Compare commits
3 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
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f8bf0507ac | ||
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d4992f9b41 | ||
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175ea87fed |
554
MJDATA.TXT
Normal file
554
MJDATA.TXT
Normal file
@@ -0,0 +1,554 @@
|
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SIZES
|
||||
narena 13631488
|
||||
nbuffer 13952
|
||||
nplugin 0
|
||||
maxuse_stack 2388
|
||||
maxuse_arena 29552
|
||||
maxuse_con 1
|
||||
maxuse_efc 4
|
||||
solver_nisland 1
|
||||
ne 0
|
||||
nf 0
|
||||
nl 0
|
||||
nefc 0
|
||||
nnzJ 0
|
||||
ncon 0
|
||||
nisland 0
|
||||
threadpool 0
|
||||
|
||||
TIMER
|
||||
0: duration = 0.0029 number = 1
|
||||
1: duration = 0.0055 number = 2
|
||||
2: duration = 0 number = 0
|
||||
3: duration = 0.0039 number = 2
|
||||
4: duration = 0.0009 number = 2
|
||||
5: duration = 0.00017 number = 2
|
||||
6: duration = 6e-05 number = 2
|
||||
7: duration = 8e-05 number = 1
|
||||
8: duration = 0.001 number = 2
|
||||
9: duration = 0.00032 number = 2
|
||||
10: duration = 0.0018 number = 2
|
||||
11: duration = 0.00029 number = 2
|
||||
12: duration = 6e-05 number = 2
|
||||
13: duration = 0.0014 number = 2
|
||||
14: duration = 0.00031 number = 2
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||||
|
||||
ENERGY = 0 0
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||||
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||||
TIME = 3.7e+04
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||||
|
||||
QPOS
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||||
0.6
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||||
0.51
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||||
-27
|
||||
-99
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||||
-99
|
||||
-7.3e+02
|
||||
1
|
||||
0
|
||||
0
|
||||
0
|
||||
|
||||
QVEL
|
||||
-1.1e-322
|
||||
-1.1e-322
|
||||
-2e-323
|
||||
0
|
||||
0
|
||||
-3.7e+05
|
||||
0
|
||||
0
|
||||
0
|
||||
|
||||
QACC_WARMSTART
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||||
1.2e-321
|
||||
1.2e-321
|
||||
8.8e-322
|
||||
0
|
||||
0
|
||||
-9.8
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||||
0
|
||||
0
|
||||
0
|
||||
|
||||
CTRL
|
||||
0
|
||||
0
|
||||
0
|
||||
|
||||
QFRC_APPLIED
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
|
||||
XFRC_APPLIED
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
|
||||
QACC
|
||||
1.2e-321
|
||||
1.2e-321
|
||||
8.8e-322
|
||||
0
|
||||
0
|
||||
-9.8
|
||||
0
|
||||
0
|
||||
0
|
||||
|
||||
XPOS
|
||||
0 0 0
|
||||
0.6 0.51 0.35
|
||||
-99 -99 0.1
|
||||
1.5 0 0.25
|
||||
-1.2 1 0.2
|
||||
0.8 -1 0.15
|
||||
-0.5 -0.8 0.2
|
||||
1 0.8 0.25
|
||||
|
||||
XQUAT
|
||||
1 0 0 0
|
||||
0.74 0 0 -0.67
|
||||
1 0 0 0
|
||||
1 0 0 0
|
||||
1 0 0 0
|
||||
1 0 0 0
|
||||
1 0 0 0
|
||||
1 0 0 0
|
||||
|
||||
XMAT
|
||||
1 0 0 0 1 0 0 0 1
|
||||
0.096 1 0 -1 0.096 -0 -0 0 1
|
||||
1 0 0 0 1 0 0 0 1
|
||||
1 0 0 0 1 0 0 0 1
|
||||
1 0 0 0 1 0 0 0 1
|
||||
1 0 0 0 1 0 0 0 1
|
||||
1 0 0 0 1 0 0 0 1
|
||||
1 0 0 0 1 0 0 0 1
|
||||
|
||||
XIPOS
|
||||
0 0 0
|
||||
0.61 0.48 0.36
|
||||
-99 -99 0.1
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||||
1.5 0 0.25
|
||||
-1.2 1 0.2
|
||||
0.8 -1 0.15
|
||||
-0.5 -0.8 0.2
|
||||
1 0.8 0.25
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||||
|
||||
XIMAT
|
||||
1 0 0 0 1 0 0 0 1
|
||||
0.024 -1 0.049 -0.021 -0.05 -1 1 0.023 -0.022
|
||||
1 0 0 0 1 0 0 0 1
|
||||
1 0 0 0 1 0 0 0 1
|
||||
1 0 0 0 1 0 0 0 1
|
||||
1 0 0 0 1 0 0 0 1
|
||||
1 0 0 0 1 0 0 0 1
|
||||
1 0 0 0 1 0 0 0 1
|
||||
|
||||
XANCHOR
|
||||
0 0 0.35
|
||||
0.6 0 0.35
|
||||
0.6 0.51 0.35
|
||||
-99 -99 0.1
|
||||
|
||||
XAXIS
|
||||
1 0 0
|
||||
0 1 0
|
||||
0 0 1
|
