release: v0.1 initial version
This commit is contained in:
lovez
@@ -1,197 +1,303 @@
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#!/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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MuJoCo 全向小车导航（前向射线扫描 + 势场避障 + 目标点导航）
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- vx, vy, wz 全向移动（车体系输出 -> 世界系执行器）
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- mj_ray 扇形前向扫描
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- 避障：排斥 + 朝开阔方向绕行
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- 卡住检测：连续位移太小 -> 强制脱困策略
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"""
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from __future__ import annotations
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import os
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from dataclasses import dataclass
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from typing import Optional, Tuple, List
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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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# ===================== 参数配置 =====================
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@dataclass
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class NavCfg:
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# ray scan
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num_rays: int = 19
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ray_angle_span: float = np.pi # -span/2 ~ +span/2
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# distances
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obstacle_threshold: float = 0.45
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safe_distance: float = 0.65
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goal_threshold: float = 0.20
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# limits
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max_linear: float = 0.60
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max_angular: float = 0.80
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min_linear: float = 0.18 # 防止“只转不走”或输出趋零卡死
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# gains
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k_attract: float = 1.0
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k_repel: float = 0.6
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k_yaw_goal: float = 0.8
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# stuck detect
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stuck_thresh: float = 0.03
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stuck_steps: int = 100
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# goal set
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goal_ahead_dist: float = 2.0
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goal_max_r: float = 5.0
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# output smoothing
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ema_alpha: float = 0.70
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max_dv: float = 0.18
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max_dw: float = 0.20
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max_dv_stuck: float = 0.25
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max_dw_stuck: float = 0.28
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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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CFG = NavCfg()
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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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# ===================== 工具函数 =====================
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def clamp_goal(gx: float, gy: float, max_r: float) -> Tuple[float, float]:
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r = float(np.hypot(gx, gy)) + 1e-9
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if r > max_r:
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s = max_r / r
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return float(gx * s), float(gy * s)
