Electromagnetic flowmeters face significant challenges in measuring complex fluids, characterized by weak flow signals and severe noise interference. Conventional solutions, such as dual-frequency rectangular wave excitation, suffer from multiple drawbacks including rich harmonic components, high electromagnetic noise during switching transitions, a propensity for resonance which shortens stabilization time, reduced sampling windows, and complex circuit implementation. Similarly, traditional single-frequency excitation struggles to balance zero stability with the suppression of slurry noise. To address these limitations, this paper proposes a novel converter design based on dual-frequency sinusoidal wave excitation. A pure hardware circuit is used to generate the composite excitation signal, which superimposes low-frequency and high-frequency components. This approach eliminates the need for a master control chip in signal generation, thereby reducing both circuit complexity and computational resource allocation. The signal processing chain employs a technique of "high-order Butterworth separation filtering combined with synchronous demodulation," effectively suppressing power frequency, orthogonal, and in-phase interference, achieving an improvement in interference rejection by approximately three orders of magnitude (1000x). Experimental results show that the proposed converter featured simplified circuitry, achieved a measurement accuracy of class 0.5, and validated the overall feasibility of the scheme.

In addressing the optimal motion planning issue for multi-arm rock drilling robots, this paper introduces a high-precision motion planning method based on Multi-Strategy Sampling RRT* (MSS-RRT*). A dual Jacobi iterative inverse solution method, coupled with a forward kinematics error compensation model, is introduced to dynamically correct target positions, improving end-effector positioning accuracy. A multi-strategy sampling mechanism is constructed by integrating DRL position sphere sampling, spatial random sampling, and goal-oriented sampling. This mechanism flexibly applies three sampling methods at different stages of path planning, significantly improving the adaptability and search efficiency of the RRT* algorithm. In particular, DRL position sphere sampling is prioritized during the initial phase, effectively reducing the number of invalid sampling points. For training a three-arm DRL model with the twin delayed deep deterministic policy gradient algorithm (TD3), the Hindsight Experience Replay-Obstacle Arm Transfer (HER-OAT) method is used for data replay. The cylindrical bounding box method effectively prevents collisions between arms. The experimental results show that the proposed method improves motion planning accuracy by 94.15% compared to a single Jacobi iteration. MSS-RRT* can plan a superior path in a shorter duration, with the planning time under optimal path conditions being only 20.71% of that required by Informed-RRT*, and with the path length reduced by 21.58% compared to Quick-RRT* under the same time constraints.

In order to solve the problem of insufficient end positioning accuracy due to factors such as gravity and material strength during the inverse solution process of a large hydraulic robotic arm, this paper proposes an inverse solution algorithm based on an adaptive spider wasp optimization (ASWO) optimized back propagation (BP) neural network. Firstly, the adaptability of the SWO algorithm is enhanced by analyzing the phase change in population fitness and dynamically adjusting the trade-off rate, crossover rate, and population size in real time. Then, the ASWO algorithm is used to optimize the initial weights and biases of the BP neural network, effectively addressing the problem of the BP neural network falling into local optima. Finally, a neural network mapping relationship between the actual position of the robotic arm’s end-effector and the corresponding joint values is established to reduce the influence of forward kinematic errors on the accuracy of the inverse solution. Experimental results show that the average positioning error of the robotic arm in the XYZ direction is reduced from (91.3, 87.38, 117.31) mm to (18.16, 24.67, 27.21) mm, significantly improving positioning accuracy by 80.11%, 71.78%, and 76.81%, meeting project requirements.

In order to solve the problem of the low end positioning accuracy of large hydraulic rock drilling robotic arms due to machining error and the working environment, this paper proposes an end positioning error compensation method based on an Improved Secretary Bird Optimization Algorithm (ISBOA) optimized Back Propagation (BP) neural network. Firstly, the good point set strategy is used to initialize the secretary bird population position to make the initial population distribution more uniform and accelerate the convergence speed of the algorithm. Then, the ISBOA is used to optimize the initial weights and biases of the BP neural network, which effectively overcomes the defect of the BP neural network falling into a local optimum. Finally, by establishing the mapping relationship between the joint value of the robot arm and the end positioning error, the error compensation is realized to improve the positioning accuracy of the rock drilling robot arm. The experimental results show that the average positioning error of the rock drilling robotic arm is reduced from 187.972 mm to 28.317 mm, and the positioning accuracy is improved by 84.94%, which meets the engineering requirements.

