362 lines
12 KiB
Python
362 lines
12 KiB
Python
#!/usr/bin/env python3
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"""
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ROS1 + MuJoCo VLA 3D 导航 — 全向移动 + 前向扫描 + 避障 + 目标点导航
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圆柱体小车: vx, vy, wz 全向移动
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前向范围扫描: mj_ray 射线检测
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避障: 基于距离的势场/向量场
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"""
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import mujoco
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import mujoco.viewer
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import numpy as np
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import os
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SCENE_PATH = os.path.join(os.path.dirname(__file__), "..", "mujoco_scenes", "cylinder_obstacles.xml")
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# ============ 可配置参数 ============
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NUM_RAYS = 19 # 前向扫描射线数量（-90 ~ +90 deg）
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RAY_ANGLE_SPAN = np.pi # 扫描角度跨度 (rad)
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OBSTACLE_THRESHOLD = 0.45 # 障碍判定距离 (m)
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SAFE_DISTANCE = 0.65 # 安全距离
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GOAL_THRESHOLD = 0.2 # 到达目标判定距离
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MAX_LINEAR = 0.6 # 最大线速度
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MAX_ANGULAR = 0.8 # 最大角速度
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K_ATTRACT = 1.0 # 目标吸引力
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K_REPEL = 0.6 # 障碍排斥力
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K_YAW_GOAL = 0.8 # 目标对准角速度增益
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MIN_LINEAR = 0.18 # 最小线速度，避免卡住
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STUCK_THRESH = 0.03 # 位移阈值，低于此认为卡住 (m)
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STUCK_STEPS = 100 # 连续卡住步数触发绕行
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GOAL_POINTS = [] # 初始无目标
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GOAL_AHEAD_DIST = 2.0 # G 键：目标 = 车头前方 N 米
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GOAL_MAX_R = 5.0 # 目标点边界：距离原点不超过此值 (m)
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def _clamp_goal(gx, gy):
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"""目标点边界：限制在距离原点 GOAL_MAX_R 以内"""
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r = np.sqrt(gx * gx + gy * gy) + 1e-8
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if r > GOAL_MAX_R:
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scale = GOAL_MAX_R / r
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return (float(gx * scale), float(gy * scale))
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return (float(gx), float(gy))
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def get_agent_state(data, body_id):
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"""获取小车位姿: x, y, yaw"""
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qpos = data.qpos
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x, y = qpos[0], qpos[1]
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yaw = qpos[2]
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return x, y, yaw
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def forward_ray_scan(model, data, body_id, site_id, num_rays, angle_span):
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"""
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前向范围扫描: 在车体前方扇形区域内发射射线
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返回: (距离数组, 角度数组)
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"""
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site_xpos = data.site_xpos[site_id].copy()
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xmat = np.array(data.xmat[body_id]).reshape(3, 3)
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forward = xmat[:, 0] # 前向 (body +X)
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angles = np.linspace(-angle_span / 2, angle_span / 2, num_rays)
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distances = np.full(num_rays, 10.0)
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for i, theta in enumerate(angles):
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c, s = np.cos(theta), np.sin(theta)
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ray_dir = c * forward + s * xmat[:, 1] # 绕 body Z 旋转
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ray_dir = ray_dir / (np.linalg.norm(ray_dir) + 1e-8)
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geomid = np.array([-1], dtype=np.int32)
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d = mujoco.mj_ray(model, data, site_xpos, ray_dir, None, 1, body_id, geomid)
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if d >= 0:
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distances[i] = d
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return distances, angles
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def obstacle_avoidance_vel(distances, angles):
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"""
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避障速度 + 绕行分量。被挡时朝开阔方向加速，而非只排斥
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返回: (vx_avoid, vy_avoid, wz_avoid)
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"""
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min_d = np.min(distances)
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if min_d > OBSTACLE_THRESHOLD:
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return 0.0, 0.0, 0.0
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vx, vy, wz = 0.0, 0.0, 0.0
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safe_sector = np.argmax(distances)
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best_theta = angles[safe_sector]
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for d, theta in zip(distances, angles):
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if d < OBSTACLE_THRESHOLD:
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ratio = 1.0 - d / OBSTACLE_THRESHOLD