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return float(gx), float(gy)
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def wrap_pi(a: float) -> float:
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# 更稳定的 [-pi, pi]
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return float(np.arctan2(np.sin(a), np.cos(a)))
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def get_agent_state(data: mujoco.MjData) -> Tuple[float, float, float]:
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# 假设 qpos: x, y, yaw 在 0,1,2
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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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return float(qpos[0]), float(qpos[1]), float(qpos[2])
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def forward_ray_scan(model, data, body_id, site_id, num_rays, angle_span):
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def goal_from_camera(viewer_handle) -> Optional[Tuple[float, float]]:
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"""相机视线与 z=0 平面交点（旋转相机后按 C）。"""
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cam = viewer_handle.cam
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lookat = np.array(cam.lookat, dtype=float)
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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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# MuJoCo viewer 的相机方向约定：这里延续你原实现
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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], dtype=float)
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if abs(fz) < 1e-8:
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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], dtype=float)
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return float(pt[0]), float(pt[1])
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# ===================== 射线扫描（预计算优化） =====================
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class RayScanner:
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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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预计算扇形扫描的 body-frame 方向（基于 forward=[1,0,0], left=[0,1,0]），
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每帧用 body rotation (xmat) 转到 world，减少每帧 cos/sin + 临时数组。
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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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def __init__(self, num_rays: int, angle_span: float):
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self.num_rays = int(num_rays)
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self.angle_span = float(angle_span)
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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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self.angles = np.linspace(-self.angle_span / 2, self.angle_span / 2, self.num_rays)
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c = np.cos(self.angles)
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s = np.sin(self.angles)
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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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# body-frame ray directions: dir_b = c*[1,0,0] + s*[0,1,0]
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# shape (N,3)
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self.ray_dirs_body = np.stack([c, s, np.zeros_like(c)], axis=1).astype(np.float64)
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return distances, angles
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# buffers to avoid per-step alloc
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self.distances = np.full(self.num_rays, 10.0, dtype=np.float64)
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self._geomid = np.array([-1], dtype=np.int32)
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def scan(
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self,
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model: mujoco.MjModel,
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data: mujoco.MjData,
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body_id: int,