To address the issue of suboptimal spectral purity in Direct Digital Frequency Synthesis (DDFS) within resource-constrained environments, this paper proposes an optimized DDFS technique based on cubic Hermite interpolation. Initially, a DDFS hardware architecture is implemented on a Field-Programmable Gate Array (FPGA); subsequently, essential interpolation parameters are extracted by combining the derivative relations of sine and cosine functions with a dual-port Read-Only Memory (ROM) structure using the cubic Hermite interpolation method to reconstruct high-fidelity target waveforms. This approach effectively mitigates spurious issues caused by amplitude quantization during the DDFS digitalization process while reducing data node storage units. Moreover, this paper introduces single-quadrant ROM compression technology to further diminish the required storage space. Experimental results indicate that, compared to traditional DDFS methods, the optimization scheme proposed in this work achieves a ROM resource compression ratio of 1792:1 and a 14-bit output Spurious-Free Dynamic Range (SFDR) of −88.134 dBc, effectively enhancing amplitude quantization precision and significantly lowering spurious levels. This significantly improves amplitude quantization precision and reduces spurious levels. The proposed scheme demonstrates notable advantages in both spectral performance and resource utilization efficiency, making it highly suitable for resource-constrained embedded systems and high-performance applications such as radar and communication systems.

To address the local minimum issue commonly encountered in active collision avoidance using artificial potential field (APF), this paper presents a novel algorithm that integrates APF with deep reinforcement learning (DRL) for robotic arms. Firstly, to improve the training efficiency of DRL for the collision avoidance problem, Hindsight Experience Replay (HER) was enhanced by adjusting the positions of obstacles, resulting in Hindsight Experience Replay for Collision Avoidance (HER-CA). Subsequently, A robotic arm collision avoidance action network model was trained based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) and HER-CA methods. Further, a full-body collision avoidance potential field model of the robotic arm was established based on the artificial potential field. Lastly, the trained action network model was used to guide APF in real-time collision avoidance planning. Comparative experiments between HER and HER-CA were conducted. The model trained with HER-CA improves the average success rate of the collision avoidance task by about 10% compared to the model trained with HER. And a collision avoidance simulation was conducted on the rock drilling robotic arm, confirming the effectiveness of the guided APF method.

为了保证凿岩台车在资源受限情况下状态监测数据的安全快速加密,本文提出了一种基于改进AES的凿岩台车状态监测数据加密算法.通过优化AES算法的加解密轮函数结构,采用有限域GF(28)上最简形式的正逆列混淆矩阵,减小解密轮函数的运算复杂度,提高了算法的解密速度;提出一种新的表定义改进方法,通过定义xtime运算表和状态矩阵中各列元素共有的运算变量,将加解密轮函数中繁多复杂的乘法运算转化为简单的查表操作,解决了AES算法软件实现中存在的存储空间占用大以及解密运算复杂的问题.实验结果表明:相较于已知的最优实现方法,本方法的存储空间占用减少了40％,加解密速度分别提高了6.28％和6.61％,具有较高的执行效率.

为了有效提高激光调阻机的调阻精度和调阻效率,本文设计了一种激光调阻机测控系统.通过检测调阻过程中阻值的变化,有效控制激光器动作,实现对目标电阻的刻蚀.采用高级精简指令集微处理器(ARM)作为主控制器,实现与上位机通信、电阻数据采集计算和比较器输出控制.依据开尔文测阻法搭建恒流源电路、信号调理电路和数据采集电路.通过设计可扩展的继电器阵列实现测量通道的快速切换,进而有效保障了激光调阻机的快速调阻.采用硬件电路直接输出晶体管-晶体管逻辑电平(TTL)信号控制激光器动作,实现对目标电阻的刻蚀,保证调阻精度与调阻效率.实验结果表明:电阻的测量精度最高为0.01％,调阻精度最高为0.1％,调阻速度最快为50个/s.