||||
0 0 1
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||||
|
||||
GEOM_XPOS
|
||||
0 0 0
|
||||
0.6 0.51 0.35
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||||
0.62 0.32 0.35
|
||||
0.73 0.52 0.35
|
||||
0.6 0.51 0.45
|
||||
-99 -99 0.1
|
||||
1.5 0 0.25
|
||||
-1.2 1 0.2
|
||||
0.8 -1 0.15
|
||||
-0.5 -0.8 0.2
|
||||
1 0.8 0.25
|
||||
|
||||
GEOM_XMAT
|
||||
1 0 0 0 1 0 0 0 1
|
||||
0.096 1 0 -1 0.096 -0 -0 0 1
|
||||
-2.8e-17 1 -0.096 -5.6e-17 0.096 1 1 5.6e-17 5.6e-17
|
||||
0.096 5.6e-17 -1 -1 -5.6e-17 -0.096 -5.6e-17 1 5.6e-17
|
||||
0.096 -1 -1.2e-16 -1 -0.096 -1.2e-17 0 1.2e-16 -1
|
||||
1 0 0 0 1 0 0 0 1
|
||||
1 0 0 0 1 0 0 0 1
|
||||
1 0 0 0 1 0 0 0 1
|
||||
1 0 0 0 1 0 0 0 1
|
||||
1 0 0 0 1 0 0 0 1
|
||||
1 0 0 0 1 0 0 0 1
|
||||
|
||||
SITE_XPOS
|
||||
0.63 0.26 0.35
|
||||
|
||||
SITE_XMAT
|
||||
0.096 1 0 -1 0.096 -0 -0 0 1
|
||||
|
||||
CAM_XPOS
|
||||
0 0 5
|
||||
|
||||
CAM_XMAT
|
||||
1 0 0 0 1 0 0 0 1
|
||||
|
||||
SUBTREE_COM
|
||||
-1.4 -2.1 0.23
|
||||
0.61 0.48 0.36
|
||||
-99 -99 0.1
|
||||
1.5 0 0.25
|
||||
-1.2 1 0.2
|
||||
0.8 -1 0.15
|
||||
-0.5 -0.8 0.2
|
||||
1 0.8 0.25
|
||||
|
||||
CDOF
|
||||
0 0 0 1 0 0
|
||||
0 0 0 0 1 0
|
||||
0 0 1 0.037 0.012 -0
|
||||
0 0 0 1 0 0
|
||||
0 0 0 0 1 0
|
||||
0 0 0 0 0 1
|
||||
1 0 0 0 0 0
|
||||
0 1 0 0 0 0
|
||||
0 0 1 0 0 0
|
||||
|
||||
CINERT
|
||||
0 0 0 0 0 0 0 0 0 0
|
||||
0.15 0.1 0.17 0.0025 0.00047 -0.0014 0 0 0 7.3
|
||||
0.042 0.042 0.042 0 0 0 0 0 0 7.2
|
||||
9.2 9.2 11 0 0 0 0 0 0 1.8e+02
|
||||
0.54 0.54 0.32 0 0 0 0 0 0 28
|
||||
0.13 0.13 0.13 0 0 0 0 0 -3.9e-16 14
|
||||
3.4 3.4 4.2 0 0 0 0 0 0 1e+02
|
||||
0.55 0.55 0.16 0 0 0 0 0 0 23
|
||||
|
||||
ACTUATOR_LENGTH
|
||||
0.6
|
||||
0.51
|
||||
-27
|
||||
|
||||
ACTUATOR_MOMENT
|
||||
1 0 0 0 0 0 0 0 0
|
||||
0 1 0 0 0 0 0 0 0
|
||||
0 0 1 0 0 0 0 0 0
|
||||
|
||||
CRB
|
||||
0 0 0 0 0 0 0 0 0 0
|
||||
0.15 0.1 0.17 0.0025 0.00047 -0.0014 0 0 0 7.3
|
||||
0.042 0.042 0.042 0 0 0 0 0 0 7.2
|
||||
9.2 9.2 11 0 0 0 0 0 0 1.8e+02
|
||||
0.54 0.54 0.32 0 0 0 0 0 0 28
|
||||
0.13 0.13 0.13 0 0 0 0 0 -3.9e-16 14
|
||||
3.4 3.4 4.2 0 0 0 0 0 0 1e+02
|
||||
0.55 0.55 0.16 0 0 0 0 0 0 23
|
||||
|
||||
QM
|
||||
7.3 0 0.27 0 0 0 0 0 0
|
||||
0 7.3 0.088 0 0 0 0 0 0
|
||||
0.27 0.088 0.18 0 0 0 0 0 0
|
||||
0 0 0 7.2 0 0 0 0 0
|
||||
0 0 0 0 7.2 0 0 0 0
|
||||
0 0 0 0 0 7.2 0 0 0
|
||||
0 0 0 0 0 0 0.042 0 0
|
||||
0 0 0 0 0 0 0 0.042 0
|
||||
0 0 0 0 0 0 0 0 0.042
|
||||
|
||||
QLD
|
||||
6.9 -0.018 1.5 0 0 0 0 0 0
|
||||
-0.018 7.3 0.48 0 0 0 0 0 0
|
||||
1.5 0.48 0.18 0 0 0 0 0 0
|
||||
0 0 0 7.2 0 0 0 0 0
|
||||
0 0 0 0 7.2 0 0 0 0
|
||||
0 0 0 0 0 7.2 0 0 0
|
||||
0 0 0 0 0 0 0.042 0 0
|
||||
0 0 0 0 0 0 0 0.042 0
|
||||
0 0 0 0 0 0 0 0 0.042
|
||||
|
||||
QLDIAGINV
|
||||
0.14
|
||||
0.14
|
||||
5.5
|
||||
0.14
|
||||
0.14
|
||||
0.14
|
||||
24
|
||||
24
|
||||
24
|
||||
|
||||
QLDIAGSQRTINV
|
||||
0.38
|
||||
0.37
|
||||
2.3
|
||||
0.37
|
||||
0.37
|
||||
0.37
|
||||
4.9
|
||||
4.9
|
||||
4.9
|
||||
|
||||
B_rownnz 9 3 6 0 0 0 0 0
|
||||
|
||||
B_rowadr 0 9 12 18 18 18 18 18
|
||||
|
||||
B_colind 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8
|
||||
|
||||
C_rownnz 3 3 3 1 1 1 1 1 1
|
||||
|
||||
C_rowadr 0 3 6 9 10 11 12 13 14
|
||||
|
||||
C_colind 0 1 2 0 1 2 0 1 2 3 4 5 6 7 8
|
||||
|
||||
mapM2C 0 2 5 2 1 4 5 4 3 6 7 9 12 16 21
|
||||
|
||||
D_rownnz 3 3 3 6 6 6 6 6 6
|
||||
|
||||
D_rowadr 0 3 6 9 15 21 27 33 39
|
||||
|
||||
D_colind 0 1 2 0 1 2 0 1 2 3 4 5 6 7 8 3 4 5 6 7 8 3 4 5 6 7 8 3 4 5 6 7 8 3 4 5 6 7 8 3 4 5 6 7 8
|
||||
|
||||
mapM2D 0 2 5 2 1 4 5 4 3 6 8 11 15 20 26 8 7 10 14 19 25 11 10 9 13 18 24 15 14 13 12 17 23 20 19 18 17 16 22 26 25 24 23 22 21
|
||||
|
||||
mapD2M 0 4 3 8 7 6 9 16 15 23 22 21 30 29 28 27 37 36 35 34 33 44 43 42 41 40 39
|
||||
|
