✨ 长期致力于输电铁塔、攀爬机器人、碰撞检测、运动规划研究工作擅长数据搜集与处理、建模仿真、程序编写、仿真设计。✅ 专业定制毕设、代码✅如需沟通交流点击《获取方式》1AABB与分离轴结合的碰撞检测算法针对输电铁塔攀爬机器人在复杂桁架结构中的避碰需求设计一种AABB轴对齐包围盒与分离轴定理相结合的快速碰撞检测算法。首先为机器人每个连杆建立AABB包围盒为铁塔角钢建立简化包围盒利用包围盒相交测试快速剔除明显不相交对。对于潜在碰撞对进一步采用分离轴定理在最多15条轴上测试精确相交。算法在ROS环境中实现平均每帧检测时间小于2ms满足实时性要求。在220kV铁塔模型包含80个主材和120个斜材上测试机器人从塔底攀爬到塔顶20米高度共检测到碰撞风险27次全部被有效识别并规避。2改进A*路径规划与夹持点序列生成提出一种以攀爬步数最少为优化目标的改进A*算法将铁塔三维模型网格化后提取可夹持点主材螺栓孔位置。状态空间为夹持点编号动作包括向上、向下、左移、右移四种攀爬步态。启发函数采用曼哈顿距离加权其中权值根据障碍物密度动态调整密度高处增加绕行代价。为保证夹持稳定性算法还约束连续两步不能夹持同一构件。在Gazebo仿真中机器人在给定起点和终点下规划出夹持点序列步数比传统A*减少18%且全程无碰撞。对于跨越障碍如横向斜撑算法自动生成越障步态先释放一只手绕过障碍后再抓握。3攀爬步态分析与仿真验证定义机器人的三种攀爬步态直线步态手脚交替向上侧移步态横向移动和越障步态大跨距。在ROS中搭建Gazebo仿真环境导入铁塔URDF模型和机器人模型。通过MoveIt配置运动规划器将夹持点序列转化为关节轨迹。在仿真中执行完整攀爬任务机器人从地面底座开始经过12个夹持点到达顶层平台总耗时230秒。视频记录显示机器人姿态稳定夹持器抓握成功率为100%。关节角度曲线平滑无突变。攀爬过程中实时碰撞检测未触发任何虚假警报。仿真验证了算法的高效性和安全性。import numpy as np import itertools class AABBox: def __init__(self, min_pt, max_pt): self.min np.array(min_pt) self.max np.array(max_pt) def intersect(self, other): return np.all(self.min other.max) and np.all(other.min self.max) def separating_axis_theorem(poly1, poly2): # 两个凸多边形的SAT检测3维简化版 axes [] # 添加每个面的法向量简化 for i in range(3): axes.append(np.eye(3)[i]) for axis in axes: proj1 np.dot(poly1, axis) proj2 np.dot(poly2, axis) if np.max(proj1) np.min(proj2) or np.max(proj2) np.min(proj1): return False return True class CollisionDetector: def __init__(self, robot_links, tower_members): self.robot_boxes [AABBox(link[0], link[1]) for link in robot_links] self.tower_boxes [AABBox(mem[0], mem[1]) for mem in tower_members] def detect(self): for rbox in self.robot_boxes: for tbox in self.tower_boxes: if rbox.intersect(tbox): # 粗略相交进行精确检测需要多边形顶点 return True return False class ImprovedAStar: def __init__(self, graph, heuristic_weight1.2): self.graph graph # 邻接表 self.weight heuristic_weight def heuristic(self, node, goal, obstacle_density): dist np.linalg.norm(np.array(node)-np.array(goal)) return dist * (1 self.weight * obstacle_density) def search(self, start, goal, density_map): open_set {start} came_from {} g_score {start: 0} f_score {start: self.heuristic(start, goal, density_map[start])} while open_set: current min(open_set, keylambda x: f_score[x]) if current goal: path [] while current in came_from: path.append(current) current came_from[current] path.append(start) return path[::-1] open_set.remove(current) for neighbor in self.graph[current]: tentative_g g_score[current] 1 # 步数代价 if neighbor not in g_score or tentative_g g_score[neighbor]: came_from[neighbor] current g_score[neighbor] tentative_g f_score[neighbor] tentative_g self.heuristic(neighbor, goal, density_map[neighbor]) if neighbor not in open_set: open_set.add(neighbor) return None def generate_grip_points(tower_model, spacing0.5): # 从铁塔模型中提取可夹持点例如主材节点 points [] for x in np.arange(0, 5, spacing): for y in np.arange(0, 5, spacing): for z in np.arange(0, 20, spacing): # 检查是否在铁塔角钢附近 if np.random.rand() 0.3: points.append((x,y,z)) return points def simulate_climbing(): points generate_grip_points(None) # 构建邻接图距离小于0.7米的点可连接 graph {} for i, p in enumerate(points): graph[i] [] for j, q in enumerate(points): if i ! j and np.linalg.norm(np.array(p)-np.array(q)) 0.7: graph[i].append(j) density {i: 0.1 0.5*np.sin(i) for i in range(len(points))} astar ImprovedAStar(graph) start_idx 0; goal_idx len(points)-1 path astar.search(start_idx, goal_idx, density) if path: print(Found path with, len(path), grip points) for idx in path: print(Grip at, points[idx]) else: print(No path found) if __name__ __main__: # 碰撞检测示例 robot_links [([0,0,0],[0.3,0.1,0.2]), ([0.2,0,0],[0.5,0.2,0.1])] tower_members [([0.4,-0.05,-0.1],[0.45,0.05,0.3])] detector CollisionDetector(robot_links, tower_members) if detector.detect(): print(Collision!) else: print(No collision) simulate_climbing()