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strength = K_REPEL * (ratio ** 2)
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vx -= strength * np.cos(theta)
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vy -= strength * np.sin(theta)
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elif d < SAFE_DISTANCE:
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ratio = (SAFE_DISTANCE - d) / (SAFE_DISTANCE - OBSTACLE_THRESHOLD)
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strength = K_REPEL * 0.15 * ratio
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wz += strength * np.clip(best_theta - theta, -1, 1)
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# 绕行：朝最开阔方向加正向速度，避免只退不绕
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bypass = 0.35
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vx += bypass * np.cos(best_theta)
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vy += bypass * np.sin(best_theta)
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wz += 0.4 * best_theta
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return vx, vy, wz
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def goal_attraction_vel(x, y, yaw, goal_x, goal_y):
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"""
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目标吸引力: 在车体坐标系下计算朝目标的 vx, vy, wz
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优先保持线速度，角速度仅用于微调朝向
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"""
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dx = goal_x - x
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dy = goal_y - y
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dist = np.sqrt(dx * dx + dy * dy) + 1e-6
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# 世界系下期望方向
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gx_w = dx / dist
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gy_w = dy / dist
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# 转到车体系
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c, s = np.cos(-yaw), np.sin(-yaw)
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gx_b = c * gx_w - s * gy_w
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gy_b = s * gx_w + c * gy_w
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# 线速度：朝目标，随距离平滑
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scale = np.tanh(dist) * 0.8 + 0.2
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vx = K_ATTRACT * scale * gx_b
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vy = K_ATTRACT * scale * gy_b
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# 角速度：弱增益，避免只转不走
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target_yaw = np.arctan2(dy, dx)
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yaw_err = np.arctan2(np.sin(target_yaw - yaw), np.cos(target_yaw - yaw))
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wz = K_YAW_GOAL * np.tanh(yaw_err)
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return vx, vy, wz
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def blend_and_clamp(vx_a, vy_a, wz_a, vx_g, vy_g, wz_g, dist_to_goal, min_d, stuck):
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"""融合避障与目标速度，限幅，卡住时强制最小速度"""
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vx = vx_a + vx_g
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vy = vy_a + vy_g
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wz = wz_a + wz_g
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lin = np.sqrt(vx * vx + vy * vy)
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# 未到目标且线速度过小：强制最小速度
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if dist_to_goal > GOAL_THRESHOLD and lin < MIN_LINEAR:
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if lin > 1e-6:
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vx *= MIN_LINEAR / lin
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vy *= MIN_LINEAR / lin
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else:
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# 融合后接近零：优先朝目标，被挡时朝避障方向
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ax, ay = vx_g + vx_a, vy_g + vy_a
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anorm = np.sqrt(ax * ax + ay * ay) + 1e-8
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vx = MIN_LINEAR * ax / anorm
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vy = MIN_LINEAR * ay / anorm
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lin = np.sqrt(vx * vx + vy * vy)
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if lin > MAX_LINEAR:
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scale = MAX_LINEAR / lin
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vx *= scale
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vy *= scale
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wz = np.clip(wz, -MAX_ANGULAR, MAX_ANGULAR)
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return vx, vy, wz
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class VelocityFilter:
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"""EMA 滤波 + 速率限制，平滑控制输出"""
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def __init__(self, alpha=0.75, max_dv=0.15, max_dw=0.2):
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self.alpha = alpha # 滤波系数，越大越平滑
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self.max_dv = max_dv # 每步最大线速度变化
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self.max_dw = max_dw # 每步最大角速度变化
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self.vx, self.vy, self.wz = 0.0, 0.0, 0.0
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def update(self, vx_cmd, vy_cmd, wz_cmd):
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# 1. EMA 滤波
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vx_f = self.alpha * self.vx + (1 - self.alpha) * vx_cmd
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vy_f = self.alpha * self.vy + (1 - self.alpha) * vy_cmd
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wz_f = self.alpha * self.wz + (1 - self.alpha) * wz_cmd
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# 2. 速率限制
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dvx = np.clip(vx_f - self.vx, -self.max_dv, self.max_dv)
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dvy = np.clip(vy_f - self.vy, -self.max_dv, self.max_dv)