||||
QDERIV
|
||||
0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0
|
||||
|
||||
QLU
|
||||
0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0
|
||||
0 0 0 0 0 0 0 0 0
|
||||
|
||||
CONTACT
|
||||
ACTUATOR_VELOCITY
|
||||
-1.1e-322
|
||||
-1.1e-322
|
||||
-2e-323
|
||||
|
||||
CVEL
|
||||
0 0 0 0 0 0
|
||||
0 0 -2e-323 -1.1e-322 -1.1e-322 0
|
||||
0 0 0 0 0 -3.7e+05
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
|
||||
CDOF_DOT
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 -1.1e-322 1.1e-322 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 -3.7e+05 0
|
||||
0 0 0 3.7e+05 0 0
|
||||
0 0 0 0 0 0
|
||||
|
||||
QFRC_BIAS
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
71
|
||||
0
|
||||
0
|
||||
0
|
||||
|
||||
QFRC_SPRING
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
|
||||
QFRC_DAMPER
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
|
||||
QFRC_GRAVCOMP
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
|
||||
QFRC_FLUID
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
|
||||
QFRC_PASSIVE
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
|
||||
SUBTREE_LINVEL
|
||||
0 0 0
|
||||
0 0 0
|
||||
0 0 0
|
||||
0 0 0
|
||||
0 0 0
|
||||
0 0 0
|
||||
0 0 0
|
||||
0 0 0
|
||||
|
||||
SUBTREE_ANGMOM
|
||||
0 0 0
|
||||
0 0 0
|
||||
0 0 0
|
||||
0 0 0
|
||||
0 0 0
|
||||
0 0 0
|
||||
0 0 0
|
||||
0 0 0
|
||||
|
||||
ACTUATOR_FORCE
|
||||
9.1e-321
|
||||
9.1e-321
|
||||
5.9e-322
|
||||
|
||||
QFRC_ACTUATOR
|
||||
9.1e-321
|
||||
9.1e-321
|
||||
5.9e-322
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
|
||||
QFRC_SMOOTH
|
||||
9.1e-321
|
||||
9.1e-321
|
||||
5.9e-322
|
||||
0
|
||||
0
|
||||
-71
|
||||
0
|
||||
0
|
||||
0
|
||||
|
||||
QACC_SMOOTH
|
||||
1.2e-321
|
||||
1.2e-321
|
||||
8.8e-322
|
||||
0
|
||||
0
|
||||
-9.8
|
||||
0
|
||||
0
|
||||
0
|
||||
|
||||
QFRC_CONSTRAINT
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
|
||||
QFRC_INVERSE
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
0
|
||||
|
||||
CACC
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
|
||||
CFRC_INT
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
|
||||
CFRC_EXT
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
0 0 0 0 0 0
|
||||
|
||||
105
README.md
105
README.md
@@ -0,0 +1,105 @@
|
||||
# VLA 视觉避障 3D 导航（MuJoCo + ROS1）
|
||||
|
||||
基于 MuJoCo 与 ROS1 的 **全向小车视觉避障导航** 仿真项目，支持仿真深度图输入，并可直接对接 RealSense / Orbbec / ZED 等深感相机落地。
|
||||
|
||||
---
|
||||
|
||||
## 特性
|
||||
|
||||
- **全向移动**：vx, vy, wz 车体系速度 → 世界系执行器
|
||||
- **仿真深度图**：mj_ray 网格模拟深感相机，便于替换为真实 depth/pointcloud
|
||||
- **DDA 避障**：Directional Distance Avoidance，基于方向距离的避障
|
||||
- **目标点导航**：按键设置目标，大角度先转后走，小角度边走边调
|
||||
- **转向策略**：大角度（>35°）优先转向，小角度时前进并微调朝向
|
||||
|
||||
---
|
||||
|
||||
## 安装
|
||||
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
依赖：`mujoco>=3.0.0`、`numpy`
|
||||
|
||||
---
|
||||
|
||||
## 快速开始
|
||||
|
||||
```bash
|
||||
python scripts/run_simulation.py
|
||||
```
|
||||
|
||||
### 操作说明
|
||||
|
||||
| 按键 | 功能 |
|
||||
|------|------|
|
||||
| **G** | 目标 = 车头前方 2m |
|
||||
| **C** | 目标 = 相机视线与地面交点 |
|
||||
| **ESC** | 退出 |
|
||||
|
||||
小车会按设定目标行驶，到达后停止，可继续按键设置新目标。目标点有边界限制（距原点 ≤ 5m）。
|
||||
|
||||
---
|
||||
|
||||
## 目录结构
|
||||
|
||||
```
|
||||
├── README.md
|
||||
├── OUTLINE.md # 项目大纲与里程碑
|
||||
├── requirements.txt
|
||||
├── mujoco_scenes/ # MuJoCo 场景
|
||||
│ └── cylinder_obstacles.xml
|
||||
├── scripts/
|
||||
│ └── run_simulation.py # 仿真主程序
|
||||
└── docs/
|
||||
└── DDA_KNOWLEDGE.md # DDA 知识点与 ROS1 落地说明
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 控制流程
|
||||
|
||||
```
|
||||
仿真深度图 (mj_ray) / 真实 depth
|
||||
→ depth_to_sectors() → dists, angles
|
||||
→ dda_avoidance_from_depth() → vx_a, vy_a, wz_a
|
||||
→ goal_attraction_vel() → vx_g, vy_g, wz_g
|
||||
