Velocity #parse utf-8 多一行空格的问题

本文详细阐述了Velocity在处理utf-8文件时遇到的BOM问题,以及通过配置FileResourceLoader和设置unicode=true来解决此问题的方法。

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昨天做项目的时候,把 #parse 的子文件块从 ansi 改成了 utf-8 ,结果在网页上就多出来一行空白,查看源代码也没发现这行空白怎么来的.只有在firebug下查看DOM才能看的到.

原因倒是很快就找到了. Velocity 在对 utf-8 加载的时候有兼容性问题,会将 utf-8 的 BOM 读取并解析成一个空行.

解决办法,就是使用 FileResourceLoader 代替默认的 ResourceLoader .并指定 unicode = true.

Velocity 配置如下:

resource.loader = file
file.resource.loader.class = org.apache.velocity.runtime.resource.loader.FileResourceLoader
file.resource.loader.path= D:/eworkspace/.......  这里换成自己的VM物理目录.
file.resource.loader.cache = false  是否使用缓存模板,建议在开发的时候设置为关闭.实际上线运行的时候打开,可以提升效率.
file.resource.loader.unicode = true  该配置可忽略utf-8 的BOM解析.
file.resource.loader.modificationCheckInterval = 5 当模板设置成缓存后,检测模板变更间隔时间.


晚上花了好几个小时在找怎么把 VM目录换成网站的相对路径,一直找不到结果,只能作罢...
在mujoco中进行可视化 ,修正以下代码#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ 人形机器人仿真演示 - 优化版本 """ import sys import os import time import json import numpy as np import matplotlib.pyplot as plt from typing import Dict, List, Tuple, Optional import threading import argparse import mujoco import pygame from pygame.locals import * import math import cv2 from PIL import Image import io import traceback import subprocess import ctypes import glfw # 检查OpenGL支持并尝试修复 def fix_opengl_issues(): """尝试解决OpenGL初始化问题""" print("🛠️ 尝试解决OpenGL初始化问题...") # 尝试设置不同的渲染后端 backends = ['glfw', 'osmesa', 'egl'] for backend in backends: glfw_initialized = False try: os.environ['MUJOCO_GL'] = backend print(f" 尝试使用 {backend} 渲染后端...") # 初始化GLFW窗口用于测试 if not glfw.init(): raise RuntimeError("GLFW初始化失败") glfw_initialized = True # 创建隐藏窗口 glfw.window_hint(glfw.VISIBLE, glfw.FALSE) window = glfw.create_window(640, 480, "OpenGL Test", None, None) if not window: raise RuntimeError("GLFW窗口创建失败") glfw.make_context_current(window) print(f"✅ 使用 {backend} 后端成功创建OpenGL上下文!") # 测试OpenGL功能 glClearColor = ctypes.CDLL(None).glClearColor glClearColor(0.2, 0.3, 0.4, 1.0) print("✅ OpenGL基本功能测试通过") glfw.destroy_window(window) glfw.terminate() glfw_initialized = False return True except Exception as e: print(f"⚠️ {backend} 后端失败: {e}") if 'window' in locals() and window: glfw.destroy_window(window) if glfw_initialized: glfw.terminate() # 尝试加载OpenGL库 try: print(" 尝试直接加载OpenGL库...") opengl_libs = [ 'libGL.so.1', # Linux '/usr/lib/x86_64-linux-gnu/libGL.so.1', # Ubuntu 'opengl32.dll', # Windows '/System/Library/Frameworks/OpenGL.framework/OpenGL' # macOS ] for lib in opengl_libs: try: ctypes.CDLL(lib) print(f"✅ 成功加载 {lib}") return True except Exception as e: print(f"⚠️ 加载 {lib} 失败: {e}") except Exception as e: print(f"⚠️ OpenGL库加载失败: {e}") print("💡 建议解决方案:") print(" 1. 更新显卡驱动程序") print(" 2. 安装系统OpenGL库 (Linux: libgl1-mesa-glx, Windows: OpenGL runtime)") print(" 3. 在虚拟环境中使用: conda install -c conda-forge glew glfw") return False # 导入自定义模块 try: from mujoco_simulation import HumanoidRobot from advanced_control import ( IntelligentDecisionSystem, PathPlanner, EnergyOptimizer, AdaptiveController, EnvironmentState, RobotState, TaskType, TerrainType ) except ImportError: # 创建模拟类以避免导入错误 class HumanoidRobot: def __init__(self): self.model = None self.data = None self.position = np.array([0.0, 0.0, 1.0]) self.velocity = np.zeros(3) self.orientation = np.array([1.0, 0.0, 0.0, 0.0]) class IntelligentDecisionSystem: pass class VisualizationSystem: """可视化系统 - 用于2D和3D渲染""" def __init__(self, model: Optional[mujoco.MjModel] = None, width: int = 1200, height: int = 800): """初始化可视化系统""" pygame.init() # 使用标准2D渲染模式,避免OpenGL模式冲突 self.screen = pygame.display.set_mode((width, height)) pygame.display.set_caption("人形机器人仿真演示") self.clock = pygame.time.Clock() self.font = pygame.font.SysFont('SimHei', 20) self.title_font = pygame.font.SysFont('SimHei', 28, bold=True) # 地形颜色配置 self.terrain_colors = { 'flat': (180, 200, 180), 'slope': (160, 180, 200), 'stairs': (200, 180, 160), 'sand': (220, 200, 150), 'grass': (150, 200, 150) } self.obstacle_colors = { 'static': (200, 100, 100), 'dynamic': (100, 150, 200), 'moving': (150, 100, 200) } # 创建机器人图像 self.robot_img = self._create_robot_image() # 地形纹理缓存 self.terrain_textures = { 'flat': self._create_terrain_texture('flat'), 'slope': self._create_terrain_texture('slope'), 'stairs': self._create_terrain_texture('stairs'), 'sand': self._create_terrain_texture('sand'), 'grass': self._create_terrain_texture('grass') } # 尝试修复OpenGL问题 self.opengl_fixed = fix_opengl_issues() self.using_software_rendering = False # 初始化MuJoCo渲染组件 self.camera = mujoco.MjvCamera() self.opt = mujoco.MjvOption() self.scene = None self.context = None if model is not None and self.opengl_fixed: try: # 尝试创建渲染上下文 self.context = mujoco.MjrContext(model, mujoco.mjtFontScale.mjFONTSCALE_150) self.scene = mujoco.MjvScene(model=model, maxgeom=1000) print("✅ 成功初始化渲染上下文") # 设置相机位置 self.camera.distance = 5.0 self.camera.elevation = -20 self.camera.azimuth = 90 except Exception as e: print(f"⚠️ 渲染上下文初始化失败: {e}") self.scene = None self.context = None self.using_software_rendering = True else: print("⚠️ 无法修复OpenGL问题,使用软件渲染") self.using_software_rendering = True def _create_robot_image(self): """创建机器人图像""" surface = pygame.Surface((50, 80), pygame.SRCALPHA) # 绘制头部 pygame.draw.circle(surface, (100, 150, 255), (25, 15), 10) # 绘制身体 pygame.draw.rect(surface, (100, 200, 100), (15, 25, 20, 30)) # 绘制腿部 pygame.draw.line(surface, (0, 0, 0), (20, 55), (15, 75), 3) pygame.draw.line(surface, (0, 0, 0), (30, 55), (35, 75), 3) # 绘制手臂 pygame.draw.line(surface, (0, 0, 0), (15, 35), (5, 50), 3) pygame.draw.line(surface, (0, 0, 0), (35, 35), (45, 50), 3) return surface def _create_terrain_texture(self, terrain_type: str): """创建地形纹理""" size = (350, 100) texture = pygame.Surface(size, pygame.SRCALPHA) base_color = self.terrain_colors[terrain_type] if terrain_type == 'flat': # 平地的简单纹理 for i in range(0, size[0], 10): for j in range(0, size[1], 10): color_variation = np.random.randint(-10, 10) color = ( max(0, min(255, base_color[0] + color_variation)), max(0, min(255, base_color[1] + color_variation)), max(0, min(255, base_color[2] + color_variation)) ) pygame.draw.rect(texture, color, (i, j, 10, 10)) elif terrain_type == 'slope': # 斜坡纹理 for i in range(size[0]): height = int(size[1] * (1 - i / (size[0] * 1.5))) pygame.draw.line(texture, base_color, (i, height), (i, size[1]), 1) if i % 10 == 0: pygame.draw.line(texture, (150, 150, 150), (i, height), (i, size[1]), 1) elif terrain_type == 'stairs': # 楼梯纹理 step_height = 20 step_width = 70 for i in range(5): pygame.draw.rect(texture, (180, 160, 140), (i * step_width, size[1] - (i + 1) * step_height, step_width, step_height)) elif terrain_type == 'sand': # 沙地纹理 texture.fill(base_color) for _ in range(100): x = np.random.randint(0, size[0]) y = np.random.randint(0, size[1]) radius = np.random.randint(2, 5) shade = np.random.randint(-15, 15) color = ( max(0, min(255, base_color[0] + shade)), max(0, min(255, base_color[1] + shade)), max(0, min(255, base_color[2] + shade)) ) pygame.draw.circle(texture, color, (x, y), radius) elif terrain_type == 'grass': # 草地纹理 texture.fill(base_color) for i in range(0, size[0], 5): for j in range(0, size[1], 15): pygame.draw.line(texture, (100, 180, 100), (i, j), (i + np.random.randint(-2, 2), j - np.random.randint(5, 10)), 1) return texture def render_2d(self, demo, current_time: float): """渲染2D可视化界面""" self.screen.fill((240, 240, 245)) # 获取当前场景状态 scenario_state = demo.get_current_scenario_state(current_time) # 绘制标题 title = self.title_font.render("人形机器人仿真演示系统", True, (30, 30, 100)) self.screen.blit(title, (20, 15)) # 绘制状态面板 self._render_status_panel(demo, scenario_state, current_time) # 绘制场景可视化 self._render_scenario_visualization(demo, scenario_state, current_time) # 绘制3D渲染窗口 self._render_3d_view(demo) # 绘制性能图表 self._render_performance_charts(demo) # 绘制控制说明 self._render_instructions() pygame.display.flip() def _render_status_panel(self, demo, scenario_state: Dict, current_time: float): """渲染状态面板""" # 状态面板背景 pygame.draw.rect(self.screen, (250, 250, 255), (20, 70, 450, 180), 0, 10) pygame.draw.rect(self.screen, (200, 200, 220), (20, 70, 450, 180), 2, 10) # 场景信息 scene_text = self.font.render(f"场景: {scenario_state['description']}", True, (30, 30, 30)) self.screen.blit(scene_text, (40, 90)) # 地形信息 terrain_text = self.font.render(f"地形: {scenario_state['terrain']}", True, (30, 30, 30)) self.screen.blit(terrain_text, (40, 120)) # 时间信息 time_text = self.font.render(f"时间: {current_time:.1f}s / {demo.demo_config['duration']:.1f}s", True, (30, 30, 30)) self.screen.blit(time_text, (40, 150)) # 能量消耗 energy_text = self.font.render( f"能量消耗: {demo.energy_consumption[-1] if demo.energy_consumption else 0:.2f} J", True, (30, 30, 30)) self.screen.blit(energy_text, (40, 180)) # 控制模式 mode_text = self.font.render(f"控制模式: {'AI控制' if demo.demo_config['enable_ai'] else '手动控制'}", True, (30, 30, 30)) self.screen.blit(mode_text, (40, 210)) # 渲染状态 render_status = "硬件加速" if not self.using_software_rendering else "软件渲染" render_text = self.font.render(f"渲染模式: {render_status}", True, (30, 30, 30)) self.screen.blit(render_text, (250, 180)) # 状态指示器 status_color = (100, 200, 100) if demo.is_running else (200, 100, 100) pygame.draw.circle(self.screen, status_color, (400, 100), 10) status_text = self.font.render("运行中" if demo.is_running else "已停止", True, (30, 30, 30)) self.screen.blit(status_text, (415, 95)) # 暂停状态 if demo.paused: pause_text = self.font.render("已暂停", True, (200, 100, 50)) self.screen.blit(pause_text, (400, 130)) def _render_scenario_visualization(self, demo, scenario_state: Dict, current_time: float): """渲染场景可视化""" # 场景可视化背景 pygame.draw.rect(self.screen, (250, 250, 255), (20, 270, 450, 300), 0, 10) pygame.draw.rect(self.screen, (200, 200, 220), (20, 270, 450, 300), 2, 10) # 使用预生成的地形纹理 terrain_type = scenario_state['terrain'] if terrain_type in self.terrain_textures: terrain_img = self.terrain_textures[terrain_type] self.screen.blit(terrain_img, (50, 350)) else: # 默认地形 terrain_color = self.terrain_colors.get(terrain_type, (180, 180, 180)) pygame.draw.rect(self.screen, terrain_color, (50, 350, 350, 100)) # 绘制障碍物 for obs in scenario_state['obstacles']: color = self.obstacle_colors.get(obs['type'], (150, 150, 150)) x = 50 + (obs['position'][0] / 10) * 350 y = 400 - (obs['position'][1] / 2) * 50 radius = obs['radius'] * 50 # 绘制障碍物阴影增加深度感 pygame.draw.circle(self.screen, (100, 100, 100), (int(x - 3), int(y + 3)), int(radius)) pygame.draw.circle(self.screen, color, (int(x), int(y)), int(radius)) # 动态障碍物标记 if obs['type'] == 'dynamic': pygame.draw.circle(self.screen, (255, 255, 0), (int(x + radius * 0.6), int(y - radius * 0.6)), int(radius * 0.3)) # 绘制机器人 progress = min(current_time / demo.demo_config['duration'], 1.0) robot_x = 50 + progress * 350 robot_y = 400 - 30 * math.sin(progress * 20) # 添加行走动画效果 self.screen.blit(self.robot_img, (robot_x - 25, robot_y - 40)) # 绘制风力效果 if np.linalg.norm(scenario_state['wind']) > 0.1: wind_dir = scenario_state['wind'] / np.linalg.norm(scenario_state['wind']) for i in range(5): offset = i * 20 pygame.draw.line(self.screen, (100, 150, 255), (robot_x + 30 + offset, robot_y - 20), (robot_x + 50 + offset + wind_dir[0] * 10, robot_y - 20 + wind_dir[1] * 10), 2) pygame.draw.polygon(self.screen, (100, 150, 255), [ (robot_x + 50 + offset + wind_dir[0] * 10, robot_y - 20 + wind_dir[1] * 10), (robot_x + 45 + offset + wind_dir[0] * 15, robot_y - 25 + wind_dir[1] * 15), (robot_x + 45 + offset + wind_dir[0] * 15, robot_y - 15 + wind_dir[1] * 15) ]) # 场景标题 scene_title = self.font.render(f"当前场景: {scenario_state['description']}", True, (30, 30, 100)) self.screen.blit(scene_title, (40, 290)) def _render_3d_view(self, demo): """渲染3D视图""" # 3D视图背景 pygame.draw.rect(self.screen, (250, 250, 255), (500, 70, 680, 300), 0, 10) pygame.draw.rect(self.screen, (200, 200, 220), (500, 70, 680, 300), 2, 10) # 添加标题 title = self.font.render("机器人3D视图", True, (30, 30, 100)) self.screen.blit(title, (510, 80)) # 检查渲染组件是否可用 if self.scene is None or self.context is None or demo.robot.model is None or self.using_software_rendering: error_text = self.font.render("3D渲染不可用: 使用软件渲染模式", True, (100, 100, 200)) self.screen.blit(error_text, (520, 150)) # 显示替代图像 pygame.draw.rect(self.screen, (200, 220, 240), (550, 150, 580, 200)) pygame.draw.circle(self.screen, (100, 150, 255), (700, 250), 60) pygame.draw.rect(self.screen, (100, 200, 100), (660, 200, 80, 100)) pygame.draw.line(self.screen, (0, 0, 0), (670, 300), (650, 350), 4) pygame.draw.line(self.screen, (0, 0, 0), (730, 300), (750, 350), 4) pygame.draw.line(self.screen, (0, 0, 0), (660, 230), (600, 250), 4) pygame.draw.line(self.screen, (0, 0, 0), (740, 230), (780, 250), 4) return try: # 更新3D场景 mujoco.mjv_updateScene( demo.robot.model, demo.robot.data, self.opt, None, self.camera, mujoco.mjtCatBit.mjCAT_ALL, self.scene) # 渲染到内存缓冲区 width, height = 680, 300 buffer = np.zeros((height, width, 3), dtype=np.uint8) viewport = mujoco.MjrRect(0, 0, width, height) mujoco.mjr_render(viewport, self.scene, self.context) # 获取渲染图像 mujoco.mjr_readPixels(buffer, None, viewport, self.context) # 换为Pygame表面 img = Image.fromarray(buffer, 'RGB') img = img.transpose(Image.FLIP_TOP_BOTTOM) img_bytes = img.tobytes() py_image = pygame.image.fromstring(img_bytes, (width, height), 'RGB') # 绘制到屏幕 self.screen.blit(py_image, (500, 70)) except Exception as e: # 显示错误信息 error_text = self.font.render(f"3D渲染错误: {str(e)}", True, (255, 0, 0)) self.screen.blit(error_text, (510, 150)) # 记录详细错误日志 print(f"⚠️ 3D渲染错误详情: {traceback.format_exc()}") def _render_performance_charts(self, demo): """渲染性能图表""" # 性能面板背景 pygame.draw.rect(self.screen, (250, 250, 255), (500, 390, 680, 180), 0, 10) pygame.draw.rect(self.screen, (200, 200, 220), (500, 390, 680, 180), 2, 10) # 标题 title = self.font.render("性能指标", True, (30, 30, 100)) self.screen.blit(title, (510, 400)) # 绘制能量消耗图表 if len(demo.energy_consumption) > 1: points = [] max_energy = max(demo.energy_consumption) if max(demo.energy_consumption) > 0 else 1 for i, val in enumerate(demo.energy_consumption): x = 520 + (i / (len(demo.energy_consumption) - 1)) * 300 y = 550 - (val / max_energy) * 100 points.append((x, y)) if len(points) > 1: pygame.draw.lines(self.screen, (100, 150, 255), False, points, 2) # 绘制最后一个点 pygame.draw.circle(self.screen, (100, 150, 255), (int(points[-1][0]), int(points[-1][1])), 4) # 添加当前值标签 value_text = self.font.render(f"{val:.1f}J", True, (100, 150, 255)) self.screen.blit(value_text, (int(points[-1][0]) + 5, int(points[-1][1]) - 10)) # 绘制稳定性图表 if len(demo.performance_metrics) > 1: points = [] max_stability = max([m['stability'] for m in demo.performance_metrics]) or 1 for i, metric in enumerate(demo.performance_metrics): x = 850 + (i / (len(demo.performance_metrics) - 1)) * 300 y = 550 - (metric['stability'] / max_stability) * 100 points.append((x, y)) if len(points) > 1: pygame.draw.lines(self.screen, (255, 150, 100), False, points, 2) # 绘制最后一个点 pygame.draw.circle(self.screen, (255, 150, 100), (int(points[-1][0]), int(points[-1][1])), 4) # 添加当前值标签 value_text = self.font.render(f"{metric['stability']:.2f}", True, (255, 150, 100)) self.screen.blit(value_text, (int(points[-1][0]) + 5, int(points[-1][1]) - 10)) # 图表标签 energy_label = self.font.render("能量消耗", True, (100, 150, 255)) self.screen.blit(energy_label, (520, 560)) stability_label = self.font.render("稳定性指标", True, (255, 150, 100)) self.screen.blit(stability_label, (850, 560)) # 添加图表标题 pygame.draw.line(self.screen, (100, 150, 255), (520, 530), (540, 530), 3) pygame.draw.line(self.screen, (255, 150, 100), (850, 530), (870, 530), 3) def _render_instructions(self): """渲染控制说明""" # 控制说明背景 pygame.draw.rect(self.screen, (250, 250, 255), (20, 580, 1160, 100), 0, 10) pygame.draw.rect(self.screen, (200, 200, 220), (20, 580, 1160, 100), 2, 10) # 控制说明 instructions = [ "ESC: 退出程序", "空格键: 暂停/继续", "R键: 重置演示", "↑↓←→: 控制机器人移动", "C键: 切换摄像头视角", "S键: 保存性能数据" ] for i, text in enumerate(instructions): inst_text = self.font.render(text, True, (80, 80, 180)) self.screen.blit(inst_text, (40 + (i % 3) * 380, 600 + (i // 3) * 30)) def handle_events(self): """处理Pygame事件""" for event in pygame.event.get(): if event.type == QUIT: return False elif event.type == KEYDOWN: if event.key == K_ESCAPE: return False if event.key == K_SPACE: return "pause" elif event.key == K_r: return "reset" elif event.key == K_UP: return "move_forward" elif event.key == K_DOWN: return "move_backward" elif event.key == K_LEFT: return "move_left" elif event.key == K_RIGHT: return "move_right" elif event.key == K_c: return "change_camera" elif event.key == K_s: return "save_data" return True class HumanoidDemo: """人形机器人仿真演示类""" def __init__(self): """初始化演示系统""" print("🤖 人形机器人仿真演示系统") print("=" * 60) # 创建人形机器人 self.robot = self._create_robot() # 创建高级控制模块 self.decision_system = IntelligentDecisionSystem() self.path_planner = PathPlanner() self.energy_optimizer = EnergyOptimizer() self.adaptive_controller = AdaptiveController() # 创建可视化系统 - 传递机器人模型 self.visualization = VisualizationSystem( self.robot.model if hasattr(self.robot, 'model') else None ) # 演示配置 self.demo_config = { 'duration': 30.0, 'enable_ai': True, 'enable_optimization': True, 'enable_adaptation': True, 'record_data': True, 'save_video': False } # 演示数据 self.demo_data = { 'timestamps': [], 'robot_states': [], 'environment_states': [], 'decisions': [], 'energy_consumption': [0.0], 'performance_metrics': [{'stability': 1.0, 'efficiency': 1.0}] } # 运行状态 self.is_running = False self.current_time = 0.0 self.paused = False self.camera_mode = 0 # 0: 默认, 1: 跟随, 2: 第一人称 print("✅ 演示系统初始化完成") def _create_robot(self): """创建机器人实例,处理可能的错误""" try: return HumanoidRobot() except Exception as e: print(f"⚠️ 创建机器人时出错: {e}") print("⚠️ 使用模拟机器人替代") return HumanoidRobot() # 使用之前定义的模拟类 @property def energy_consumption(self): return self.demo_data['energy_consumption'] @property def performance_metrics(self): return self.demo_data['performance_metrics'] def setup_demo_scenario(self, scenario_type: str = "comprehensive"): """设置演示场景""" scenarios = { "comprehensive": self._setup_comprehensive_scenario, "walking": self._setup_walking_scenario, "obstacle": self._setup_obstacle_scenario, "terrain": self._setup_terrain_scenario, "interference": self._setup_interference_scenario } if scenario_type in scenarios: print(f"🎬 设置 {scenario_type} 演示场景...") scenarios[scenario_type]() else: print(f"⚠️ 未知场景类型: {scenario_type},使用综合场景") self._setup_comprehensive_scenario() def _setup_comprehensive_scenario(self): """设置综合演示场景""" self.scenario_timeline = [ [0, 5, "walking", "平地行走"], [5, 10, "obstacle", "动态避障"], [10, 15, "terrain", "斜坡行走"], [15, 20, "terrain", "楼梯攀爬"], [20, 25, "interference", "风力干扰"], [25, 30, "walking", "恢复行走"] ] self.environment_config = { 'obstacles': [ {'position': [2, 0, 0.5], 'radius': 0.3, 'type': 'static'}, {'position': [4, 1, 0.3], 'radius': 0.3, 'type': 'dynamic'}, {'position': [6, -1, 0.4], 'radius': 0.2, 'type': 'moving'} ], 'terrain_sequence': ['flat', 'slope', 'stairs', 'sand', 'flat'], 'wind_patterns': [ {'time': [20, 25], 'force': [50, 0, 0], 'type': 'gust'}, {'time': [25, 30], 'force': [20, 10, 0], 'type': 'steady'} ], 'light_patterns': [ {'time': [22, 24], 'intensity': 0.2, 'type': 'dim'}, {'time': [24, 26], 'intensity': 0.9, 'type': 'bright'} ] } def _setup_walking_scenario(self): """设置行走演示场景""" self.scenario_t极line = [ [0, 10, "walking", "基础行走"], [10, 20, "walking", "快速行走"], [20, 30, "walking", "慢速行走"] ] self.environment_config = { 'obstacles': [], 'terrain_sequence': ['flat'] * 3, 'wind_patterns': [], 'light_patterns': [] } def _setup_obstacle_scenario(self): """设置避障演示场景""" self.scenario_timeline = [ [0, 10, "obstacle", "静态障碍物"], [10, 20, "obstacle", "动态障碍物"], [20, 30, "obstacle", "复杂障碍物"] ] self.environment_config = { 'obstacles': [ {'position': [1, 0, 0.5], 'radius': 0.3, 'type': 'static'}, {'position': [3, 0, 0.3], 'radius': 0.2, 'type': 'dynamic'}, {'position': [5, 0, 0.4], 'radius': 0.25, 'type': 'moving'} ], 'terrain_sequence': ['flat'] * 3, 'wind_patterns': [], 'light_patterns': [] } def _setup_terrain_scenario(self): """设置地形演示场景""" self.scenario_timeline = [ [0, 6, "terrain", "平地"], [6, 12, "terrain", "斜坡"], [12, 18, "terrain", "楼梯"], [极8, 24, "terrain", "沙地"], [24, 30, "terrain", "平地"] ] self.environment_config = { 'obstacles': [], 'terrain_sequence': ['flat', 'slope', 'stairs', 'sand', 'flat'], 'wind_patterns': [], 'light_patterns': [] } def _setup_interference_scenario(self): """设置干扰演示场景""" self.scenario_timeline = [ [0, 10, "walking", "正常行走"], [10, 20, "interference", "风力干扰"], [20, 30, "interference", "光照干扰"] ] self.environment_config = { 'obstacles': [], 'terrain_sequence': ['flat'] * 3, 'wind_patterns': [ {'time': [10, 20], 'force': [80, 0, 0], 'type': 'strong_wind'} ], 'light_patterns': [ {'time': [20, 30], 'intensity': 0.1, 'type': 'low_light'} ] } def get_current_scenario_state(self, current_time: float) -> Dict: """获取当前场景状态""" current_task = "idle" task_description = "待机" # 确定当前任务 for start_time, end_time, task, description in self.scenario_timeline: if start_time <= current_time < end_time: current_task = task task_description = description break # 确定当前地形 terrain_index = min(int(current_time / 6), len(self.environment_config['terrain_sequence']) - 1) current_terrain = self.environment_config['terrain_sequence'][terrain_index] # 计算当前风力 current_wind = np.zeros(3) for wind_pattern in self.environment_config['wind_patterns']: if wind_pattern['time'][0] <= current_time < wind_pattern['time'][1]: current_wind = np.array(wind_pattern['force']) break # 计算当前光照 current_light = 1.0 for light_pattern in self.environment_config['light_patterns']: if light_pattern['time'][0] <= current_time < light_pattern['time'][1]: current_light = light_pattern['intensity'] break return { 'task': current_task, 'description': task_description, 'terrain': current_terrain, 'wind': current_wind, 'light': current_light, 'obstacles': self.environment_config['obstacles'].copy(), 'time': current_time } def update_robot_state(self): """更新机器人状态""" # 模拟机器人控制逻辑 # 这里应该调用实际的机器人控制模块 pass def change_camera_mode(self): """切换摄像头视角""" self.camera_mode = (self.camera_mode + 1) % 3 if self.visualization.camera: if self.camera_mode == 0: # 默认视角 self.visualization.camera.distance = 5.0 self.visualization.camera.elevation = -20 self.visualization.camera.azimuth = 90 elif self.camera_mode == 1: # 跟随视角 self.visualization.camera.distance = 3.0 self.visualization.camera.elevation = -10 self.visualization.camera.azimuth = 45 elif self.camera_mode == 2: # 第一人称视角 self.visualization.camera.distance = 1.5 self.visualization.camera.elevation = 0 self.visualization.camera.azimuth = 0 def record_data(self, current_time): """记录演示数据""" self.demo_data['timestamps'].append(current_time) # 模拟能量消耗 - 根据地形和任务类型变化 energy_factor = 1.0 if self.get_current_scenario_state(current_time)['terrain'] == 'slope': energy_factor = 1.5 elif self.get_current_scenario_state(current_time)['terrain'] == 'stairs': energy_factor = 1.8 elif self.get_current_scenario_state(current_time)['terrain'] == 'sand': energy_factor = 2.0 new_energy = self.energy_consumption[-1] + 0.1 * energy_factor self.energy_consumption.append(new_energy) # 模拟性能指标 stability = 1.0 - np.random.uniform(0, 0.1) efficiency = 1.0 - np.random.uniform(0, 0.05) self.performance_metrics.append({ 'stability': max(0.1, stability), 'efficiency': max(0.1, efficiency) }) def save_performance_data(self): """保存性能数据""" filename = f"performance_data_{time.strftime('%Y%m%d_%H%M%S')}.json" try: with open(filename, 'w') as f: json.dump({ 'timestamps': self.demo_data['timestamps'], 'energy_consumption': self.demo_data['energy_consumption'], 'performance_metrics': self.demo_data['performance_metrics'] }, f, indent=2) print(f"✅ 性能数据已保存到 {filename}") return True except Exception as e: print(f"⚠️ 保存性能数据失败: {e}") return False def run_demo(self): """运行演示""" print(f"🚀 开始演示,持续时间: {self.demo_config['duration']}秒") print("=" * 60) self.is_running = True start_time = time.time() try: while self.current_time < self.demo_config['duration']: # 处理事件 event_result = self.visualization.handle_events() if event_result == False: break elif event_result == "pause": self.paused = not self.paused elif event_result == "reset": start_time = time.time() self.current_time = 0.0 self.demo_data = { 'timestamps': [], 'energy_consumption': [0.0], 'performance_metrics': [{'stability': 1.0, 'efficiency': 1.0}] } elif event_result == "change_camera": self.change_camera_mode() elif event_result == "save_data": self.save_performance_data() if self.paused: time.sleep(0.1) continue # 更新当前时间 self.current_time = time.time() - start_time # 更新机器人状态 self.update_robot_state() # 记录数据 if self.demo_config['record_data']: self.record_data(self.current_time) # 渲染可视化界面 self.visualization.render_2d(self, self.current_time) # 控制帧率 self.visualization.clock.tick(30) self.is_running = False print("\n✅ 演示完成!") # 自动保存性能数据 if self.demo_config['record_data']: self.save_performance_data() except Exception as e: self.is_running = False print(f"\n⛔ 演示出错: {e}") traceback.print_exc() # 出错时保存当前数据 if self.demo_config['record_data']: print("⚠️ 尝试保存当前性能数据...") self.save_performance_data() finally: pygame.quit() def main(): """主函数""" parser = argparse.ArgumentParser(description='人形机器人仿真演示') parser.add_argument('--scenario', type=str, default='comprehensive', choices=['comprehensive', 'walking', 'obstacle', 'terrain', 'interference'], help='演示场景类型') parser.add_argument('--duration', type=float, default=30.0, help='演示持续时间(秒)') parser.add_argument('--no-opengl', action='store_true', help='强制禁用OpenGL加速') args = parser.parse_args() # 创建演示实例 demo = HumanoidDemo() demo.demo_config['duration'] = args.duration # 如果指定禁用OpenGL if args.no_opengl: os.environ['MUJOCO_GL'] = 'osmesa' print("⚠️ 已禁用OpenGL硬件加速") # 设置场景 demo.setup_demo_scenario(args.scenario) # 运行演示 demo.run_demo() if __name__ == "__main__": main()
最新发布
07-29
修正代码#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ 人形机器人仿真演示 - MuJoCo原生可视化 """ import sys import os import time import json import numpy as np import matplotlib.pyplot as plt from typing import Dict, List, Tuple, Optional import threading import argparse import mujoco import glfw from mujoco import viewer import imageio from datetime import datetime # 导入自定义模块 try: from mujoco_simulation import HumanoidRobot from advanced_control import ( IntelligentDecisionSystem, PathPlanner, EnergyOptimizer, AdaptiveController, EnvironmentState, RobotState, TaskType, TerrainType ) except ImportError: # 创建模拟类以避免导入错误 class HumanoidRobot: def __init__(self): self.model = mujoco.MjModel.from_xml_string(""" <mujoco> <worldbody> <light diffuse=".5 .5 .5" pos="0 0 3" dir="0 0 -1"/> <camera name="fixed" pos="0 -3 1" xyaxes="1 0 0 0 1 2"/> <body name="floor" pos="0 0 0"> <geom size="10 10 0.1" type="box" rgba=".8 .9 .8 1"/> </body> <body name="torso" pos="0 0 1.2"> <joint type="free"/> <geom size="0.2" type="sphere" rgba="1 0 0 1"/> <body name="head" pos="0 0 0.2"> <geom size="0.15" type="sphere" rgba="0 0 1 1"/> </body> <body name="left_arm" pos="0.3 0 0"> <joint type="hinge" axis="0 1 0"/> <geom size="0.08" fromto="0 0 0 0.4 0 0" type="capsule" rgba="0 1 0 1"/> </body> <body name="right_arm" pos="-0.3 0 0"> <joint type="hinge" axis="0 1 0"/> <geom size="0.08" fromto="0 0 0 -0.4 0 0" type="capsule" rgba="0 1 0 1"/> </body> <body name="left_leg" pos="0.15 0 -0.3"> <joint type="hinge" axis="0 1 0"/> <geom size="0.08" fromto="0 0 0 0 -0.4 0" type="capsule" rgba="0 1 0 1"/> </body> <body name="right_leg" pos="-0.15 0 -0.3"> <joint type="hinge" axis="0 1 0"/> <geom size="0.08" fromto="0 0 0 0 -0.4 0" type="capsule" rgba="0 1 0 1"/> </body> </body> </worldbody> </mujoco> """) self.data = mujoco.MjData(self.model) self.position = np.array([0.0, 0.0, 1.0]) self.velocity = np.zeros(3) self.orientation = np.array([1.0, 0.0, 0.0, 0.0]) # 设置初始状态 mujoco.mj_resetData(self.model, self.data) self.data.qpos[3:7] = [1, 0, 0, 0] # 四元数方向 class IntelligentDecisionSystem: pass class MuJoCoVisualizer: """MuJoCo原生可视化系统""" def __init__(self, model, data, width=1200, height=800): self.model = model self.data = data self.width = width self.height = height self.viewer = None self.camera = "fixed" # 默认摄像头 self.camera_modes = ["fixed", "track", "free"] self.current_camera_mode = 0 self.recording = False self.video_writer = None self.video_frames = [] self.scene_state = {} # 初始化摄像头位置 self.cameras = { "fixed": {"type": "fixed", "pos": [0, -3, 1], "xyaxes": [1, 0, 0, 0, 1, 2]}, "track": {"type": "track", "distance": 3.0, "elevation": -20, "azimuth": 45}, "free": {"type": "free", "distance": 5.0, "lookat": [0, 0, 1.2], "elevation": -20, "azimuth": 90} } def init_viewer(self): def __init__(self, model, data, width=1200, height=800): self.model = model self.data = data self.width = width self.height = height self.viewer = None self.camera = "fixed" # 默认摄像头 self.camera_modes = ["fixed", "track", "free"] self.current_camera_mode = 0 self.recording = False self.video_writer = None self.video_frames = [] self.scene_state = {} # 初始化摄像头位置 self.cameras = { "fixed": {"type": "fixed", "pos": [0, -3, 1], "xyaxes": [1, 0, 0, 0, 1, 2]}, "track": {"type": "track", "distance": 3.0, "elevation": -20, "azimuth": 45}, "free": {"type": "free", "distance": 5.0, "lookat": [0, 0, 1.2], "elevation": -20, "azimuth": 90} } def init_viewer(self): """初始化MuJoCo查看器""" # 创建必要的选项对象 opt = mujoco.MjvOption() pert = mujoco.MjvPerturb() scn = mujoco.MjvScene(self.model, maxgeom=1000) # 正确初始化查看器 self.viewer = viewer.Handle( width=self.width, height=self.height, opt=opt, pert=pert, user_scn=scn ) # 设置模型和数据 self.viewer.model = self.model self.viewer.data = self.data # 设置初始摄像头 self._update_camera() # 设置可视化选项 self.viewer.vopt.flags = [ mujoco.mjtVisFlag.mjVIS_JOINT, mujoco.mjtVisFlag.mjVIS_CONTACTPOINT, mujoco.mjtVisFlag.mjVIS_CONTACTFORCE ] # 设置初始视图 self._update_camera() def _update_camera(self): """更新摄像头设置""" mode = self.camera_modes[self.current_camera_mode] cam_settings = self.cameras[mode] if mode == "fixed": self.viewer.cam.type = mujoco.mjtCamera.mjCAMERA_FIXED self.viewer.cam.fixedcamid = mujoco.mj_name2id(self.model, mujoco.mjtObj.mjOBJ_CAMERA, "fixed") elif mode == "track": self.viewer.cam.type = mujoco.mjtCamera.mjCAMERA_TRACKING self.viewer.cam.trackbodyid = mujoco.mj_name2id(self.model, mujoco.mjtObj.mjOBJ_BODY, "torso") self.viewer.cam.distance = cam_settings["distance"] self.viewer.cam.elevation = cam_settings["elevation"] self.viewer.cam.azimuth = cam_settings["azimuth"] elif mode == "free": self.viewer.cam.type = mujoco.mjtCamera.mjCAMERA_FREE self.viewer.cam.lookat[:] = cam_settings["lookat"] self.viewer.cam.distance = cam_settings["distance"] self.viewer.cam.elevation = cam_settings["elevation"] self.viewer.cam.azimuth = cam_settings["azimuth"] def toggle_camera(self): """切换摄像头模式""" self.current_camera_mode = (self.current_camera_mode + 1) % len(self.camera_modes) self._update_camera() print(f"摄像头模式切换为: {self.camera_modes[self.current_camera_mode]}") def render(self, render_state=True): """渲染当前帧""" if not self.viewer: return # 更新场景状态 self.scene_state.update({ 'task': render_state.get('task', 'idle'), 'description': render_state.get('description', '待机'), 'terrain': render_state.get('terrain', 'flat'), 'time': render_state.get('time', 0.0), 'energy': render_state.get('energy', 0.0) }) # 渲染场景 self.viewer.render() # 录制视频 if self.recording: frame = np.empty((self.height, self.width, 3), dtype=np.uint8) self.viewer.read_pixels(frame, depth=None) self.video_frames.append(frame.copy()) def start_recording(self): """开始录制视频""" self.recording = True self.video_frames = [] print("⏺️ 开始录制视频...") def stop_recording(self): """停止录制并保存视频""" if not self.recording or len(self.video_frames) == 0: return False self.recording = False timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") filename = f"simulation_{timestamp}.mp4" try: with imageio.get_writer(filename, fps=30) as writer: for frame in self.video_frames: writer.append_data(frame) print(f"✅ 视频已保存到: {filename}") self.video_frames = [] return True except Exception as e: print(f"⚠️ 保存视频失败: {e}") return False def close(self): """关闭查看器""" if self.viewer: self.viewer.close() self.viewer = None class HumanoidDemo: """人形机器人仿真演示类""" def __init__(self): """初始化演示系统""" print("🤖 人形机器人仿真演示系统") print("=" * 60) # 创建人形机器人 self.robot = self._create_robot() # 创建高级控制模块 self.decision_system = IntelligentDecisionSystem() self.path_planner = PathPlanner() self.energy_optimizer = EnergyOptimizer() self.adaptive_controller = AdaptiveController() # 创建MuJoCo可视化系统 self.visualizer = MuJoCoVisualizer(self.robot.model, self.robot.data) self.visualizer.init_viewer() # 演示配置 self.demo_config = { 'duration': 30.0, 'enable_ai': True, 'enable_optimization': True, 'enable_adaptation': True, 'record_data': True, 'save_video': False } # 演示数据 self.demo_data = { 'timestamps': [], 'robot_states': [], 'environment_states': [], 'decisions': [], 'energy_consumption': [0.0], 'performance_metrics': [{'stability': 1.0, 'efficiency': 1.0}] } # 运行状态 self.is_running = False self.current_time = 0.0 self.paused = False self.key_actions = { glfw.KEY_SPACE: 'pause', glfw.KEY_R: 'reset', glfw.KEY_C: 'change_camera', glfw.KEY_S: 'save_data', glfw.KEY_V: 'toggle_recording' } print("✅ 演示系统初始化完成") def _create_robot(self): """创建机器人实例""" try: return HumanoidRobot() except Exception as e: print(f"⚠️ 创建机器人时出错: {e}") print("⚠️ 使用模拟机器人替代") return HumanoidRobot() @property def energy_consumption(self): return self.demo_data['energy_consumption'] @property def performance_metrics(self): return self.demo_data['performance_metrics'] def setup_demo_scenario(self, scenario_type: str = "comprehensive"): """设置演示场景""" scenarios = { "comprehensive": self._setup_comprehensive_scenario, "walking": self._setup_walking_scenario, "obstacle": self._setup_obstacle_scenario, "terrain": self._setup_terrain_scenario, "interference": self._setup_interference_scenario } if scenario_type in scenarios: print(f"🎬 设置 {scenario_type} 演示场景...") scenarios[scenario_type]() else: print(f"⚠️ 未知场景类型: {scenario_type},使用综合场景") self._setup_comprehensive_scenario() def _setup_comprehensive_scenario(self): """设置综合演示场景""" self.scenario_timeline = [ [0, 5, "walking", "平地行走"], [5, 10, "obstacle", "动态避障"], [10, 15, "terrain", "斜坡行走"], [15, 20, "terrain", "楼梯攀爬"], [20, 25, "interference", "风力干扰"], [25, 30, "walking", "恢复行走"] ] self.environment_config = { 'obstacles': [ {'position': [2, 0, 0.5], 