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site_id: int,
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) -> Tuple[np.ndarray, np.ndarray]:
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site_xpos = data.site_xpos[site_id] # view, no copy
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xmat = np.array(data.xmat[body_id], dtype=np.float64).reshape(3, 3)
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# world dirs: dir_w = xmat @ dir_b
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# shape (N,3)
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ray_dirs_world = (xmat @ self.ray_dirs_body.T).T
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# normalize (理论上已单位，但数值上更稳一点)
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norms = np.linalg.norm(ray_dirs_world, axis=1) + 1e-9
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ray_dirs_world /= norms[:, None]
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self.distances.fill(10.0)
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for i in range(self.num_rays):
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d = mujoco.mj_ray(
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model, data,
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site_xpos, ray_dirs_world[i],
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None, 1, body_id, self._geomid
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)
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if d >= 0:
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self.distances[i] = float(d)
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return self.distances, self.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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# ===================== 速度融合与滤波 =====================
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class VelocityFilter:
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"""EMA + 速率限制，输出更平滑，避免 ctrl 抖动。"""
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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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def __init__(self, alpha: float, max_dv: float, max_dw: float):
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self.alpha = float(alpha)
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self.max_dv = float(max_dv)
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self.max_dw = float(max_dw)
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self.vx = 0.0
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self.vy = 0.0
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self.wz = 0.0
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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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def reset(self):
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self.vx = self.vy = self.wz = 0.0
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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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def update(self, vx_cmd: float, vy_cmd: float, wz_cmd: float) -> Tuple[float, float, float]:
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vx_f = self.alpha * self.vx + (1.0 - self.alpha) * vx_cmd
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vy_f = self.alpha * self.vy + (1.0 - self.alpha) * vy_cmd
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wz_f = self.alpha * self.wz + (1.0 - self.alpha) * wz_cmd
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dvx = float(np.clip(vx_f - self.vx, -self.max_dv, self.max_dv))
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dvy = float(np.clip(vy_f - self.vy, -self.max_dv, self.max_dv))
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dwz = float(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 goal_attraction_vel(x: float, y: float, yaw: float, goal_x: float, goal_y: float, cfg: NavCfg):
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dx = goal_x - x
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dy = goal_y - y
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dist = float(np.hypot(dx, dy)) + 1e-6
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# 世界系单位方向