→ blend_and_clamp() → 融合 + 前方扇区限速
|
||||
→ 滤波 → 车体系→世界系 → 执行器
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 主要参数（`NavCfg`）
|
||||
|
||||
| 参数 | 默认 | 说明 |
|
||||
|------|------|------|
|
||||
| `hfov_deg` | 86 | 水平视场角（如 RealSense D435）|
|
||||
| `num_sectors` | 61 | 扇区数 |
|
||||
| `obstacle_threshold` | 0.45 | 避障停止距离 (m) |
|
||||
| `safe_distance` | 0.65 | 安全距离 (m) |
|
||||
| `yaw_turn_first_deg` | 35 | 大角度先转阈值 (°) |
|
||||
| `goal_ahead_dist` | 2.0 | G 键目标前方距离 (m) |
|
||||
| `goal_max_r` | 5.0 | 目标点最大半径 (m) |
|
||||
|
||||
---
|
||||
|
||||
## ROS1 + 深感相机落地
|
||||
|
||||
`depth_to_sectors()` 的输入为 H×W 深度图（米），与真实相机接口一致。在 ROS1 中订阅深度图：
|
||||
|
||||
```python
|
||||
# 伪代码
|
||||
rospy.Subscriber('/camera/depth/image_rect', Image, depth_cb)
|
||||
# depth_cb 中：depth_to_sectors(depth, hfov_rad, num_sectors, ...) → dists, angles
|
||||
```
|
||||
|
||||
详见 `docs/DDA_KNOWLEDGE.md`。
|
||||
|
||||
---
|
||||
|
||||
## 引用
|
||||
|
||||
- [MuJoCo](https://mujoco.readthedocs.io/)：物理仿真
|
||||
- [OUTLINE.md](OUTLINE.md)：项目规划与 VLA 目标
|
||||
|
||||
122
docs/DDA_KNOWLEDGE.md
Normal file
122
docs/DDA_KNOWLEDGE.md
Normal file
@@ -0,0 +1,122 @@
|
||||
# DDA 知识点：Directional Distance Avoidance
|
||||
|
||||
## 一、从「离散射线」到「深度图」的转变
|
||||
|
||||
| 原方案 | 新方案 |
|
||||
|--------|--------|
|
||||
| mj_ray 离散射线 | 深度图 / 点云（带距离的视场） |
|
||||
| N 条射线直接给距离 | 从深度图按方位做 1D 距离扇区 |
|
||||
| 每条射线独立 | 每个扇区取**最近障碍距离**（鲁棒统计） |
|
||||
|
||||
**核心**：深感相机给你的是 **Depth / PointCloud**，不需要 mj_ray 这种离散射线；先把深度转成扇区最近距离，再做避障。
|
||||
|
||||
---
|
||||
|
||||
## 二、DDA 核心思想
|
||||
|
||||
1. **输入**：`dist[sector]` —— 每个方向上的最近障碍距离（米）
|
||||
2. **判断**：当前期望运动方向 `move_theta = atan2(vy_cmd, vx_cmd)` 是否过近
|
||||
3. **动作**：
|
||||
- 若过近：**禁止/衰减**朝该方向的速度分量
|
||||
- 同时生成**侧向/偏航**的「脱离」控制，朝开阔方向
|
||||
|
||||
---
|
||||
|
||||
## 三、深度 → 扇区最近距离（depth_to_sectors）
|
||||
|
||||
- 视场水平角 `HFOV`（如 RealSense D435 ≈ 86°）
|
||||
- 扇区数 `N`（如 31、61）
|
||||
- 对每个扇区：在深度图对应列范围取**鲁棒最小值**：
|
||||
- `percentile(depth, 5%)` 或
|
||||
- 剔除 0/无效 + 中值滤波
|
||||
|
||||
得到：
|
||||
- `angles[i]`：扇区角（车体坐标系，左正右负）
|
||||
- `d[i]`：该方向最近障碍距离（米）
|
||||
|
||||
---
|
||||
|
||||
## 四、DDA 避障控制律
|
||||
|
||||
1. 计算期望运动方向角：`move_theta = atan2(vy_cmd, vx_cmd)`
|
||||
2. 在 `move_theta` 附近取窗口内最小距离 `dmin_dir`
|
||||
3. 阈值判断：
|
||||
- `dmin_dir >= safe_dist`：正常走
|
||||
- `dmin_dir < stop_dist`：禁止向前 + 强制侧移/转向
|
||||
- 中间区间：按比例衰减 + 轻微偏航
|
||||
4. 选择「最开阔方向」`best_theta`，作为绕行/偏航目标
|
||||
|
||||
---
|
||||
|
||||
## 五、ROS1 + 深感相机 落地要点
|
||||
|
||||
### 5.1 深度图输入
|
||||
|
||||
- **Depth Image**：`sensor_msgs/Image`，订阅 `/camera/depth/image_rect` 等
|
||||
- **PointCloud**：`sensor_msgs/PointCloud2`，转为深度图或直接投影到扇区
|
||||
|
||||
### 5.2 常用深感相机参数
|
||||
|
||||
| 相机 | HFOV | 分辨率 |
|
||||
|------|------|--------|
|
||||
| RealSense D435 | ~87° | 640×480 |
|
||||
| Orbbec Astra | ~60° | 640×480 |
|
||||
| ZED | 可调 | 多种 |
|
||||
| OAK-D | ~73° | 640×400 |
|
||||
|
||||
### 5.3 推荐两行增强（仿真中已启用）
|
||||
|
||||
1. **速度自适应安全距离**：跑得越快看得越远
|
||||
```py
|
||||
safe_dist = base_safe + k * speed
|
||||
stop_dist = base_stop + 0.5 * k * speed
|
||||
```
|
||||
|
||||
2. **前方扇区限速**：只用前方 ±20° 的最小距离限速
|
||||
```py
|
||||
front = (np.abs(angles) < np.deg2rad(20))
|
||||
d_front = np.min(dists[front])
|
||||
speed_limit = np.clip((d_front - stop_dist) / (safe_dist - stop_dist), 0, 1) * MAX_LINEAR
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 六、ROS1 深度输入对接（待实现）
|
||||
|
||||
### 6.1 Depth Image 订阅
|
||||
|
||||
```python
|
||||
# 伪代码：ROS1 订阅
|
||||
import rospy
|
||||
from sensor_msgs.msg import Image
|
||||
from cv_bridge import CvBridge
|
||||
|
||||
def depth_cb(msg):
|
||||
depth = bridge.imgmsg_to_cv2(msg, desired_encoding='passthrough')
|
||||
depth = depth.astype(np.float32) / 1000.0 # mm -> m
|
||||
dists, angles = depth_to_sectors(depth, hfov_rad, num_sectors, ...)