针对多臂凿岩机器人在隧道工作中易发生碰撞事故的问题,提出了一种多臂凿岩机器人的碰撞检测算法.该算法通过对机器人各机械臂间、钻臂与隧道间的实时距离进行计算和碰撞判别,实现凿岩机器人的碰撞检测.以空间圆柱体包络盒作为凿岩机器人各机械臂的碰撞检测模型,将凿岩机器人各机械臂间的碰撞检测问题转化为空间圆柱体包络盒距离求解问题,提出了一种空间两圆柱体碰撞判别方法.将钻臂与隧道轮廓的碰撞问题转化为钻臂在隧道壁面的投影矩形与隧道壁面的干涉问题,提出了一种钻臂与隧道轮廓的碰撞判别方法.最后在MATLAB软件和实际多臂凿岩机器人上进行实验,实验结果表明:该算法能实时检测凿岩机器人各机械臂间、钻臂和隧道间的碰撞情况,在钻臂进入非安全范围时及时给出碰撞信号,使系统及时预警从而避免碰撞干涉事故的发生.

针对液晶盖板检测系统多轴运动控制实时性低、可靠性差的问题,设计了一种液晶盖板检测系统的多轴运动控制器.该控制器以高级精简指令集微处理器(ARM)和现场可编程逻辑门阵列(FPGA)为控制单元,其中ARM搭载RT-Thread实时操作系统,通过以太网传输控制协议(TCP)与上位机数据交互,基于修正梯形加减速算法完成速度规划.FPGA根据速度规划的脉冲参数产生脉冲,通过输出脉冲/方向信号实现执行机构的位置控制.实验结果表明:本文所设计的运动控制器应用于4轴液晶盖板检测系统中,实现了多轴平稳运行,位置控制误差为±0.129 mm.

针对工业液压机械臂末端控制精度受惯性和摩擦等因素影响的问题,提出了一种基于深度强化学习的机械臂控制方法.首先,在机器人操作系统环境下搭建仿真机械臂并进行控制和通信模块设计.然后,对深度确定性策略梯度(DDPG)算法中的Actor-Critic网络进行设计,并基于机械臂逆运动学与深度强化学习奖励机制,设计了一种包含精度指标的分层奖励函数,促进DDPG算法收敛.最后,采用改进的DDPG算法与仿真机械臂交互训练,获得机械臂控制模型,从而实现对机械臂末端的精确控制.试验结果表明:改进的DDPG算法收敛速度提升了约14.54％,在仿真环境下机械臂可以达到6 mm的末端位置控制精度,多点测试完成率最高达到90％.

为了有效滤除电压采集信号中的高频噪声,提出了一种有限长单位冲激响应(FIR)滤波参数可调的电压采集模块设计方案.基于高级精简指令集微处理器(ARM)和现场可编程门阵列(FPGA)的硬件平台,采用FIR数字滤波器滤除采集信号中的高频噪声.ARM根据采样频率、截止频率等滤波参数自动生成滤波器系数.FPGA采用改进后的直接型滤波器,根据滤波参数调整滤波器结构,完成滤波系数与采集数据的运算.ARM与FPGA协同工作实现参数可调的FIR滤波器.实验结果表明:本文电压采集模块电压具有较高的采集精度,采集相对误差绝对值最大为0.22％.本模块能够根据设置的滤波参数,自动完成FIR滤波系数计算和滤波器结构调整,在信号采集的同时完成数据滤波,有效滤除信号中的高频噪声.

针对目前罐车装料过程中存在的智能化程度低、可靠性差等问题,提出一种罐车装料口视觉跟踪系统.通过对卷积神经网络算法的研究,提出了基于SSD的罐车装料口跟踪算法.基于迁移学习对SSD神经网络进行了训练,并建立了深度学习模型,从而实现对罐车装料口的识别跟踪.实验结果表明该系统在普通环境或复杂环境下,都能够实现对进料口的稳定跟踪,具有良好的鲁棒性和实用性.

针对传统凿岩机器人机械臂位姿数据采集方案存在采集精度不高、抗干扰能力差等问题,提出了一种凿岩机器人机械臂位姿数据采集方案.采用绝对值编码器对机械臂位姿变化数据进行采集,并基于同步串行接口(SSI)差分输入、串行输出的设计方式完成编码器数据传输.选用复杂可编程逻辑器件(CPLD)作为控制器,微处理器(ARM)STM32F412作为系统处理核心,通过控制器局域网络(CAN)接口完成数据输出.实验结果表明:本方案具有良好的采集效果,机械臂末端位置信息在X方向、Y方向和Z方向上的最大误差为9.60 cm,最小误差为0.09 cm,满足隧道施工中偏差不得超过±10 cm的技术要求.该方案保证了采集系统的采集精度,提高了数据传输过程中的抗干扰能力.

