Path Planning of Multi-Axis Robotic Arm Based on Improved RRT*

An improved RRT∗algorithm,referred to as the AGP-RRT∗algorithm,is proposed to address the problems of poor directionality,long generated paths,and slow convergence speed in multi-axis robotic arm path planning.First,an adaptive biased probabilistic sampling strategy is adopted to dynamically adjust the target deviation threshold and optimize the selection of random sampling points and the direction of generating new nodes in order to reduce the search space and improve the search efficiency.Second,a gravitationally adjustable step size strategy is used to guide the search process and dynamically adjust the step-size to accelerate the search speed of the algorithm.Finally,the planning path is processed by pruning,removing redundant points and path smoothing fitting using cubic B-spline curves to improve the flexibility of the robotic arm.Through the six-axis robotic arm path planning simulation experiments on the MATLAB platform,the results show that the AGP-RRT∗algorithm reduces 87.34%in terms of the average running time and 40.39%in terms of the average path cost;Meanwhile,under two sets of complex environments A and B,the average running time of the AGP-RRT∗algorithm is shortened by 94.56%vs.95.37%,and the average path cost is reduced by 55.28%vs.47.82%,which proves the effectiveness of the AGP-RRT∗algorithm in improving the efficiency of multi-axis robotic arm path planning.

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.

基于ARM平台的校园垃圾分拣移动机器人设计与开发

随着社会对环境保护和垃圾分类的重视,校园环境中的垃圾管理变得日益重要。该文提出了一种ARM平台技术解决方案,并设计了一款智能移动机器人,该机器人能够实现校园内垃圾的识别、分拣和收集。其采用了视觉识别技术和机械臂操作技术,通过摄像头获取图像信息,利用深度学习算法对垃圾进行分类识别,并使用机械臂进行精准抓取和分拣。实验结果表明,该移动机器人在校园环境中具有良好的实用性和可行性,能够有效地解决校园垃圾管理难题,为校园环境的整洁和垃圾资源化利用提供了有效的技术支持。

基于UARM的流水线激光雕刻系统

激光加工是工业制造中的重要加工方式之一,为了解决传统的激光加工设备由于设备成本高和操作复杂而产生的问题,则迫切需要研发设计一种高效低成本的激光加工系统。本文提出了一种基于UARM机械臂的流水线激光雕刻系统,该系统由激光模块、UARM机械臂和流水平台组成,共同实现对各种材料的高精度、高效率雕刻。基于UARM机械臂的流水线激光雕刻系统由的硬件和软件两个部分组成,其中包括UARM的机械设计、激光模块选取以及控制系统的撰写。同时展示了实验结果,证明了系统在不同材料上实现高质量雕刻的有效性,能体现出系统在产品标识和流水线雕刻加工方面具有广泛的工业应用潜力。

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。

Clinical Study of Accelerated Rehabilitation Concept Combined with Tianji Robot-Assisted Surgery in Lumbar Degenerative Diseases

Objective: To compare the effectiveness and safety of two surgical methods for lumbar degenerative diseases;the combination of the concept of accelerated rehabilitation with the assistance of Tianji Robotics and the concept of accelerated rehabilitation combined with manual pedicle screw placement assisted by conventional C-arm fluoroscopy. Methods: A retrospective analysis was performed on 70 patients who received the concept of accelerated rehabilitation combined with spinal surgery for lumbar degenerative diseases in Baise People’s Hospital from January 2022 to January 2024. Among them, 35 patients in the robot group received accelerated rehabilitation concept combined with robot-assisted surgery;In the conventional C-arm group, 35 patients received the accelerated rehabilitation concept combined with manual pedicle screw placement assisted by conventional C-arm fluoroscopy. VAS score (preoperative/postoperative), ODI score (preoperative/postoperative), intraoperative bleeding volume, postoperative hospital stay, postoperative complications and the accuracy rate of screw placement were compared between the two groups. Result: There was no statistically significant difference in preoperative VAS scores between the robot group and the conventional C-arm group (6.45 ± 0.82 VS 6.63 ± 0.81, P = 0.6600). The postoperative VAS score of the robot group was better than that of the conventional C-arm group (1.69 ± 0.80 VS 2.45 ± 0.85, P = 0.0000*). There was no statistically significant difference in preoperative ODI scores between the robot group and the conventional C-arm group (32.11 ± 3.18 VS 31.66 ± 2.25, P = 0.4900). The postoperative ODI score of the robot group was better than that of the conventional C-arm group (22.68 ± 1.94 VS 24.57 ± 2.25, P = 0.0000*). The postoperative complications in the robot group were less than those in the conventional C-arm group (2.7778% VS 28.5724%, P = 0.0030*). The intraoperative bleeding in the robot group was lower than that in the conventional C-arm group (320.85 ± 276.28 VS 490.00 ± 395.34, P = 0.0420*). The postoperative hospital stay of the robot group was shorter than that of the conventional C-arm group (10.00 ± 9.32 VS 14.49 ± 7.55, P = 0.0300*). The screw placement inaccuracy score of the robot group was lower than that of the conventional C-arm group (0.17 ± 0.51 VS 1.45 ± 1.46, P = 0.0000*). Conclusion: The combination of the concept of accelerated rehabilitation and Tianji Orthopedic robot-assisted surgery is more effective and safer in posterior lumbar decompression and internal fixation surgery with a screw rod system, and is worthy of promotion and application.

