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.

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.

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.

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。

Dynamical Analysis of a Novel Spherical Robot with Retractable Arms

A novel structure of a spherical robot with retractable arms was presented in order to fulfill the requirements of omni-direction movement and operation mission. Under the assumption of rolling without slipping, nonholonomic constraints were revealed and a dynamics model of the proposed robot was constructed by use of Kane＇s method. Numerical simulations about rectilinear motion and sigmoid curve motion of the system were carried out in Matlab, and as a comparison, the same trajectories were also implemented by a virtual prototype in ADAMS, which validate the derived dynamical model accordingly. With the derived dynamical model, torques/forces of joints were analyzed. The results indicate the disturbance forces or torques on each joint are not zero under the state of sphere moving, and with rational planning for the trajectory of the robot, there will be a great decrease of the disturbance forces or torques acting on the spherical caps and arms.

基于ARMS3C4510B的微机保护装置的设计

文章介绍了一种基于32位ARM芯片S3C4510B处理器的继电保护装置的设计。该装置采用双CPU结构,其中TMS320F2812承担计算任务,S3C4510B承担控制以及与外围电路的通信任务。由于采用了高性能的硬件平台,所设计的装置具有功能完善、运行可靠、可扩充性好的特点,可适用于110 kV及以下的系统。

混合现实场景下结合SSVEP与眼动追踪的脑控机械臂系统

针对混合现实场景下脑控机械臂系统交互性差、指令集小的问题,设计了一种结合稳态视觉诱发电位(SSVEP)和眼动追踪技术的混合现实脑控机械臂系统。该系统通过眼动追踪技术实现目标区域的初选,而SSVEP信号则被用于在初选区域内识别最终的目标指令。在不增加刺激类别数量的前提下扩大了指令集,并根据受试者的视线停留区域实现异步控制。离线实验结果表明,在使用相同刺激类别数量的情况下,增加视觉刺激目标数量不会对分类准确率产生显著影响。通过在线实验验证了系统的适用性,相较于使用单一SSVEP范式的机械臂控制系统,所提出的系统具有更好的交互性和更大的指令集。

基于ARMS3C44BOX的电力系统微机保护装置应用设计

介绍ARM芯片S3C44BOX设计微机保护装置的优点,以及32位ARM处理器实现的微机保护装置的设计,包括硬件系统构建、微机保护算法以及微机保护程序。硬件主要有CPU主系统、模拟量输入电路设计、开关量输入输出回路的设计、人机对话接口设计、通信接口的设计以及电源设计;保护算法采用傅氏算法;软件设计包括微机保护主程序、采样中断服务程序和故障处理程序等。

基于改进PSO-GSA算法的时间最优轨迹规划

传统3-5-3多项式插值轨迹规划算法速度和加速度规划过于保守,与机械臂运动极限条件相差较远,没有充分发挥其运动性能,从而导致机械臂完成任务的时间增长。针对上述问题,本文提出一种基于改进PSO-GSA算法的3-5-3多项式插值轨迹规划算法。首先引入自适应惯性权重与动态学习因子对PSO-GSA算法进行改进,然后使用改进算法对3-5-3多项式插值算法进行时间优化。在优化过程中,关节速度超速时的粒子组使用了与不超速时不同的适应度函数,引导粒子组朝关节速度减小的方向靠拢,加快了算法收敛速度。仿真结果表明,改进的PSO-GSA算法相比原算法及一些同类算法收敛速度更快、搜索精度更高、不易陷入局部最优。对3-5-3多项式插值轨迹规划法进行时间优化后相比优化前运行时间缩短了22.9%,得到的轨迹满足速度限制且平滑稳定,运行更加安全高效。

基于SCARA的柔性葡萄采摘机械臂设计与试验

针对现今葡萄种植环境狭小且复杂、常见关节型机械臂空间运动灵活度较小和避障能力较弱等问题,为了适应葡萄植株下层枝叶较少、障碍物较少,上层枝叶较多、障碍物较多的特点,设计并开发一款基于SCARA的柔性葡萄采摘机械臂,其底部为SCARA机械臂,上部为柔性机械臂部分;同时,设计了SCARA与柔性机械臂的协调运动控制系统。通过分析机械臂的机械结构和工作原理确定机械臂的末端空间位置和机械臂的运动学关系,利用MatLab绘制其工作空间,并绘制柔性机械臂在随机两个空间点运动的轨迹、速度、加速度图,从而实现SCARA机械臂与柔性机械臂的运动控制。

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.

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.

Dynamics and adaptive control of a dual-arm space robot with closed-loop constraints and uncertain inertial parameters

A dynamics-based adaptive control approach is proposed for a planar dual-arm space robot in the presence of closed-loop constraints and uncertain inertial parameters of the payload. The controller is capable of controlling the po- sition and attitude of both the satellite base and the payload grasped by the manipulator end effectors. The equations of motion in reduced-order form for the constrained system are derived by incorporating the constraint equations in terms of accelerations into Kane＇s equations of the unconstrained system. Model analysis shows that the resulting equations perfectly meet the requirement of adaptive controller design. Consequently, by using an indirect approach, an adaptive control scheme is proposed to accomplish position/attitude trajectory tracking control with the uncertain parameters be- ing estimated on-line. The actuator redundancy due to the closed-loop constraints is utilized to minimize a weighted norm of the joint torques. Global asymptotic stability is proven by using Lyapunov＇s method, and simulation results are also presented to demonstrate the effectiveness 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.

NURBS体参数化模型的无支撑打印算法

处理大角度悬垂结构的打印问题是无支撑打印的主要挑战。针对NURBS(non-uniform rational B-spline)体参数化模型的多自由度无支撑三维打印技术,提出一种锥形切片算法。通过对模型的三维文件进行几何映射,利用水平切片算法对其进行切片,得到模型变形后的连续打印路径G-code;然后对得到的G-code进行逆映射,生成适用于六轴机械臂的多自由度连续打印路径。利用锥形切片算法规划打印路径,可以在无须支撑的情况下,依靠自支撑结构实现对悬空结构部分的打印。最后,通过仿真实验和对比实验,验证了锥形切片算法的准确性、可行性和有效性。锥形切片算法的引入为NURBS体参数化模型的无支撑三维打印提供了一种更加高效、经济和环保的解决方案。

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.

AGS-RRT算法机械臂避障路径规划研究

为了解决障碍物环境下机械臂路径规划效率提升问题,这里提出了一种AGS-RRT(快速扩展随机树)算法。使用基于圆柱和球面包围盒碰撞检测模型建立机械臂和障碍物之间的碰撞检测机制,得到即时的机械手与障碍物之间最短距离。此外,这里优化了RRT算法的抽样过程,采用改进高斯约束抽样替代随机抽样,使得在采样过程中更接近障碍物之间的通道。该方法可以加速前向搜索速度。在节点扩展方面,采用了人工势场结合自适应步长的新节点约束策略。该方法充分利用了环境和节点信息,快速扩展到目标区域,减少了探索和碰撞区域的过度扩展。针对复杂、多障碍和动态环境下的路径规划平滑问题,这里采用了基于B-Spline曲线的平滑函数,利用笛卡尔空间生成树对局部路径进行重规划,修剪优化后的路径更加平滑。通过仿真实验,验证了AGS-RRT算法的有效性,证明了该方法在提高规划效率的同时具备良好的避障性能。

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.