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gx_w = dx / dist
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gy_w = dy / dist
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# 转车体系（只关心 yaw）
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c = np.cos(-yaw)
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s = 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（随距离平滑）
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scale = float(np.tanh(dist) * 0.8 + 0.2)
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vx = cfg.k_attract * scale * gx_b
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vy = cfg.k_attract * scale * gy_b
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# 角速度（弱对准）
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target_yaw = float(np.arctan2(dy, dx))
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yaw_err = wrap_pi(target_yaw - yaw)
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wz = cfg.k_yaw_goal * float(np.tanh(yaw_err))
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return vx, vy, wz
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def obstacle_avoidance_vel(distances: np.ndarray, angles: np.ndarray, cfg: NavCfg):
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min_d = float(np.min(distances))
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if min_d > cfg.obstacle_threshold:
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return 0.0, 0.0, 0.0, min_d
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# 最开阔方向
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safe_i = int(np.argmax(distances))
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best_theta = float(angles[safe_i])
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vx = 0.0
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vy = 0.0
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wz = 0.0
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# 排斥 + 轻微“扭转”朝开阔方向
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# （这里保持你原逻辑，但数值上更稳一点）
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for d, th in zip(distances, angles):
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d = float(d)
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th = float(th)
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if d < cfg.obstacle_threshold:
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ratio = 1.0 - d / cfg.obstacle_threshold
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strength = cfg.k_repel * (ratio * ratio)
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vx -= strength * np.cos(th)
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vy -= strength * np.sin(th)
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elif d < cfg.safe_distance:
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ratio = (cfg.safe_distance - d) / (cfg.safe_distance - cfg.obstacle_threshold)
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strength = cfg.k_repel * 0.15 * ratio
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wz += strength * float(np.clip(best_theta - th, -1.0, 1.0))
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# 绕行：朝最开阔方向“推一把”
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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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return vx, vy, wz, min_d
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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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def blend_and_clamp(
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vx_a: float, vy_a: float, wz_a: float,
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vx_g: float, vy_g: float, wz_g: float,
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dist_to_goal: float,
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stuck: bool,
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cfg: NavCfg
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):
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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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lin = float(np.hypot(vx, vy))
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# 强制最小速度：未到目标且输出太小