|
||||
# 存入共享变量，供控制回调使用
|
||||
|
||||
rospy.Subscriber('/camera/depth/image_rect', Image, depth_cb)
|
||||
```
|
||||
|
||||
### 6.2 PointCloud 订阅
|
||||
|
||||
从 `sensor_msgs/PointCloud2` 投影到极坐标扇区，取每扇区最近距离。
|
||||
|
||||
---
|
||||
|
||||
## 七、控制流程对接
|
||||
|
||||
**仿真**：`mj_ray` 网格 → 仿真深度图 `(1, num_sectors)` → `depth_to_sectors()`
|
||||
**实机**：深感相机 depth/pointcloud → `depth_to_sectors()`
|
||||
|
||||
```
|
||||
depth (H×W) 或 pointcloud
|
||||
→ depth_to_sectors() → dists, angles
|
||||
→ dda_avoidance_from_depth(dists, angles, vx_g, vy_g)
|
||||
→ vx_a, vy_a, wz_a
|
||||
→ blend_and_clamp(vx_a, vy_a, wz_a, vx_g, vy_g, wz_g)
|
||||
```
|
||||
|
||||
融合后继续做：滤波 → 车体系→世界系 → 执行器。
|
||||
@@ -1,197 +1,440 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
ROS1 + MuJoCo VLA 3D 导航 — 全向移动 + 前向扫描 + 避障 + 目标点导航
|
||||
MuJoCo 全向小车导航（仿真深度图 + DDA 避障 + 目标点导航）
|
||||
|
||||
圆柱体小车: vx, vy, wz 全向移动
|
||||
前向范围扫描: mj_ray 射线检测
|
||||
避障: 基于距离的势场/向量场
|
||||
- 输入：仿真深度图（mj_ray 网格）或可替换为真实深感相机的 depth/pointcloud
|
||||
- depth_to_sectors：深度 → 1D 扇区最近距离（鲁棒统计）
|
||||
- dda_avoidance_from_depth：Directional Distance Avoidance 避障
|
||||
- vx, vy, wz 全向移动（车体系 → 世界系执行器）
|
||||
- 便于 ROS1 + RealSense/Orbbec/ZED 等真实相机落地
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
from dataclasses import dataclass
|
||||
from typing import Optional, Tuple, List
|
||||
|
||||
import mujoco
|
||||
import mujoco.viewer
|
||||
import numpy as np
|
||||
import os
|
||||
|
||||
|
||||
SCENE_PATH = os.path.join(os.path.dirname(__file__), "..", "mujoco_scenes", "cylinder_obstacles.xml")
|
||||
|
||||
# ============ 可配置参数 ============
|
||||
NUM_RAYS = 19 # 前向扫描射线数量（-90 ~ +90 deg）
|
||||
RAY_ANGLE_SPAN = np.pi # 扫描角度跨度 (rad)
|
||||
OBSTACLE_THRESHOLD = 0.45 # 障碍判定距离 (m)
|
||||
SAFE_DISTANCE = 0.65 # 安全距离
|
||||
GOAL_THRESHOLD = 0.2 # 到达目标判定距离
|
||||
MAX_LINEAR = 0.6 # 最大线速度
|
||||
MAX_ANGULAR = 0.8 # 最大角速度
|
||||
K_ATTRACT = 1.0 # 目标吸引力
|
||||
K_REPEL = 0.6 # 障碍排斥力
|
||||
K_YAW_GOAL = 0.8 # 目标对准角速度增益
|
||||
MIN_LINEAR = 0.18 # 最小线速度，避免卡住
|
||||
STUCK_THRESH = 0.03 # 位移阈值，低于此认为卡住 (m)
|
||||
STUCK_STEPS = 100 # 连续卡住步数触发绕行
|
||||
GOAL_POINTS = [] # 初始无目标
|
||||
GOAL_AHEAD_DIST = 2.0 # G 键：目标 = 车头前方 N 米
|
||||
GOAL_MAX_R = 5.0 # 目标点边界：距离原点不超过此值 (m)
|
||||
|
||||
# ===================== 参数配置 =====================
|
||||
@dataclass
|
||||
class NavCfg:
|
||||
# depth sector（仿真/真实深度图统一接口）
|
||||
hfov_deg: float = 86.0 # 水平视场角（如 RealSense D435）
|
||||
num_sectors: int = 61 # 扇区数
|
||||
depth_z_min: float = 0.1
|
||||
depth_z_max: float = 6.0
|
||||
depth_percentile: float = 5.0 # 鲁棒最小值：5% 分位
|
||||
|
||||
# DDA 避障
|
||||
obstacle_threshold: float = 0.45 # stop_dist
|
||||
safe_distance: float = 0.65 # safe_dist
|
||||
dda_window: int = 2 # move_theta 附近 ±N 扇区
|
||||
speed_adaptive_k: float = 0.15 # 速度自适应：safe += k*speed
|
||||
goal_threshold: float = 0.20
|
||||
|
||||
# limits
|
||||
max_linear: float = 0.60
|
||||
max_angular: float = 0.80
|
||||
min_linear: float = 0.18 # 防止“只转不走”或输出趋零卡死
|
||||
|
||||
# gains
|
||||
k_attract: float = 1.0
|
||||
k_repel: float = 0.6
|
||||
k_yaw_goal: float = 0.8
|
||||
# 转向策略：大角度先转后走，小角度边走边调
|
||||
yaw_turn_first_deg: float = 35.0 # 超过此角度：先转向，几乎不走
|
||||
|
||||
# stuck detect
|
||||
stuck_thresh: float = 0.03
|
||||
stuck_steps: int = 100
|
||||
|
||||
# goal set
|
||||
goal_ahead_dist: float = 2.0
|
||||
goal_max_r: float = 5.0
|
||||
|
||||
# output smoothing
|
||||
ema_alpha: float = 0.70
|
||||
max_dv: float = 0.18
|
||||
max_dw: float = 0.20
|
||||
max_dv_stuck: float = 0.25
|
||||
max_dw_stuck: float = 0.28
|
||||
|
||||
|
||||
