罗克韦尔1734-POINT_I_O选型与安全指南

1734
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"《1734-POINT_I_O选型指南》是一份针对Allen-Bradley AB PLC 1734系列的详细指南，旨在帮助用户理解和选择最适合他们应用需求的POINT_I/O系统。这份文档首先强调了固态设备（如1734-POINT_I_O）与传统机电设备操作上的显著区别，指出固态设备的广泛使用可能导致特定操作条件下的限制和安全注意事项，强调了罗克韦尔自动化公司在使用过程中提供的责任界限。 章节一深入解析了POINT_I/O系列，包括系统概述，其主要特性如模块化设计、高可靠性、以及支持多种输入/输出类型。用户被引导如何根据具体项目需求来指定合适的POINTI/O系统，这可能涉及到硬件配置、功能需求和性能考虑。 第二章聚焦于通信接口的选择，介绍了NetLinx开放式网络架构作为基础，这是一种灵活的工业通信协议，使得POINT_I/O能够无缝集成到各种网络环境中。 EtherNet/IP网络是重点推荐的一种选项，它提供了高效的数据传输和远程监控能力。用户需根据项目的网络环境和数据传输需求来决定采用哪种通信方式，同时要意识到实际部署中的变数和定制需求。 在整个指南中，还包含了警告、重要事项和注意等安全提示，这些都是为了确保用户在操作过程中能够注意到潜在的风险，如电气危险、高温风险等，以防止意外事故的发生。此外，手册明确指出，未经罗克韦尔自动化公司的书面许可，所有手册内容的复制和传播都是禁止的。 最后，目录显示了指南的结构，涵盖了从系统选择到网络接口的详细内容，便于读者查找所需信息。这份指南为用户提供了一个全面的工具，帮助他们正确、安全地选用和配置1734-POINT_I/O系统，以满足其工业自动化项目的需求。"

AB Logix5000 控制系统中EtherNetIP 模块用户手册.pdf

2019-09-16 上传
AB Logix5000 控制系统中EtherNetIP 模块用户手册pdf,AB Logix5000 控制系统中EtherNetIP 模块用户手册

FANUC配置1734ADN从站

2018-04-17 上传
文档详细介绍如何给FANUC机器添加一个罗克韦尔1734ADN的从站。

IPQ6000 WI-FI ACCESS POINT SOC DATA SHEET.pdf

2021-03-25 上传
IPQ6000 Wi-Fi Access Point SoC datasheet

优化 import numpy as np import open3d as o3d from sklearn.cluster import DBSCAN # 读取点云数据 pcd = o3d.io.read_point_cloud("laser.pcd") points = np.asarray(pcd.points) # DBSCAN聚类 dbscan = DBSCAN(eps=0.2, min_samples=10) dbscan.fit(points) labels = dbscan.labels_ # 获取可行驶区域点云数据 drivable_mask = labels != -1 drivable_points = points[drivable_mask] # 获取路沿点云数据 curb_mask = np.logical_and(labels != -1, points[:, 1] < 0) curb_points = points[curb_mask] # 获取车道线点云数据 line_mask = np.logical_and(labels != -1, points[:, 1] >= 0) line_points = points[line_mask] # 可视化结果 drivable_pcd = o3d.geometry.PointCloud() drivable_pcd.points = o3d.utility.Vector3dVector(drivable_points) o3d.visualization.draw_geometries([drivable_pcd]) curb_pcd = o3d.geometry.PointCloud() curb_pcd.points = o3d.utility.Vector3dVector(curb_points) o3d.visualization.draw_geometries([curb_pcd]) line_pcd = o3d.geometry.PointCloud() line_pcd.points = o3d.utility.Vector3dVector(line_points) o3d.visualization.draw_geometries([line_pcd]) 加上预处理

2023-05-11 上传
pcd = o3d.io.read_point_cloud("laser.pcd") points = np.asarray(pcd.points) # 预处理：去除离群点 mean = np.mean(points, axis=0) std = np.std(points, axis=0) inlier_mask = np.all(np.abs(points - mean) ...