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if dist_to_goal > cfg.goal_threshold and lin < cfg.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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s = cfg.min_linear / lin
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vx *= s
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vy *= s
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else:
|
||||
# 融合后接近零：优先朝目标，被挡时朝避障方向
|
||||
ax, ay = vx_g + vx_a, vy_g + vy_a
|
||||
anorm = np.sqrt(ax * ax + ay * ay) + 1e-8
|
||||
vx = MIN_LINEAR * ax / anorm
|
||||
vy = MIN_LINEAR * ay / anorm
|
||||
lin = np.sqrt(vx * vx + vy * vy)
|
||||
if lin > MAX_LINEAR:
|
||||
scale = MAX_LINEAR / lin
|
||||
vx *= scale
|
||||
vy *= scale
|
||||
wz = np.clip(wz, -MAX_ANGULAR, MAX_ANGULAR)
|
||||
# 彻底趋零时：优先用目标方向（若也为零就不动）
|
||||
ax = vx_g + vx_a
|
||||
ay = vy_g + vy_a
|
||||
an = float(np.hypot(ax, ay)) + 1e-9
|
||||
vx = cfg.min_linear * ax / an
|
||||
vy = cfg.min_linear * ay / an
|
||||
|
||||
# 卡住脱困：额外加一点“侧向+偏航”，更容易摆脱夹角/凹障碍
|
||||
if stuck:
|
||||
# 让它更愿意横移并转一点（避免只顶着障碍）
|
||||
vx *= 0.85
|
||||
vy = vy * 1.15 + 0.06 * np.sign(vy if abs(vy) > 1e-3 else 1.0)
|
||||
wz += 0.10 * np.sign(wz if abs(wz) > 1e-3 else 1.0)
|
||||
|
||||
# 限幅
|
||||
lin = float(np.hypot(vx, vy))
|
||||
if lin > cfg.max_linear:
|
||||
s = cfg.max_linear / lin
|
||||
vx *= s
|
||||
vy *= s
|
||||
wz = float(np.clip(wz, -cfg.max_angular, cfg.max_angular))
|
||||
return vx, vy, wz
|
||||
|
||||
|
||||
class VelocityFilter:
|
||||
"""EMA 滤波 + 速率限制，平滑控制输出"""
|
||||
|
||||
def __init__(self, alpha=0.75, max_dv=0.15, max_dw=0.2):
|
||||
self.alpha = alpha # 滤波系数，越大越平滑
|
||||
self.max_dv = max_dv # 每步最大线速度变化
|
||||
self.max_dw = max_dw # 每步最大角速度变化
|
||||
self.vx, self.vy, self.wz = 0.0, 0.0, 0.0
|
||||
|
||||
def update(self, vx_cmd, vy_cmd, wz_cmd):
|
||||
# 1. EMA 滤波
|
||||
vx_f = self.alpha * self.vx + (1 - self.alpha) * vx_cmd
|
||||
vy_f = self.alpha * self.vy + (1 - self.alpha) * vy_cmd
|
||||
wz_f = self.alpha * self.wz + (1 - self.alpha) * wz_cmd
|
||||
|
||||
# 2. 速率限制
|
||||
dvx = np.clip(vx_f - self.vx, -self.max_dv, self.max_dv)
|
||||
dvy = np.clip(vy_f - self.vy, -self.max_dv, self.max_dv)
|
||||
dwz = np.clip(wz_f - self.wz, -self.max_dw, self.max_dw)
|
||||
|
||||
self.vx += dvx
|
||||
self.vy += dvy
|
||||
self.wz += dwz
|
||||
|
||||
return self.vx, self.vy, self.wz
|
||||
|
||||
|
||||
# ===================== 主程序 =====================
|
||||
def main():
|
||||
model = mujoco.MjModel.from_xml_path(SCENE_PATH)
|
||||
data = mujoco.MjData(model)
|
||||
@@ -199,163 +305,145 @@ def main():
|
||||
body_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_BODY, "cylinder_agent")
|
||||
site_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_SITE, "ray_origin")
|
||||
|
||||
# 执行器索引
|
||||
act_ids = [
|
||||
mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_ACTUATOR, "vel_x"),
|
||||
mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_ACTUATOR, "vel_y"),
|
||||
mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_ACTUATOR, "vel_yaw"),
|
||||
]
|
||||
# actuators
|
||||
act_vel_x = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_ACTUATOR, "vel_x")
|
||||
act_vel_y = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_ACTUATOR, "vel_y")
|
||||
act_vel_yaw = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_ACTUATOR, "vel_yaw")
|
||||
|
||||
# goal marker joint
|
||||
goal_joint_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_JOINT, "goal_joint")
|
||||
goal_qposadr = int(model.jnt_qposadr[goal_joint_id])
|
||||
|
||||
def hide_goal_marker():
|
||||
data.qpos[goal_qposadr: goal_qposadr + 3] = [-99.0, -99.0, 0.1]
|
||||
|
||||
def set_goal_marker(gx: float, gy: float):
|
||||
data.qpos[goal_qposadr] = gx
|
||||
data.qpos[goal_qposadr + 1] = gy
|
||||
data.qpos[goal_qposadr + 2] = 0.15
|
||||
|
||||
hide_goal_marker()
|
||||
|
||||
scanner = RayScanner(CFG.num_rays, CFG.ray_angle_span)
|
||||
vel_filter = VelocityFilter(alpha=CFG.ema_alpha, max_dv=CFG.max_dv, max_dw=CFG.max_dw)
|
||||
|
||||
goals: List[Tuple[float, float]] = []
|
||||
goal_active = False
|
||||
|
||||
goals = GOAL_POINTS.copy()
|
||||
goal_idx = 0 if goals else -1
|