def _clamp_goal(gx, gy):
|
||||
"""目标点边界：限制在距离原点 GOAL_MAX_R 以内"""
|
||||
r = np.sqrt(gx * gx + gy * gy) + 1e-8
|
||||
if r > GOAL_MAX_R:
|
||||
scale = GOAL_MAX_R / r
|
||||
return (float(gx * scale), float(gy * scale))
|
||||
return (float(gx), float(gy))
|
||||
CFG = NavCfg()
|
||||
|
||||
|
||||
def get_agent_state(data, body_id):
|
||||
"""获取小车位姿: x, y, yaw"""
|
||||
# ===================== 工具函数 =====================
|
||||
def clamp_goal(gx: float, gy: float, max_r: float) -> Tuple[float, float]:
|
||||
r = float(np.hypot(gx, gy)) + 1e-9
|
||||
if r > max_r:
|
||||
s = max_r / r
|
||||
return float(gx * s), float(gy * s)
|
||||
return float(gx), float(gy)
|
||||
|
||||
|
||||
def wrap_pi(a: float) -> float:
|
||||
# 更稳定的 [-pi, pi]
|
||||
return float(np.arctan2(np.sin(a), np.cos(a)))
|
||||
|
||||
|
||||
def get_agent_state(data: mujoco.MjData) -> Tuple[float, float, float]:
|
||||
# 假设 qpos: x, y, yaw 在 0,1,2
|
||||
qpos = data.qpos
|
||||
x, y = qpos[0], qpos[1]
|
||||
yaw = qpos[2]
|
||||
return x, y, yaw
|
||||
return float(qpos[0]), float(qpos[1]), float(qpos[2])
|
||||
|
||||
|
||||
def forward_ray_scan(model, data, body_id, site_id, num_rays, angle_span):
|
||||
def goal_from_camera(viewer_handle) -> Optional[Tuple[float, float]]:
|
||||
"""相机视线与 z=0 平面交点（旋转相机后按 C）。"""
|
||||
cam = viewer_handle.cam
|
||||
lookat = np.array(cam.lookat, dtype=float)
|
||||
dist = float(cam.distance)
|
||||
az = np.deg2rad(float(cam.azimuth))
|
||||
el = np.deg2rad(float(cam.elevation))
|
||||
|
||||
# MuJoCo viewer 的相机方向约定：这里延续你原实现
|
||||
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], dtype=float)
|
||||
|
||||
if abs(fz) < 1e-8:
|
||||
return None
|
||||
t = -cam_pos[2] / fz
|
||||
if t < 0:
|
||||
return None
|
||||
pt = cam_pos + t * np.array([fx, fy, fz], dtype=float)
|
||||
return float(pt[0]), float(pt[1])
|
||||
|
||||
|
||||
# ===================== 仿真深度图 + 扇区 + DDA =====================
|
||||
def depth_to_sectors(
|
||||
depth: np.ndarray,
|
||||
hfov_rad: float,
|
||||
num_sectors: int,
|
||||
z_min: float = 0.1,
|
||||
z_max: float = 6.0,
|
||||
percentile: float = 5.0,
|
||||
) -> Tuple[np.ndarray, np.ndarray]:
|
||||
"""
|
||||
前向范围扫描: 在车体前方扇形区域内发射射线
|
||||
返回: (距离数组, 角度数组)
|
||||
深度图 → 1D 扇区最近距离（可直接用于真实深感相机）
|
||||
depth: H×W (m)，0/NaN 表示无效
|
||||
"""
|
||||
site_xpos = data.site_xpos[site_id].copy()
|
||||
xmat = np.array(data.xmat[body_id]).reshape(3, 3)
|
||||
forward = xmat[:, 0] # 前向 (body +X)
|
||||
H, W = depth.shape
|
||||
angles = np.linspace(-hfov_rad / 2, hfov_rad / 2, num_sectors, dtype=np.float64)
|
||||
cols = np.linspace(0, W, num_sectors + 1).astype(int)
|
||||
dists = np.full(num_sectors, z_max, dtype=np.float64)
|
||||
|
||||
angles = np.linspace(-angle_span / 2, angle_span / 2, num_rays)
|
||||
distances = np.full(num_rays, 10.0)
|
||||
for i in range(num_sectors):
|
||||
c0, c1 = cols[i], cols[i + 1]
|
||||
strip = depth[:, c0:c1].reshape(-1)
|
||||
strip = strip[np.isfinite(strip)]
|
||||
strip = strip[(strip > z_min) & (strip < z_max)]
|
||||
if strip.size == 0:
|
||||
continue
|
||||
dists[i] = float(np.percentile(strip, percentile))
|
||||
|
||||
for i, theta in enumerate(angles):
|
||||
c, s = np.cos(theta), np.sin(theta)
|
||||
ray_dir = c * forward + s * xmat[:, 1] # 绕 body Z 旋转
|
||||
ray_dir = ray_dir / (np.linalg.norm(ray_dir) + 1e-8)
|
||||
|
||||
geomid = np.array([-1], dtype=np.int32)
|
||||
d = mujoco.mj_ray(model, data, site_xpos, ray_dir, None, 1, body_id, geomid)
|
||||
if d >= 0:
|
||||
distances[i] = d
|
||||
|
||||
return distances, angles
|
||||
dists = np.minimum(dists, np.roll(dists, 1))