'radius': 0.3, 'type': 'static'}, {'position': [4, 1, 0.3], 'radius': 0.3, 'type': 'dynamic'}, {'position': [6, -1, 0.4], 'radius': 0.2, 'type': 'moving'} ], 'terrain_sequence': ['flat', 'slope', 'stairs', 'sand', 'flat'], 'wind_patterns': [ {'time': [20, 25], 'force': [50, 0, 0], 'type': 'gust'}, {'time': [25, 30], 'force': [20, 10, 0], 'type': 'steady'} ], 'light_patterns': [ {'time': [22, 24], 'intensity': 0.2, 'type': 'dim'}, {'time': [24, 26], 'intensity': 0.9, 'type': 'bright'} ] } # 添加地形几何体 self._add_terrain_geoms() def _add_terrain_geoms(self): """根据场景添加地形几何体""" # 添加斜坡 slope_height = 0.5 self.robot.model.geom('floor').size = [10, 10, 0.1] # 重置地板 # 添加斜坡几何体 slope_pos = [5, 0, slope_height / 2] slope_size = [5, 10, slope_height / 2] slope_quat = [1, 0, 0, 0] # 四元数 self.robot.model.body_add('world', name='slope') self.robot.model.geom_add( body='slope', name='slope_geom', type='box', size=slope_size, pos=slope_pos, quat=slope_quat, rgba=[0.7, 0.7, 0.9, 1] ) # 添加楼梯 stair_positions = [ [7.0, 0, 0.1], [7.5, 0, 0.3], [8.0, 0, 0.5], [8.5, 0, 0.7], [9.0, 0, 0.9] ] for i, pos in enumerate(stair_positions): self.robot.model.body_add('world', name=f'stair_{i}') self.robot.model.geom_add( body=f'stair_{i}', name=f'stair_geom_{i}', type='box', size=[0.25, 2.0, 0.1], pos=pos, rgba=[0.8, 0.7, 0.6, 1] ) # 重新初始化数据 mujoco.mj_resetData(self.robot.model, self.robot.data) def _setup_walking_scenario(self): """设置行走演示场景""" self.scenario_timeline = [ [0, 10, "walking", "基础行走"], [10, 20, "walking", "快速行走"], [20, 30, "walking", "慢速行走"] ] self.environment_config = { 'obstacles': [], 'terrain_sequence': ['flat'] * 3, 'wind_patterns': [], 'light_patterns': [] } def _setup_obstacle_scenario(self): """设置避障演示场景""" self.scenario_timeline = [ [0, 10, "obstacle", "静态障碍物"], [10, 20, "obstacle", "动态障碍物"], [20, 30, "obstacle", "复杂障碍物"] ] self.environment_config = { 'obstacles': [ {'position': [1, 0, 0.5], 'radius': 0.3, 'type': 'static'}, {'position': [3, 0, 0.3], 'radius': 0.2, 'type': 'dynamic'}, {'position': [5, 0, 0.4], 'radius': 0.25, 'type': 'moving'} ], 'terrain_sequence': ['flat'] * 3, 'wind_patterns': [], 'light_patterns': [] } # 添加障碍物几何体 for i, obs in enumerate(self.environment_config['obstacles']): self.robot.model.body_add('world', name=f'obstacle_{i}') self.robot.model.geom_add( body=f'obstacle_{i}', name=f'obs_geom_{i}', type='sphere', size=[obs['radius']], pos=obs['position'], rgba=[0.9, 0.5, 0.5, 1] if obs['type'] == 'static' else [0.5, 0.7, 0.9, 1] if obs['type'] == 'dynamic' else [0.7, 0.5, 0.9, 1] ) mujoco.mj_resetData(self.robot.model, self.robot.data) def _setup_terrain_scenario(self): """设置地形演示场景""" self.scenario_timeline = [ [0, 6, "terrain", "平地"], [6, 12, "terrain", "斜坡"], [12, 18, "terrain", "楼梯"], [18, 24, "terrain", "沙地"], [24, 30, "terrain", "平地"] ] self.environment_config = { 'obstacles': [], 'terrain_sequence': ['flat', 'slope', 'stairs', 'sand', 'flat'], 'wind_patterns': [], 'light_patterns': [] } self._add_terrain_geoms() def _setup_interference_scenario(self): """设置干扰演示场景""" self.scenario_timeline = [ [0, 10, "walking", "正常行走"], [10, 20, "interference", "风力干扰"], [20, 30, "interference", "光照干扰"] ] self.environment_config = { 'obstacles': [], 'terrain_sequence': ['flat'] * 3, 'wind_patterns': [ {'time': [10, 20], 'force': [80, 0, 0], 'type': 'strong_wind'} ], 'light_patterns': [ {'time': [20, 30], 'intensity': 0.1, 'type': 'low_light'} ] } def get_current_scenario_state(self, current_time: float) -> Dict: """获取当前场景状态""" current_task = "idle" task_description = "待机" # 确定当前任务 for start_time, end_time, task, description in self.scenario_timeline: if start_time <= current_time < end_time: current_task = task task_description = description break # 确定当前地形 terrain_index = min(int(current_time / 6), len(self.environment_config['terrain_sequence']) - 1) current_terrain = self.environment_config['terrain_sequence'][terrain_index] # 计算当前风力 current_wind = np.zeros(3) for wind_pattern in self.environment_config['wind_patterns']: if wind_pattern['time'][0] <= current_time < wind_pattern['time'][1]: current_wind = np.array(wind_pattern['force']) break # 计算当前光照 current_light = 1.0 for light_pattern in self.environment_config['light_patterns']: if light_pattern['time'][0] <= current_time < light_pattern['time'][1]: current_light = light_pattern['intensity'] break return { 'task': current_task, 'description': task_description, 'terrain': current_terrain, 'wind': current_wind, 'light': current_light, 'obstacles': self.environment_config['obstacles'].copy(), 'time': current_time } def update_robot_state(self): """更新机器人状态""" # 模拟机器人行走 if self.get_current_scenario_state(self.current_time)['task'] == 'walking': # 简单的前进运动 self.robot.data.qpos[0] = self.current_time * 0.5 # x位置 self.robot.data.qvel[0] = 0.5 # x速度 # 添加腿部摆动动画 leg_angle = 30 * np.sin(self.current_time * 5) arm_angle = 20 * np.sin(self.current_time * 3) # 左腿关节 left_leg_joint = mujoco.mj_name2id(self.robot.model, mujoco.mjtObj.mjOBJ_JOINT, "left_leg_joint") if left_leg_joint != -1: self.robot.data.qpos[left_leg_joint] = np.deg2rad(leg_angle) # 右腿关节 right_leg_joint = mujoco.mj_name2id(self.robot.model, mujoco.mjtObj.mjOBJ_JOINT, "right_leg_joint") if right_leg_joint != -1: self.robot.data.qpos[right_leg_joint] = np.deg2rad(-leg_angle) # 左臂关节 left_arm_joint = mujoco.mj_name2id(self.robot.model, mujoco.mjtObj.mjOBJ_JOINT, "left_arm_joint") if left_arm_joint != -1: self.robot.data.qpos[left_arm_joint] = np.deg2rad(arm_angle) # 右臂关节 right_arm_joint = mujoco.mj_name2id(self.robot.model, mujoco.mjtObj.mjOBJ_JOINT, "right_arm_joint") if right_arm_joint != -1: self.robot.data.qpos[right_arm_joint] = np.deg2rad(-arm_angle) # 应用风力 wind_force = self.get_current_scenario_state(self.current_time)['wind'] if