针对目前嵌入式视觉系统的图像识别算法复杂、鲁棒性差等问题,提出了一种嵌入式智能视觉系统.该系统以进阶精简指令集机器Cortex-A53为核心搭建硬件平台,并搭载Linux嵌入式操作系统.基于V4L2接口设计图像采集程序,完成图像的采集及存储.通过移植学习框架,采用Inception-V3神经网络模型实现图像的智能化识别.实验结果表明:该系统具有良好的采集效果,对不同物体和同一物体在不同姿态下的识别准确度均可达100％,满足智能视觉系统的设计要求.

Sensing technology is the core and foundation of intelligent manufacturing and modern industry. In view of the characteristics of the industry in the field of mechanical and electrical engineering, as well as the needs of the students' training, especially the graduate training, the curriculum, course content, teaching and examination methods of the new sensor principle and application are reformed and explored. The knowledge structure and system are perfected, and the application of research project and the sensor technology are strengthened. The introduction of the project teaching, the integration of "teaching, learning and doing" and online learning, are used in this teaching reform and exploration. And our workcan be used as reference for other related courses to improve the education quality.

为了防止计算机数据被U盘反向窃取,造成信息泄露,提出了一种基于光纤的U盘单向高速读取系统,可以实现数据的高速单向准确传输.基于Linux系统平台,采用管道技术,实现NAND FLASH接口对打包文件的数据传输.以光纤作为数据传输手段,基于FPGA设计数据编码和解码模块,通过研究曼彻斯特编码与同步传输的特性,设计了一种具有时钟线的数据编码传输方式,实现高速准确传输.采用高速USB与上位机进行通信,并利用SDRAM设计FIFO缓存方案,克服USB数据传输中的干扰.实验结果表明:FAT32、exFAT、NTFS 3种文件系统的U盘传输速度均在15 MB/s以上,文件传输的正确率可达到100％,满足U盘单向读取系统的需求.

针对传统相位测量仪结构复杂、精度低的缺点,提出了一种全相位快速傅里叶变换(FFT)法的高精度相位测量仪设计方案.该方案以高性能Cortex-M4内核的STM32F407VGT6作为系统核心,利用高速同步AD7606获取相位信号,采用高精度的全相位FFT法进行相位计算,并通过以太网接口实现数据的远程传输和显示.实验结果表明:采样点数取1024,测相位为0°～360°,测频为1 Hz～60 kHz时,实际测量误差小于0.003°,响应时间小于1.0 s.

针对目前以太网串口服务器串口数量固定而无法实时增减的问题,提出一种基于高级精简指令集处理器(ARM)和现场可编程门阵列(FPGA)的以太网串口服务器设计方案.该以太网串口服务器采用ARM+FPGA构架,以ARM+LAN8720芯片为以太网硬件平台,在 μC/OS-Ⅱ 操作系统上实现以太网通信.以FPGA+接口芯片作为串口硬件平台,基于FPGA构建串口单元模块实现串口通信.通过操作系统的多任务管理、可变静态存储控制器(FSMC)的映射寄存器地址操作及FPGA的多串口管理等功能的设计,实现多串口的增减操作.通过对串口增减操作过程中动态资源分配算法的研究,求出本系统能实现的最大串口数量.实验结果表明:该以太网串口服务器能够实现多串口通信,串口数量可增减,且波特率可独立设置,最大波特率可达115200 bit/s,最大串口数为35.

The two-wheel self-balancing vehicle is a dynamic balance system based on the inverted pendulum model.The key points of the vehicle body structure,posture detection and dynamic balance control in system design were sutdied.A lightweight body structure was designed by using integrated hub motor as the driving system,which could reduce the mass and energy loss of the vehicle.A 9-axis attitude sensor with gyroscope,accelerometer and compass was used to detect the body posture angle information in the control system.Kalman filtering was used in the data fusion to find optimal estimation of attitude angle.The PID motion control algorithm was used to drive the hub motor to relize the dynamic balance of the body.Through the optimization of the parameters,the response speed of system is improved,and the error of attitude angle estimation is reduced to less than 0.5°,and the autonomous dynamic balance function is realized,which provides a simple,feasible and low-cost design solution for the design of two-wheel self-balancing vehicle.