm was designed with four degrees of freedom and talented to accomplish accurately simple tasks, such as light material handling, which will be integrated into a mobile platform that serves as an assistant for industrial workforce. The robot arm is equipped with several servo motors which do links between arms and perform arm movements. The servo motors include encoder so that no controller was implemented. To control the robot we used Labview, which performs inverse kinematic calculations and communicates the proper angles serially to a microcontroller that drives the servo motors with the capability of modifying position, speed and acceleration. Testing and validation of the robot arm was carried out and results shows that it work properly.

Dynamic Coordination of Uncalibrated Hand/Eye Robotic System Based on Neural Network

A nonlinear visual mapping model is presented to replace the image Jacobian relation for uncalibrated hand/eye coordination. A new visual tracking controller based on artificial neural network is designed. Simulation results show that this method can drive the static tracking error to zero quickly and keep good robustness and adaptability at the same time. In addition, the algorithm is very easy to be implemented with low computational complexity.

Resonance Suppression Strategy for Humanoid Robot Arm

To address the problem of resonance in the control of a robot arm,a resonance suppression strategy is proposed for a single-joint humanoid robot arm based on the proportionalresonant(PR)controller.First,an arm joint model of the humanoid robot is established.Then the influence of resonance frequency on the performance of the control system with the robot arm is analyzed.The voltage fluctuation of the drive motor caused by the changes in arm motion is recognized as the disturbance of the current loop.The PR controller has the characteristic of disturbance rejection at a specific frequency.The output fluctuation of the driving system caused by the change of arm motion state at the resonance frequency is suppressed.Therefore the output current of the inverter will not be affected by the vibration of the arm at the resonance frequency.Finally,the control system is verified by MATLAB/Simulink simulation.The simulation results demonstrate that the control strategy for the humanoid robot arm based on the PR controller can suppress the resonance of the arm effectively,improving the dynamic performance and system stability.