import open3d as o3d # 读取点云数据 point_cloud = o3d.io.read_point_cloud("01.pcd") # 创建Voxel Grid下采样器 voxel_size = 0.1 # 设置立方体格子的大小 downpcd = point_cloud.voxel_down_sample(voxel_size) # 保持下采样后的点云数量为2048 if len(downpcd.points) > 2048: downpcd.points = downpcd.points[:2048] # 可视化结果 o3d.io.write_point_cloud("downsampled_point_cloud.pcd", downpcd) o3d.visualization.draw_geometries([downpcd])请修改以下这段代码，使得体素下采样在体素方块中随机采样一个点

2023-07-17 上传
voxel_points = np.asarray(point_cloud.points)[np.where((point_cloud.points >= voxel) & (point_cloud.points < voxel + voxel_size))] if len(voxel_points) > 0: random_index = np.random.randint(len...

double PurePursuitController::judge_lr(const TrajectoryPoint& start_point, const TrajectoryPoint& end_point, const TrajectoryPoint& j_point) { double is_prec = (start_point.path_point.y - end_point.path_point.y) * j_point.path_point.x + (end_point.path_point.x - start_point.path_point.x) * j_point.path_point.y + start_point.path_point.x * end_point.path_point.y - end_point.path_point.x * start_point.path_point.y; if (is_reverse) { is_prec = (end_point.path_point.y - start_point.path_point.y) * j_point.path_point.x + (start_point.path_point.x - end_point.path_point.x) * j_point.path_point.y + start_point.path_point.y * end_point.path_point.x - end_point.path_point.y * start_point.path_point.x; } double np = is_prec > 0.0 ? 1.0 : (-1.0); return np; }

2023-06-01 上传
这段代码是C++语言编写的。它是一个纯追踪控制器（Pure Pursuit Controller）中的一部分，用于判断当前车辆在轨迹上的位置相对于目标点的左侧还是右侧。该函数接受三个轨迹点作为输入参数，计算车辆当前位置相对于...

cloud = o3d.io.read_point_cloud("Model.pcd") Delta = 0.01 dPlatform = 0.3 point_cloud = np.asarray(cloud.points) _, _, z_min = np.amin(point_cloud, axis=0) idx = [] for i in range(len(point_cloud)): index = np.floor((point_cloud[i][2] - z_min) / dPlatform) sliceMin = z_min + index * dPlatform if sliceMin <= point_cloud[i][2] < sliceMin + Delta: idx.append(i) slicing_cloud = (cloud.select_by_index(idx))这段代码获取了几个点云切片，如何改可以单独获得每个切片

2023-06-05 上传
for i in range(len(point_cloud)): # 获取当前切片中的点 if sliceMin <= point_cloud[i][2] idx.append(i) slicing_clouds.append(cloud.select_by_index(idx)) # 将当前切片存储到列表中 ``` 这样，`...

import numpy as np import open3d as o3d data = np.loadtxt('E:/radar_dataset_astyx-main/dataset_astyx_demo/radar_hires1/test01.txt', skiprows=1) point_cloud = o3d.geometry.PointCloud() point_cloud.points = o3d.utility.Vector3dVector(data[:, :3]) # 创建一个新的点云属性对象 velocity_attr = o3d.utility.DoubleVector(data[:, 3]) scatter_attr = o3d.utility.DoubleVector(data[:, 4]) # 将点云属性作为点的属性添加到点云对象中 point_cloud.point['velocity'] = o3d.utility.Vector3dVector(velocity_attr) point_cloud.point['scatter'] = o3d.utility.Vector3dVector(scatter_attr) o3d.visualization.draw_geometries([point_cloud])发生异常: RuntimeError Unable to cast Python instance to C++ type (compile in debug mode for details) File "E:\迅雷下载\serial-port-to-database-master\import open3d as o3d.py", line 14, in <module> point_cloud.point['velocity'] = o3d.utility.Vector3dVector(velocity_attr) RuntimeError: Unable to cast Python instance to C++ type (compile in debug mode for details)

2023-07-16 上传
point_cloud.point['velocity'] = o3d.utility.VectorFloat(data[:, 3].tolist()) point_cloud.point['scatter'] = o3d.utility.VectorFloat(data[:, 4].tolist()) o3d.visualization.draw_geometries([point_cloud...