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dwz = np.clip(wz_f - self.wz, -self.max_dw, self.max_dw)
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self.vx += dvx
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self.vy += dvy
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self.wz += dwz
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return self.vx, self.vy, self.wz
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def main():
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model = mujoco.MjModel.from_xml_path(SCENE_PATH)
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data = mujoco.MjData(model)
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body_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_BODY, "cylinder_agent")
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site_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_SITE, "ray_origin")
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# 执行器索引
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act_ids = [
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mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_ACTUATOR, "vel_x"),
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mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_ACTUATOR, "vel_y"),
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mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_ACTUATOR, "vel_yaw"),
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]
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goals = GOAL_POINTS.copy()
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goal_idx = 0 if goals else -1
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vel_filter = VelocityFilter(alpha=0.70, max_dv=0.18, max_dw=0.2)
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last_x, last_y = 0.0, 0.0
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stuck_cnt = 0
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goal_joint_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_JOINT, "goal_joint")
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goal_qposadr = model.jnt_qposadr[goal_joint_id]
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data.qpos[goal_qposadr : goal_qposadr + 3] = [-99, -99, 0.1] # 初始隐藏
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print("=" * 55)
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print("VLA 3D 导航 - 全向移动 + 前向扫描 + 避障")
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print("=" * 55)
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print("G: 车头前方 {:.0f}m | C: 相机视线 | 目标边界 R<{:.0f}m".format(GOAL_AHEAD_DIST, GOAL_MAX_R))
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print("ESC 退出")
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print("=" * 55)
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add_goal_ahead = [False]
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add_goal_camera = [False]
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def key_cb(keycode):
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if keycode == 71: # G
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add_goal_ahead[0] = True
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elif keycode == 67: # C: 相机视线与地面交点
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add_goal_camera[0] = True
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def _goal_from_camera(viewer_handle):
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"""相机视线与 z=0 地面交点（旋转相机后按 C）"""
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cam = viewer_handle.cam
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lookat = np.array(cam.lookat)
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dist = float(cam.distance)
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az = np.deg2rad(float(cam.azimuth))
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el = np.deg2rad(float(cam.elevation))
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# 相机位置 = lookat - dist * 前向单位向量
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fx = np.cos(el) * np.sin(az)
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fy = -np.cos(el) * np.cos(az)
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fz = np.sin(el)
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cam_pos = lookat - dist * np.array([fx, fy, fz])
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# 射线与 z=0 交点: cam_pos + t*[fx,fy,fz], 令 z=0
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if abs(fz) < 1e-6:
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return None
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t = -cam_pos[2] / fz
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if t < 0:
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return None
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pt = cam_pos + t * np.array([fx, fy, fz])
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return (float(pt[0]), float(pt[1]))
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def control_callback():
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nonlocal goal_idx, last_x, last_y, stuck_cnt
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if add_goal_ahead[0]:
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add_goal_ahead[0] = False
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x, y, yaw = get_agent_state(data, body_id)
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gx = x + GOAL_AHEAD_DIST * np.cos(yaw)
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gy = y + GOAL_AHEAD_DIST * np.sin(yaw)
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gx, gy = _clamp_goal(gx, gy)
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goals.clear()
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goals.append((gx, gy))
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goal_idx = 0
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vel_filter.vx = vel_filter.vy = vel_filter.wz = 0.0
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print(" 目标: ({:.2f}, {:.2f}) [G=车头前方]".format(gx, gy))
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elif add_goal_camera[0]:
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add_goal_camera[0] = False
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g = _goal_from_camera(viewer)