|
||||
dists = np.minimum(dists, np.roll(dists, -1))
|
||||
return dists, angles
|
||||
|
||||
|
||||
def obstacle_avoidance_vel(distances, angles):
|
||||
class DepthSimulator:
|
||||
"""用 mj_ray 生成仿真深度图（模拟深感相机输出）"""
|
||||
|
||||
def __init__(self, num_sectors: int, hfov_rad: float):
|
||||
self.num_sectors = int(num_sectors)
|
||||
self.angles = np.linspace(-hfov_rad / 2, hfov_rad / 2, self.num_sectors)
|
||||
c = np.cos(self.angles)
|
||||
s = np.sin(self.angles)
|
||||
self.ray_dirs_body = np.stack([c, s, np.zeros_like(c)], axis=1).astype(np.float64)
|
||||
self.depth = np.full((1, self.num_sectors), 10.0, dtype=np.float64)
|
||||
self._geomid = np.array([-1], dtype=np.int32)
|
||||
|
||||
def render(
|
||||
self,
|
||||
model: mujoco.MjModel,
|
||||
data: mujoco.MjData,
|
||||
body_id: int,
|
||||
site_id: int,
|
||||
) -> np.ndarray:
|
||||
site_xpos = data.site_xpos[site_id]
|
||||
xmat = np.array(data.xmat[body_id], dtype=np.float64).reshape(3, 3)
|
||||
ray_dirs_world = (xmat @ self.ray_dirs_body.T).T
|
||||
norms = np.linalg.norm(ray_dirs_world, axis=1) + 1e-9
|
||||
ray_dirs_world /= norms[:, None]
|
||||
|
||||
self.depth.fill(10.0)
|
||||
for i in range(self.num_sectors):
|
||||
d = mujoco.mj_ray(
|
||||
model, data,
|
||||
site_xpos, ray_dirs_world[i],
|
||||
None, 1, body_id, self._geomid
|
||||
)
|
||||
if d >= 0:
|
||||
self.depth[0, i] = float(d)
|
||||
return self.depth
|
||||
|
||||
|
||||
def dda_avoidance_from_depth(
|
||||
dists: np.ndarray,
|
||||
angles: np.ndarray,
|
||||
vx_cmd: float,
|
||||
vy_cmd: float,
|
||||
cfg: NavCfg,
|
||||
) -> Tuple[float, float, float]:
|
||||
"""
|
||||
避障速度 + 绕行分量。被挡时朝开阔方向加速，而非只排斥
|
||||
返回: (vx_avoid, vy_avoid, wz_avoid)
|
||||
Directional Distance Avoidance (DDA)
|
||||
只关心「你要去的方向」是否过近；过近则削掉该方向分量 + 朝开阔方向侧移/转向
|
||||
"""
|
||||
min_d = np.min(distances)
|
||||
if min_d > OBSTACLE_THRESHOLD:
|
||||
speed = float(np.hypot(vx_cmd, vy_cmd))
|
||||
if speed < 1e-6:
|
||||
return 0.0, 0.0, 0.0
|
||||
|
||||
vx, vy, wz = 0.0, 0.0, 0.0
|
||||
safe_sector = np.argmax(distances)
|
||||
best_theta = angles[safe_sector]
|
||||
move_theta = float(np.arctan2(vy_cmd, vx_cmd))
|
||||
N = len(dists)
|
||||
i0 = int(np.argmin(np.abs(angles - move_theta)))
|
||||
win = cfg.dda_window
|
||||
idxs = [(i0 + k) % N for k in range(-win, win + 1)]
|
||||
dmin_dir = float(np.min(dists[idxs]))
|
||||
|
||||
for d, theta in zip(distances, angles):
|
||||
if d < OBSTACLE_THRESHOLD:
|
||||
ratio = 1.0 - d / OBSTACLE_THRESHOLD
|
||||
strength = K_REPEL * (ratio ** 2)
|
||||
vx -= strength * np.cos(theta)
|
||||
vy -= strength * np.sin(theta)
|
||||
elif d < SAFE_DISTANCE:
|
||||
ratio = (SAFE_DISTANCE - d) / (SAFE_DISTANCE - OBSTACLE_THRESHOLD)
|
||||
strength = K_REPEL * 0.15 * ratio
|
||||
wz += strength * np.clip(best_theta - theta, -1, 1)
|
||||
best_i = int(np.argmax(dists))
|
||||
best_theta = float(angles[best_i])
|
||||
|
||||
# 绕行：朝最开阔方向加正向速度，避免只退不绕
|
||||
bypass = 0.35
|
||||
vx += bypass * np.cos(best_theta)
|
||||
vy += bypass * np.sin(best_theta)
|
||||
wz += 0.4 * best_theta
|
||||
stop_dist = cfg.obstacle_threshold
|
||||
safe_dist = cfg.safe_distance
|
||||
if hasattr(cfg, "speed_adaptive_k") and cfg.speed_adaptive_k > 0:
|
||||
safe_dist = safe_dist + cfg.speed_adaptive_k * speed
|
||||
stop_dist = stop_dist + 0.5 * cfg.speed_adaptive_k * speed
|
||||
|
||||
return vx, vy, wz
|
||||
if dmin_dir >= safe_dist:
|
||||
return 0.0, 0.0, 0.0
|
||||
|
||||
danger = (safe_dist - dmin_dir) / max(safe_dist - stop_dist, 1e-6)
|
||||
danger = float(np.clip(danger, 0.0, 1.0))