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。

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.

ADAPTIVE FUZZY CONTROL FOR ROBOT ARM MANIPULATOR WITH 5-DOF

To control the robot and track the designed trajectory with uncertain disturbances in a specified precision range, an adaptive fuzzy control scheme for the robot arm manipulator is discussed. The controller output error method （COEM） is used to design the adaptive fuzzy controller. A few or all of the parameters of the controller are adjusted by using the gradient descent algorithm to minimize the output error. COEM is adopted in the adaptive control system for the robot arm manipulator with 5-DOF. Simulation results show the effectiveness of the method and the real time adjustment of the parameters.

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.

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.

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.

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.

Posture Maintenance Control of 2-Link Object By Nonprehensile Two-Cooperative-Arm Robot Without Compensating Friction

In this paper, a method to posture maintenance control of 2-link object by nonprehensile two-cooperative-arm robot without compensating friction is proposed. In details, a mathematical model of the 2-link object is firstly built. Based on the model, stable regions for holding motion of nonprehensile two-cooperative-arm robot are obtained while the 2-link object is kept stable on the robot arms with static friction. Among the obtained stable regions, the robust pairs of orientation angles of the 2-link object are found. Under the robust orientation angles, a feedback control system is designed to control the arms to maintain the 2-link object's posture while it is being held or lifted up. Finally, experimental results are shown to verify the effectiveness of the proposed method.

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.

Iterative sliding mode control strategy of robotic arm based on fractional calculus

In order to improve the control performance of industrial robotic arms,an efficient fractional-order iterative sliding mode control method is proposed by combining fractional calculus theory with iterative learning control and sliding mode control.In the design process of the controller,fractional approaching law and fractional sliding mode control theories are used to introduce fractional calculus into iterative sliding mode control,and Lyapunov theory is used to analyze the system stability.Then taking a two-joint robotic arm as an example,the proposed control strategy is verified by MATLAB simulation.The simulation experiments show that the fractional-order iterative sliding mode control strategy can effectively improve the tracking speed and tracking accuracy of the joint,reduce the tracking error,have strong robustness and effectively suppress the chattering phenomenon of sliding mode control.

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.

System design of a dexterous lightweight robot arm with remote control

This paper presents a novel remote controlled dexterous robot arm with 6 degrees of freedom （DOF）. As a highly integrated mechatronics system, sensors and their signal processing system are integrated inside each joint. To lighten the weight, almost all mechanical parts are made of aluminum and the robot control system is placed outside. The modular concept is adopted during the robot design process for time and cost saving. Considering the much greater torque acted on the two shoulder joints, the joint shells are strengthened in the design to increase joint stiffness and suppress system vibration. Meanwhile, to simplify the maintenance, a new spring pins electronic connector is designed to disassemble every joint, connector and link independently without cutting any cables. The teleoperation technology enables the robot to offer more convenient service definitely for people＇ s daily life. Virtual reality technology is used to solve the time delay problem during teleoperation. Finally, two typical daily chore experiments are implemented to prove the manipulation ability of the dexterous robot arm.