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if g is not None:
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g = _clamp_goal(g[0], g[1])
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goals.clear()
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goals.append(g)
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goal_idx = 0
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vel_filter.vx = vel_filter.vy = vel_filter.wz = 0.0
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print(" 目标: ({:.2f}, {:.2f}) [C=相机视线]".format(g[0], g[1]))
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else:
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print(" [C] 相机未指向地面，请调整视角后重试")
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if goal_idx < 0 or not goals:
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data.ctrl[act_ids[0]] = 0
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data.ctrl[act_ids[1]] = 0
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data.ctrl[act_ids[2]] = 0
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vel_filter.vx = vel_filter.vy = vel_filter.wz = 0.0
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data.qpos[goal_qposadr : goal_qposadr + 3] = [-99, -99, 0.1] # 藏起标记
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return
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x, y, yaw = get_agent_state(data, body_id)
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goal_x, goal_y = goals[goal_idx]
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dist_to_goal = np.sqrt((goal_x - x) ** 2 + (goal_y - y) ** 2)
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if dist_to_goal < GOAL_THRESHOLD:
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goals.clear()
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goal_idx = -1
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vel_filter.vx = vel_filter.vy = vel_filter.wz = 0.0
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print(" 已到达，停止。按 G 设置新目标")
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data.ctrl[act_ids[0]] = 0
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data.ctrl[act_ids[1]] = 0
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data.ctrl[act_ids[2]] = 0
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data.qpos[goal_qposadr : goal_qposadr + 3] = [-99, -99, 0.1]
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return
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# 更新目标点标记位置
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data.qpos[goal_qposadr] = goal_x
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data.qpos[goal_qposadr + 1] = goal_y
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data.qpos[goal_qposadr + 2] = 0.15
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# 1. 前向范围扫描
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distances, angles = forward_ray_scan(
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model, data, body_id, site_id, NUM_RAYS, RAY_ANGLE_SPAN
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)
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min_d = np.min(distances)
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# 2. 卡住检测
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moved = np.sqrt((x - last_x) ** 2 + (y - last_y) ** 2)
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if moved < STUCK_THRESH and dist_to_goal > GOAL_THRESHOLD:
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stuck_cnt += 1
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else:
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stuck_cnt = 0
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last_x, last_y = x, y
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stuck = stuck_cnt > STUCK_STEPS
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# 3. 避障速度（含绕行分量）
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vx_a, vy_a, wz_a = obstacle_avoidance_vel(distances, angles)
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# 4. 目标吸引速度
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vx_g, vy_g, wz_g = goal_attraction_vel(x, y, yaw, goal_x, goal_y)
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# 5. 融合、最小速度、限幅
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vx, vy, wz = blend_and_clamp(
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vx_a, vy_a, wz_a, vx_g, vy_g, wz_g, dist_to_goal, min_d, stuck
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)
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# 6. 滤波 + 速率限制（卡住时放宽）
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max_dv = 0.25 if stuck else 0.18
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max_dw = 0.28 if stuck else 0.2
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vel_filter.max_dv = max_dv
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vel_filter.max_dw = max_dw
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vx, vy, wz = vel_filter.update(vx, vy, wz)
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# 车体系 -> 世界系（slide_x/y 沿世界轴）
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c, s = np.cos(yaw), np.sin(yaw)
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vx_w = vx * c - vy * s
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vy_w = vx * s + vy * c
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data.ctrl[act_ids[0]] = vx_w
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data.ctrl[act_ids[1]] = vy_w
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data.ctrl[act_ids[2]] = wz
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with mujoco.viewer.launch_passive(model, data, key_callback=key_cb) as viewer:
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while viewer.is_running():
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mujoco.mj_forward(model, data)
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control_callback()
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mujoco.mj_step(model, data)
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viewer.sync()
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if __name__ == "__main__":
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main()
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