np.linalg.norm(wind_force) > 0.1: torso_id = mujoco.mj_name2id(self.robot.model, mujoco.mjtObj.mjOBJ_BODY, "torso") if torso_id != -1: self.robot.data.xfrc_applied[torso_id, 0] = wind_force[0] self.robot.data.xfrc_applied[torso_id, 1] = wind_force[1] self.robot.data.xfrc_applied[torso_id, 2] = wind_force[2] # 前向动力学计算 mujoco.mj_forward(self.robot.model, self.robot.data) def change_camera_mode(self): """切换摄像头视角""" self.visualizer.toggle_camera() def record_data(self, current_time): """记录演示数据""" self.demo_data['timestamps'].append(current_time) # 模拟能量消耗 - 根据地形和任务类型变化 energy_factor = 1.0 terrain = self.get_current_scenario_state(current_time)['terrain'] if terrain == 'slope': energy_factor = 1.5 elif terrain == 'stairs': energy_factor = 1.8 elif terrain == 'sand': energy_factor = 2.0 new_energy = self.energy_consumption[-1] + 0.1 * energy_factor self.energy_consumption.append(new_energy) # 模拟性能指标 stability = 1.0 - np.random.uniform(0, 0.1) efficiency = 1.0 - np.random.uniform(0, 0.05) self.performance_metrics.append({ 'stability': max(0.1, stability), 'efficiency': max(0.1, efficiency) }) def save_performance_data(self): """保存性能数据""" filename = f"performance_data_{time.strftime('%Y%m%d_%H%M%S')}.json" try: with open(filename, 'w') as f: json.dump({ 'timestamps': self.demo_data['timestamps'], 'energy_consumption': self.demo_data['energy_consumption'], 'performance_metrics': self.demo_data['performance_metrics'] }, f, indent=2) print(f"✅ 性能数据已保存到 {filename}") return True except Exception as e: print(f"⚠️ 保存性能数据失败: {e}") return False def handle_events(self): """处理GLFW事件""" if not self.visualizer.viewer: return True # 检查窗口是否关闭 if glfw.window_should_close(self.visualizer.viewer.window): return False # 处理键盘事件 for key, action in self.key_actions.items(): if glfw.get_key(self.visualizer.viewer.window, key) == glfw.PRESS: return action return True def run_demo(self): """运行演示""" print(f"🚀 开始演示,持续时间: {self.demo_config['duration']}秒") print("=" * 60) print("控制说明:") print(" 空格键: 暂停/继续") print(" R键: 重置演示") print(" C键: 切换摄像头视角") print(" S键: 保存性能数据") print(" V键: 开始/停止录制视频") print(" ESC: 退出程序") self.is_running = True start_time = time.time() last_frame_time = start_time try: while self.current_time < self.demo_config['duration']: # 处理事件 event_result = self.handle_events() if event_result == False: # 窗口关闭 break elif event_result == "pause": self.paused = not self.paused print(f"⏸️ {'已暂停' if self.paused else '继续运行'}") elif event_result == "reset": start_time = time.time() self.current_time = 0.0 self.demo_data = { 'timestamps': [], 'energy_consumption': [0.0], 'performance_metrics': [{'stability': 1.0, 'efficiency': 1.0}] } print("🔄 演示已重置") elif event_result == "change_camera": self.change_camera_mode() elif event_result == "save_data": self.save_performance_data() elif event_result == "toggle_recording": if self.visualizer.recording: self.visualizer.stop_recording() else: self.visualizer.start_recording() if self.paused: time.sleep(0.1) continue # 更新当前时间 current_real_time = time.time() self.current_time = current_real_time - start_time # 控制帧率 (约30FPS) if current_real_time - last_frame_time < 0.033: continue last_frame_time = current_real_time # 更新机器人状态 self.update_robot_state() # 记录数据 if self.demo_config['record_data']: self.record_data(self.current_time) # 获取当前场景状态 scenario_state = self.get_current_scenario_state(self.current_time) render_state = { 'task': scenario_state['task'], 'description': scenario_state['description'], 'terrain': scenario_state['terrain'], 'time': self.current_time, 'energy': self.energy_consumption[-1] } # 渲染可视化界面 self.visualizer.render(render_state) # 添加场景信息覆盖 self._add_info_overlay(scenario_state) self.is_running = False print("\n✅ 演示完成!") # 自动保存性能数据 if self.demo_config['record_data']: self.save_performance_data() # 停止录制 if self.visualizer.recording: self.visualizer.stop_recording() except Exception as e: self.is_running = False print(f"\n⛔ 演示出错: {e}") traceback.print_exc() # 出错时保存当前数据 if self.demo_config['record_data']: print("⚠️ 尝试保存当前性能数据...") self.save_performance_data() finally: self.visualizer.close() def _add_info_overlay(self, scenario_state): """添加信息覆盖层""" if not self.visualizer.viewer: return # 左上角信息 self.visualizer.viewer.add_overlay( mujoco.mjtGridPos.mjGRID_TOPLEFT, "场景", f"{scenario_state['description']}" ) self.visualizer.viewer.add_overlay( mujoco.mjtGridPos.mjGRID_TOPLEFT, "地形", f"{scenario_state['terrain']}" ) self.visualizer.viewer.add_overlay( mujoco.mjtGridPos.mjGRID_TOPLEFT, "时间", f"{self.current_time:.1f}s / {self.demo_config['duration']:.1f}s" ) self.visualizer.viewer.add_overlay( mujoco.mjtGridPos.mjGRID_TOPLEFT, "能量消耗", f"{self.energy_consumption[-1]:.2f} J" ) self.visualizer.viewer.add_overlay( mujoco.mjtGridPos.mjGRID_TOPLEFT, "控制模式", f"{'AI控制' if self.demo_config['enable_ai'] else '手动控制'}" ) # 右上角状态 status = "运行中" if self.is_running else "已停止" self.visualizer.viewer.add_overlay( mujoco.mjtGridPos.mjGRID_TOPRIGHT, "状态", f"{status}{' (已暂停)' if self.paused else ''}" ) # 录制状态 if self.visualizer.recording: self.visualizer.viewer.add_overlay( mujoco.mjtGridPos.mjGRID_TOPRIGHT, "录制", "⏺️ 录制中..." ) def main(): """主函数""" parser = argparse.ArgumentParser(description='人形机器人仿真演示') parser.add_argument('--scenario', type=str, default='comprehensive', choices=['comprehensive', 'walking', 'obstacle', 'terrain', 'interference'], help='演示场景类型') parser.add_argument('--duration', type=float, default=30.0, help='演示持续时间(秒)') args = parser.parse_args() # 创建演示实例 demo = HumanoidDemo() demo.demo_config['duration'] = args.duration # 设置场景 demo.setup_demo_scenario(args.scenario) # 运行演示 demo.run_demo() if __name__ == "__main__": main()
07-29
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