||||
vel_filter = VelocityFilter(alpha=0.70, max_dv=0.18, max_dw=0.2)
|
||||
last_x, last_y = 0.0, 0.0
|
||||
stuck_cnt = 0
|
||||
|
||||
goal_joint_id = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_JOINT, "goal_joint")
|
||||
goal_qposadr = model.jnt_qposadr[goal_joint_id]
|
||||
data.qpos[goal_qposadr : goal_qposadr + 3] = [-99, -99, 0.1] # 初始隐藏
|
||||
# key flags
|
||||
add_goal_ahead = False
|
||||
add_goal_camera = False
|
||||
|
||||
print("=" * 55)
|
||||
print("VLA 3D 导航 - 全向移动 + 前向扫描 + 避障")
|
||||
print("=" * 55)
|
||||
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))
|
||||
print("=" * 60)
|
||||
print("MuJoCo 全向导航：射线扫描 + 避障 + 目标点")
|
||||
print(f"G: 车头前方 {CFG.goal_ahead_dist:.1f}m | C: 相机视线落点 | 目标边界 R<{CFG.goal_max_r:.1f}m")
|
||||
print("ESC 退出")
|
||||
print("=" * 55)
|
||||
print("=" * 60)
|
||||
|
||||
add_goal_ahead = [False]
|
||||
add_goal_camera = [False]
|
||||
def key_cb(keycode: int):
|
||||
nonlocal add_goal_ahead, add_goal_camera
|
||||
if keycode == 71: # G
|
||||
add_goal_ahead = True
|
||||
elif keycode == 67: # C
|
||||
add_goal_camera = True
|
||||
|
||||
def key_cb(keycode):
|
||||
if keycode == 71: # G
|
||||
add_goal_ahead[0] = True
|
||||
elif keycode == 67: # C: 相机视线与地面交点
|
||||
add_goal_camera[0] = True
|
||||
|
||||
def _goal_from_camera(viewer_handle):
|
||||
"""相机视线与 z=0 地面交点（旋转相机后按 C）"""
|
||||
cam = viewer_handle.cam
|
||||
lookat = np.array(cam.lookat)
|
||||
dist = float(cam.distance)
|
||||
az = np.deg2rad(float(cam.azimuth))
|
||||
el = np.deg2rad(float(cam.elevation))
|
||||
# 相机位置 = lookat - dist * 前向单位向量
|
||||
fx = np.cos(el) * np.sin(az)
|
||||
fy = -np.cos(el) * np.cos(az)
|
||||
fz = np.sin(el)
|
||||
cam_pos = lookat - dist * np.array([fx, fy, fz])
|
||||
# 射线与 z=0 交点: cam_pos + t*[fx,fy,fz], 令 z=0
|
||||
if abs(fz) < 1e-6:
|
||||
return None
|
||||
t = -cam_pos[2] / fz
|
||||
if t < 0:
|
||||
return None
|
||||
pt = cam_pos + t * np.array([fx, fy, fz])
|
||||
return (float(pt[0]), float(pt[1]))
|
||||
|
||||
def control_callback():
|
||||
nonlocal goal_idx, last_x, last_y, stuck_cnt
|
||||
if add_goal_ahead[0]:
|
||||
add_goal_ahead[0] = False
|
||||
x, y, yaw = get_agent_state(data, body_id)
|
||||
gx = x + GOAL_AHEAD_DIST * np.cos(yaw)
|
||||
gy = y + GOAL_AHEAD_DIST * np.sin(yaw)
|
||||
gx, gy = _clamp_goal(gx, gy)
|
||||
goals.clear()
|
||||
goals.append((gx, gy))
|
||||
goal_idx = 0
|
||||
vel_filter.vx = vel_filter.vy = vel_filter.wz = 0.0
|
||||
print(" 目标: ({:.2f}, {:.2f}) [G=车头前方]".format(gx, gy))
|
||||
elif add_goal_camera[0]:
|
||||
add_goal_camera[0] = False
|
||||
g = _goal_from_camera(viewer)
|
||||
if g is not None:
|
||||
g = _clamp_goal(g[0], g[1])
|
||||
goals.clear()
|
||||
goals.append(g)
|
||||
goal_idx = 0
|
||||
vel_filter.vx = vel_filter.vy = vel_filter.wz = 0.0
|
||||
print(" 目标: ({:.2f}, {:.2f}) [C=相机视线]".format(g[0], g[1]))
|
||||
else:
|
||||
print(" [C] 相机未指向地面，请调整视角后重试")
|
||||
|
||||
if goal_idx < 0 or not goals:
|
||||
data.ctrl[act_ids[0]] = 0
|
||||
data.ctrl[act_ids[1]] = 0
|
||||
data.ctrl[act_ids[2]] = 0
|
||||
vel_filter.vx = vel_filter.vy = vel_filter.wz = 0.0
|
||||
data.qpos[goal_qposadr : goal_qposadr + 3] = [-99, -99, 0.1] # 藏起标记
|
||||
return
|
||||
|
||||
x, y, yaw = get_agent_state(data, body_id)
|
||||
goal_x, goal_y = goals[goal_idx]
|
||||
|
||||
dist_to_goal = np.sqrt((goal_x - x) ** 2 + (goal_y - y) ** 2)
|
||||
if dist_to_goal < GOAL_THRESHOLD:
|
||||
goals.clear()
|
||||
goal_idx = -1
|
||||
vel_filter.vx = vel_filter.vy = vel_filter.wz = 0.0
|
||||
print(" 已到达，停止。按 G 设置新目标")
|
||||
data.ctrl[act_ids[0]] = 0
|
||||
data.ctrl[act_ids[1]] = 0
|
||||
data.ctrl[act_ids[2]] = 0
|
||||
data.qpos[goal_qposadr : goal_qposadr + 3] = [-99, -99, 0.1]
|
||||
return
|
||||
|
||||
# 更新目标点标记位置
|
||||
data.qpos[goal_qposadr] = goal_x
|
||||
data.qpos[goal_qposadr + 1] = goal_y
|
||||
data.qpos[goal_qposadr + 2] = 0.15
|
||||
|
||||
# 1. 前向范围扫描
|
||||
distances, angles = forward_ray_scan(
|
||||
model, data, body_id, site_id, NUM_RAYS, RAY_ANGLE_SPAN
|
||||
)
|
||||
min_d = np.min(distances)
|
||||
|
||||
# 2. 卡住检测
|
||||
moved = np.sqrt((x - last_x) ** 2 + (y - last_y) ** 2)
|
||||
if moved < STUCK_THRESH and dist_to_goal > GOAL_THRESHOLD:
|
||||
stuck_cnt += 1