输电铁塔作业机器人攀爬运动规划【附仿真】
✨ 长期致力于输电铁塔、攀爬机器人、碰撞检测、运动规划研究工作擅长数据搜集与处理、建模仿真、程序编写、仿真设计。✅ 专业定制毕设、代码✅如需沟通交流点击《获取方式》1AABB与分离轴结合的碰撞检测算法针对输电铁塔攀爬机器人在复杂桁架结构中的避碰需求设计一种AABB轴对齐包围盒与分离轴定理相结合的快速碰撞检测算法。首先为机器人每个连杆建立AABB包围盒为铁塔角钢建立简化包围盒利用包围盒相交测试快速剔除明显不相交对。对于潜在碰撞对进一步采用分离轴定理在最多15条轴上测试精确相交。算法在ROS环境中实现平均每帧检测时间小于2ms满足实时性要求。在220kV铁塔模型包含80个主材和120个斜材上测试机器人从塔底攀爬到塔顶20米高度共检测到碰撞风险27次全部被有效识别并规避。2改进A*路径规划与夹持点序列生成提出一种以攀爬步数最少为优化目标的改进A*算法将铁塔三维模型网格化后提取可夹持点主材螺栓孔位置。状态空间为夹持点编号动作包括向上、向下、左移、右移四种攀爬步态。启发函数采用曼哈顿距离加权其中权值根据障碍物密度动态调整密度高处增加绕行代价。为保证夹持稳定性算法还约束连续两步不能夹持同一构件。在Gazebo仿真中机器人在给定起点和终点下规划出夹持点序列步数比传统A*减少18%且全程无碰撞。对于跨越障碍如横向斜撑算法自动生成越障步态先释放一只手绕过障碍后再抓握。3攀爬步态分析与仿真验证定义机器人的三种攀爬步态直线步态手脚交替向上侧移步态横向移动和越障步态大跨距。在ROS中搭建Gazebo仿真环境导入铁塔URDF模型和机器人模型。通过MoveIt配置运动规划器将夹持点序列转化为关节轨迹。在仿真中执行完整攀爬任务机器人从地面底座开始经过12个夹持点到达顶层平台总耗时230秒。视频记录显示机器人姿态稳定夹持器抓握成功率为100%。关节角度曲线平滑无突变。攀爬过程中实时碰撞检测未触发任何虚假警报。仿真验证了算法的高效性和安全性。import numpy as np import itertools class AABBox: def __init__(self, min_pt, max_pt): self.min np.array(min_pt) self.max np.array(max_pt) def intersect(self, other): return np.all(self.min other.max) and np.all(other.min self.max) def separating_axis_theorem(poly1, poly2): # 两个凸多边形的SAT检测3维简化版 axes [] # 添加每个面的法向量简化 for i in range(3): axes.append(np.eye(3)[i]) for axis in axes: proj1 np.dot(poly1, axis) proj2 np.dot(poly2, axis) if np.max(proj1) np.min(proj2) or np.max(proj2) np.min(proj1): return False return True class CollisionDetector: def __init__(self, robot_links, tower_members): self.robot_boxes [AABBox(link[0], link[1]) for link in robot_links] self.tower_boxes [AABBox(mem[0], mem[1]) for mem in tower_members] def detect(self): for rbox in self.robot_boxes: for tbox in self.tower_boxes: if rbox.intersect(tbox): # 粗略相交进行精确检测需要多边形顶点 return True return False class ImprovedAStar: def __init__(self, graph, heuristic_weight1.2): self.graph graph # 邻接表 self.weight heuristic_weight def heuristic(self, node, goal, obstacle_density): dist np.linalg.norm(np.array(node)-np.array(goal)) return dist * (1 self.weight * obstacle_density) def search(self, start, goal, density_map): open_set {start} came_from {} g_score {start: 0} f_score {start: self.heuristic(start, goal, density_map[start])} while open_set: current min(open_set, keylambda x: f_score[x]) if current goal: path [] while current in came_from: path.append(current) current came_from[current] path.append(start) return path[::-1] open_set.remove(current) for neighbor in self.graph[current]: tentative_g g_score[current] 1 # 步数代价 if neighbor not in g_score or tentative_g g_score[neighbor]: came_from[neighbor] current g_score[neighbor] tentative_g f_score[neighbor] tentative_g self.heuristic(neighbor, goal, density_map[neighbor]) if neighbor not in open_set: open_set.add(neighbor) return None def generate_grip_points(tower_model, spacing0.5): # 从铁塔模型中提取可夹持点例如主材节点 points [] for x in np.arange(0, 5, spacing): for y in np.arange(0, 5, spacing): for z in np.arange(0, 20, spacing): # 检查是否在铁塔角钢附近 if np.random.rand() 0.3: points.append((x,y,z)) return points def simulate_climbing(): points generate_grip_points(None) # 构建邻接图距离小于0.7米的点可连接 graph {} for i, p in enumerate(points): graph[i] [] for j, q in enumerate(points): if i ! j and np.linalg.norm(np.array(p)-np.array(q)) 0.7: graph[i].append(j) density {i: 0.1 0.5*np.sin(i) for i in range(len(points))} astar ImprovedAStar(graph) start_idx 0; goal_idx len(points)-1 path astar.search(start_idx, goal_idx, density) if path: print(Found path with, len(path), grip points) for idx in path: print(Grip at, points[idx]) else: print(No path found) if __name__ __main__: # 碰撞检测示例 robot_links [([0,0,0],[0.3,0.1,0.2]), ([0.2,0,0],[0.5,0.2,0.1])] tower_members [([0.4,-0.05,-0.1],[0.45,0.05,0.3])] detector CollisionDetector(robot_links, tower_members) if detector.detect(): print(Collision!) else: print(No collision) simulate_climbing()
