发那科FANUC机器人维修与安全手册

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"Mate维修说明书1CM_07 (1).pdf" 这是一份关于发那科(FANUC)机器人Mate系列的维修说明书,详细涵盖了机械保养、电气接口编程以及安全使用指南等内容。该手册是全中文版,旨在确保用户在使用和维护FANUC Robot Mate时能够遵循正确的步骤和安全规定。 FANUC Robot Mate系列是FANUC公司的一款工业机器人产品,主要面向制造业中的自动化应用。本手册中的内容分为多个部分,首先强调了在使用机器人前必须阅读和理解"FANUC Robot SAFETY HANDBOOK (B-80687EN)",这是一个专门的安全手册,包含了与机器人操作相关的全面安全信息。 安全使用须知是手册的核心部分,它提醒用户在操作机器人时,必须熟悉并遵守安全规程。手册特别指出,不同的用户角色(操作者、程序员、维修工程师)有不同的权限和责任。操作者仅负责电源开关和程序启动,而程序员和维修工程师则需要在安全栅栏内进行更复杂的任务,如示教、维护等,并且他们必须经过专业的机器人培训。 手册还警告用户,由于产品的不断改进,实物可能会与手册中的描述有所差异,且出口这些设备可能受到日本《外汇和外国贸易法》以及美国再出口法规的限制。此外,对于可能导致严重事故或损失的应用,建议设置安全装置以防止因机器人故障造成的危险。 在实际操作中,手册可能无法涵盖所有禁止或不允许的行为,因此,如果手册未明确允许的行动,应视为禁止。这强调了用户在操作过程中需具备判断力,遵守未明示的安全规则。 维修部分详细介绍了机器人的维护、修理、调整和更换作业,指导用户如何正确地进行这些工作,以保持机器人的良好运行状态和延长其使用寿命。同时,手册也提醒维修工程师,只有经过专业训练后才能在安全栅栏内进行作业,以确保人员安全。 这份维修说明书提供了FANUC Robot Mate的全面操作指南,包括安全规定、操作程序和维护保养等方面,是用户安全、有效使用和维护该机器人的重要参考资料。

const ServerParam & SP = ServerParam::i(); const int self_min = wm.interceptTable()->selfReachCycle(); const int mate_min = wm.interceptTable()->teammateReachCycle(); int opp_min = wm.interceptTable()->opponentReachCycle(); const PlayerObject * opp_fastest = wm.interceptTable()->fastestOpponent(); if ( opp_fastest && opp_fastest->goalie() && wm.gameMode().isPenaltyKickMode() && opp_fastest->pos().dist( wm.ball().pos() ) >= 3.0 ) // MAGIC NUMBER { M_tackle_situation = false; M_opponent_ball = false; dlog.addText( Logger::TEAM, __FILE__":(update) penalty shootouts. not a tackle situation" ); return; } if ( opp_fastest && wm.gameMode().isPenaltyKickMode() && ! opp_fastest->goalie() ) { const AbstractPlayerObject * opponent_goalie = wm.getTheirGoalie(); if ( opponent_goalie ) { /* //yz del std::map< const AbstractPlayerObject*, int >::const_iterator player_map_it = wm.interceptTable()->playerMap().find( opponent_goalie ); if ( player_map_it != wm.interceptTable()->playerMap().end() ) { // considering only opponent goalie in penalty-kick mode opp_min = player_map_it->second; dlog.addText( Logger::TEAM, __FILE__":(update) replaced min_opp with goalie's reach cycle (%d).", opp_min ); } else { opp_min = 1000000; // practically canceling the fastest non-goalie opponent player dlog.addText( Logger::TEAM, __FILE__":%d: (update) set opp_min as 1000000 so as not to consider the fastest opponent.", __LINE__ ); } */ } else { opp_min = 1000000; // practically canceling the fastest non-goalie opponent player dlog.addText( Logger::TEAM, __FILE__":%d (update) set opp_min as 1000000 so as not to consider the fastest opponent.", __LINE__); } }

2023-07-25 上传

import deap import random from deap import base, creator, tools, algorithms import numpy as np import pandas as pd # 参数 stations = 30 start_end_stations = [1, 2, 5, 8, 10, 14, 17, 18, 21, 22, 25, 26, 27, 30] min_interval = 108 min_stopping_time = 20 max_stopping_time = 120 passengers_per_train = 1860 min_small_loop_stations = 3 max_small_loop_stations = 24 average_boarding_time = 0.04 # 使用 ExcelFile ,通过将 xls 或者 xlsx 路径传入,生成一个实例 stations_kilo1 = pd.read_excel(r'D:\桌面\附件2:区间运行时间(1).xlsx', sheet_name="Sheet1") stations_kilo2 = pd.read_excel(r'D:\桌面\附件3:OD客流数据(1).xlsx', sheet_name="Sheet1") stations_kilo3 = pd.read_excel(r'D:\桌面\附件4:断面客流数据.xlsx', sheet_name="Sheet1") print(stations_kilo1) print(stations_kilo2) print(stations_kilo3) # 适应度函数 def fitness_function(individual): big_loop_trains, small_loop_trains, small_loop_start, small_loop_end = individual small_loop_length = small_loop_end - small_loop_start if small_loop_length < min_small_loop_stations or small_loop_length > max_small_loop_stations: return 1e9, cost = (big_loop_trains + small_loop_trains) * (stations - 1) * min_interval + average_boarding_time * passengers_per_train * (big_loop_trains + small_loop_trains) return cost, # 创建适应度和个体类 creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", list, fitness=creator.FitnessMin) # 注册初始化函数 toolbox = base.Toolbox() toolbox.register("big_loop_trains", random.randint, 1, 10) toolbox.register("small_loop_trains", random.randint, 1, 10) toolbox.register("small_loop_start", random.choice, start_end_stations) toolbox.register("small_loop_end", random.choice, start_end_stations) toolbox.register("individual", tools.initCycle, creator.Individual, (toolbox.big_loop_trains, toolbox.small_loop_trains, toolbox.small_loop_start, toolbox.small_loop_end), n=1) toolbox.register("population", tools.initRepeat, list, toolbox.individual) # 注册遗传算法操作 toolbox.register("mate", tools.cxTwoPoint) toolbox.register("mutate", tools.mutUniformInt, low=[1, 1, min(start_end_stations), min(start_end_stations)], up=[10, 10, max(start_end_stations), max(start_end_stations)], indpb=0.5) toolbox.register("select", tools.selBest) toolbox.register("evaluate", fitness_function) # 设置遗传算法参数 population_size = 100 crossover_probability = 0.8 mutation_probability = 0.2 num_generations = 100 # 初始化种群 population = toolbox.population(n=population_size) # 进化 for gen in range(num_generations): offspring = algorithms.varAnd(population, toolbox, cxpb=crossover_probability, mutpb=mutation_probability) fits = toolbox.map(toolbox.evaluate, offspring) for fit, ind in zip(fits, offspring): ind.fitness.values = fit population = toolbox.select(offspring, k=len(population)) # 找到最佳个体 best_individual = tools.selBest(population, k=1)[0] # 解码最佳个体 big_loop_trains, small_loop_trains, small_loop_start, small_loop_end = best_individual # 输出结果 print("Big Loop Trains:", big_loop_trains) print("Small Loop Trains:", small_loop_trains) print("Small Loop Start Station:", small_loop_start) print("Small Loop End Station:", small_loop_end)分析代码

2023-04-22 上传