|
||||
else:
|
||||
stuck_cnt = 0
|
||||
last_x, last_y = x, y
|
||||
stuck = stuck_cnt > STUCK_STEPS
|
||||
|
||||
# 3. 避障速度（含绕行分量）
|
||||
vx_a, vy_a, wz_a = obstacle_avoidance_vel(distances, angles)
|
||||
|
||||
# 4. 目标吸引速度
|
||||
vx_g, vy_g, wz_g = goal_attraction_vel(x, y, yaw, goal_x, goal_y)
|
||||
|
||||
# 5. 融合、最小速度、限幅
|
||||
vx, vy, wz = blend_and_clamp(
|
||||
vx_a, vy_a, wz_a, vx_g, vy_g, wz_g, dist_to_goal, min_d, stuck
|
||||
)
|
||||
|
||||
# 6. 滤波 + 速率限制（卡住时放宽）
|
||||
max_dv = 0.25 if stuck else 0.18
|
||||
max_dw = 0.28 if stuck else 0.2
|
||||
vel_filter.max_dv = max_dv
|
||||
vel_filter.max_dw = max_dw
|
||||
vx, vy, wz = vel_filter.update(vx, vy, wz)
|
||||
|
||||
# 车体系 -> 世界系（slide_x/y 沿世界轴）
|
||||
c, s = np.cos(yaw), np.sin(yaw)
|
||||
vx_w = vx * c - vy * s
|
||||
vy_w = vx * s + vy * c
|
||||
|
||||
data.ctrl[act_ids[0]] = vx_w
|
||||
data.ctrl[act_ids[1]] = vy_w
|
||||
data.ctrl[act_ids[2]] = wz
|
||||
def stop_output():
|
||||
data.ctrl[act_vel_x] = 0.0
|
||||
data.ctrl[act_vel_y] = 0.0
|
||||
data.ctrl[act_vel_yaw] = 0.0
|
||||
|
||||
with mujoco.viewer.launch_passive(model, data, key_callback=key_cb) as viewer:
|
||||
while viewer.is_running():
|
||||
mujoco.mj_forward(model, data)
|
||||
control_callback()
|
||||
|
||||
# ========== goal set ==========
|
||||
nonlocal_vars = None # 仅为了强调这里不用闭包 nonlocal
|
||||
if add_goal_ahead:
|
||||
add_goal_ahead = False
|
||||
x, y, yaw = get_agent_state(data)
|
||||
gx = x + CFG.goal_ahead_dist * float(np.cos(yaw))
|
||||
gy = y + CFG.goal_ahead_dist * float(np.sin(yaw))
|
||||
gx, gy = clamp_goal(gx, gy, CFG.goal_max_r)
|
||||
goals[:] = [(gx, gy)]
|
||||
goal_active = True
|
||||
vel_filter.reset()
|
||||
print(f" 目标: ({gx:.2f}, {gy:.2f}) [G=车头前方]")
|
||||
elif add_goal_camera:
|
||||
add_goal_camera = False
|
||||
g = goal_from_camera(viewer)
|
||||
if g is None:
|
||||
print(" [C] 相机未指向地面，请调整视角后重试")
|
||||
else:
|
||||
gx, gy = clamp_goal(g[0], g[1], CFG.goal_max_r)
|
||||
goals[:] = [(gx, gy)]
|
||||
goal_active = True
|
||||
vel_filter.reset()
|
||||
print(f" 目标: ({gx:.2f}, {gy:.2f}) [C=相机视线]")
|
||||
|
||||
if not goal_active or not goals:
|
||||
stop_output()
|
||||
vel_filter.reset()
|
||||
hide_goal_marker()
|
||||
mujoco.mj_step(model, data)
|
||||
viewer.sync()
|
||||
continue
|
||||
|
||||
# ========== state ==========
|
||||
x, y, yaw = get_agent_state(data)
|
||||
goal_x, goal_y = goals[0]
|
||||
|
||||
dist_to_goal = float(np.hypot(goal_x - x, goal_y - y))
|
||||
if dist_to_goal < CFG.goal_threshold:
|
||||
print(" 已到达，停止。按 G / C 设置新目标")
|
||||
goal_active = False
|
||||
goals.clear()
|
||||
vel_filter.reset()
|
||||
stop_output()
|
||||
hide_goal_marker()
|
||||
mujoco.mj_step(model, data)
|
||||
viewer.sync()
|
||||
continue
|
||||
|
||||
set_goal_marker(goal_x, goal_y)
|
||||
|
||||
# ========== scan ==========
|
||||
distances, angles = scanner.scan(model, data, body_id, site_id)
|
||||
|
||||
# ========== stuck detect ==========
|
||||
moved = float(np.hypot(x - last_x, y - last_y))
|
||||
if moved < CFG.stuck_thresh and dist_to_goal > CFG.goal_threshold:
|
||||
stuck_cnt += 1
|
||||
else:
|
||||
stuck_cnt = 0
|
||||
last_x, last_y = x, y
|
||||
stuck = stuck_cnt > CFG.stuck_steps
|
||||
|
||||
# ========== velocities ==========
|
||||
vx_a, vy_a, wz_a, _min_d = obstacle_avoidance_vel(distances, angles, CFG)
|
||||
vx_g, vy_g, wz_g = goal_attraction_vel(x, y, yaw, goal_x, goal_y, CFG)
|
||||
vx_b, vy_b, wz_b = blend_and_clamp(vx_a, vy_a, wz_a, vx_g, vy_g, wz_g, dist_to_goal, stuck, CFG)
|
||||
|
||||
# ========== filter ==========
|
||||
vel_filter.max_dv = CFG.max_dv_stuck if stuck else CFG.max_dv
|
||||
vel_filter.max_dw = CFG.max_dw_stuck if stuck else CFG.max_dw
|
||||
vx, vy, wz = vel_filter.update(vx_b, vy_b, wz_b)
|
||||
|
||||
# ========== body -> world for actuator ==========
|
||||
c = float(np.cos(yaw))
|
||||
s = float(np.sin(yaw))
|
||||
vx_w = vx * c - vy * s
|
||||
vy_w = vx * s + vy * c
|
||||
|
||||
data.ctrl[act_vel_x] = vx_w
|
||||
data.ctrl[act_vel_y] = vy_w
|
||||
data.ctrl[act_vel_yaw] = wz
|
||||
|
||||
mujoco.mj_step(model, data)
|
||||
viewer.sync()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
main()
|
||||
Reference in New Issue
Block a user