在pfc6.0软件中，这段代码的具体含义是什么zone create brick size (4,1,11) point 0 ([zhuan_length*1.5],[-zhuan_Width*0.5],[-zhuan_height*5.5]) ... point 1 ([zhuan_length*5.5],[-zhuan_Width*0.5],[-zhuan_height*5.5]) ... point 2 ([zhuan_length*1.5],[zhuan_Width*0.5],[-zhuan_height*5.5]) ... point 3 ([zhuan_length*1.5],[-zhuan_Width*0.5],[zhuan_height*5.5]) zone create brick size (4,1,11) point 0 ([-zhuan_length*5.5],[-zhuan_Width*0.5],[-zhuan_height*5.5]) ... point 1 ([-zhuan_length*1.5],[-zhuan_Width*0.5],[-zhuan_height*5.5]) ... point 2 ([-zhuan_length*5.5],[zhuan_Width*0.5],[-zhuan_height*5.5]) ... point 3 ([-zhuan_length*5.5],[-zhuan_Width*0.5],[zhuan_height*5.5]) zone create brick size (3,1,4) point 0 ([-zhuan_length*1.5],[-zhuan_Width*0.5],[zhuan_height*1.5]) ... point 1 ([zhuan_length*1.5],[-zhuan_Width*0.5],[zhuan_height*1.5]) ... point 2 ([-zhuan_length*1.5],[zhuan_Width*0.5],[zhuan_height*1.5]) ... point 3 ([-zhuan_length*1.5],[-zhuan_Width*0.5],[zhuan_height*5.5]) zone create brick size (3,1,4) point 0 ([-zhuan_length*1.5],[-zhuan_Width*0.5],[-zhuan_height*5.5]) ... point 1 ([zhuan_length*1.5],[-zhuan_Width*0.5],[-zhuan_height*5.5]) ... point 2 ([-zhuan_length*1.5],[zhuan_Width*0.5],[-zhuan_height*5.5]) ... point 3 ([-zhuan_length*1.5],[-zhuan_Width*0.5],[-zhuan_height*1.5]) zone cmodel assign elastic zone property young [emod] poisson 0.25

2023-05-23 上传
这段代码是在pfc6.0软件中创建了四个矩形的“zone”，也就是空间中的区域。每个矩形都通过四个点的坐标来定义，每个点的坐标由“zhuan_length”，“zhuan_Width”和“zhuan_height”乘以不同的系数得到。...

x, y = np.meshgrid(np.arange(range_x), np.arange(range_y)) # np.savetxt('reshape_data.txt', x, delimiter=' ', fmt="%i") x_o = x - range_x / 2 y_o = y - range_y / 2 x_i = x - dx y_i = y - dy z_critical = 50 R_o = 550 R_i = 200 def crop_pointcloud(data_crop, x_o, y_o, x_i, y_i, R_o, R_i, z_critical): K_o = R_o ** 2 / range_z K_i = R_i ** 2 / range_z for z in range(range_z): r_o = np.sqrt(z * K_o) data_layer = data_crop[:, :, z] d_o = np.sqrt(x_o ** 2 + y_o ** 2) d_i = np.sqrt(x_i ** 2 + y_i ** 2) if z < z_critical: r_i = 0 else: r_i = np.sqrt(z * K_i) data_crop[:, :, z] = np.where((d_o > r_o) | (d_i <= r_i), 0, data_layer) return data_crop data_crop = crop_pointcloud(data_crop, x_o, y_o, x_i, y_i, R_o, R_i, z_critical) data_crop = data_crop[:, :, 10:] 转C++ data_crop的类型为Eigen::Tensor<uint8_t, 3, Eigen::RowMajor>

2023-06-10 上传
crop_pointcloud(data_crop, x_o, y_o, x_i, y_i, R_o, R_i, z_critical); data_crop = data_crop.slice(Eigen::array, 3>({0, 0, 10}), Eigen::array, 3>({range_x, range_y, range_z - 10})); return 0; } ``` ...
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