|
||||
|
||||
vx_a = 0.0
|
||||
vy_a = 0.0
|
||||
wz_a = 0.0
|
||||
|
||||
mx = float(np.cos(move_theta))
|
||||
my = float(np.sin(move_theta))
|
||||
proj = vx_cmd * mx + vy_cmd * my
|
||||
cut = (0.6 + 0.4 * danger) * proj
|
||||
vx_a -= cut * mx
|
||||
vy_a -= cut * my
|
||||
|
||||
bypass = 0.25 + 0.25 * danger
|
||||
vx_a += bypass * np.cos(best_theta)
|
||||
vy_a += bypass * np.sin(best_theta)
|
||||
wz_a += (0.35 + 0.45 * danger) * best_theta
|
||||
|
||||
if dmin_dir < stop_dist:
|
||||
vx_a -= vx_cmd * 0.9
|
||||
vy_a -= vy_cmd * 0.9
|
||||
wz_a += 0.6 * np.sign(best_theta if abs(best_theta) > 1e-3 else 1.0)
|
||||
|
||||
return vx_a, vy_a, wz_a
|
||||
|
||||
|
||||
def goal_attraction_vel(x, y, yaw, goal_x, goal_y):
|
||||
# ===================== 射线扫描（旧接口，DDA 下可弃用） =====================
|
||||
class RayScanner:
|
||||
"""
|
||||
目标吸引力: 在车体坐标系下计算朝目标的 vx, vy, wz
|
||||
优先保持线速度，角速度仅用于微调朝向
|
||||
预计算扇形扫描的 body-frame 方向（基于 forward=[1,0,0], left=[0,1,0]），
|
||||
每帧用 body rotation (xmat) 转到 world，减少每帧 cos/sin + 临时数组。
|
||||
"""
|
||||
dx = goal_x - x
|
||||
dy = goal_y - y
|
||||
dist = np.sqrt(dx * dx + dy * dy) + 1e-6
|
||||
|
||||
# 世界系下期望方向
|
||||
gx_w = dx / dist
|
||||
gy_w = dy / dist
|
||||
def __init__(self, num_rays: int, angle_span: float):
|
||||
self.num_rays = int(num_rays)
|
||||
self.angle_span = float(angle_span)
|
||||
|
||||
# 转到车体系
|
||||
c, s = np.cos(-yaw), np.sin(-yaw)
|
||||
gx_b = c * gx_w - s * gy_w
|
||||
gy_b = s * gx_w + c * gy_w
|
||||
self.angles = np.linspace(-self.angle_span / 2, self.angle_span / 2, self.num_rays)
|
||||
c = np.cos(self.angles)
|
||||
s = np.sin(self.angles)
|
||||
|
||||
# 线速度：朝目标，随距离平滑
|
||||
scale = np.tanh(dist) * 0.8 + 0.2
|
||||
vx = K_ATTRACT * scale * gx_b
|
||||
vy = K_ATTRACT * scale * gy_b
|
||||
# body-frame ray directions: dir_b = c*[1,0,0] + s*[0,1,0]
|
||||
# shape (N,3)
|
||||
self.ray_dirs_body = np.stack([c, s, np.zeros_like(c)], axis=1).astype(np.float64)
|
||||
|
||||
# 角速度：弱增益，避免只转不走
|
||||
target_yaw = np.arctan2(dy, dx)
|
||||
yaw_err = np.arctan2(np.sin(target_yaw - yaw), np.cos(target_yaw - yaw))
|
||||
wz = K_YAW_GOAL * np.tanh(yaw_err)
|
||||
# buffers to avoid per-step alloc
|
||||
self.distances = np.full(self.num_rays, 10.0, dtype=np.float64)
|
||||
self._geomid = np.array([-1], dtype=np.int32)
|
||||
|
||||
return vx, vy, wz
|
||||
|
||||
|
||||
def blend_and_clamp(vx_a, vy_a, wz_a, vx_g, vy_g, wz_g, dist_to_goal, min_d, stuck):
|
||||
"""融合避障与目标速度，限幅，卡住时强制最小速度"""
|
||||
vx = vx_a + vx_g
|
||||
vy = vy_a + vy_g
|
||||
wz = wz_a + wz_g
|
||||
|
||||
lin = np.sqrt(vx * vx + vy * vy)
|
||||
# 未到目标且线速度过小：强制最小速度
|
||||
if dist_to_goal > GOAL_THRESHOLD and lin < MIN_LINEAR:
|
||||
if lin > 1e-6:
|
||||
vx *= MIN_LINEAR / lin
|
||||
vy *= MIN_LINEAR / lin
|
||||
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)
|
||||
return vx, vy, wz
|
||||
def scan(
|
||||
self,
|
||||
model: mujoco.MjModel,
|
||||
data: mujoco.MjData,
|
||||
body_id: int,
|
||||
site_id: int,
|
||||
) -> Tuple[np.ndarray, np.ndarray]:
|
||||
site_xpos = data.site_xpos[site_id] # view, no copy
|
||||
xmat = np.array(data.xmat[body_id], dtype=np.float64).reshape(3, 3)
|
||||
|
||||
# world dirs: dir_w = xmat @ dir_b
|
||||
# shape (N,3)
|
||||
ray_dirs_world = (xmat @ self.ray_dirs_body.T).T
|
||||
|
||||
# normalize (理论上已单位，但数值上更稳一点)
|
||||
norms = np.linalg.norm(ray_dirs_world, axis=1) + 1e-9
|
||||
ray_dirs_world /= norms[:, None]
|
||||
|
||||
self.distances.fill(10.0)
|
||||
for i in range(self.num_rays):
|
||||
d = mujoco.mj_ray(
|
||||
model, data,
|
||||
site_xpos, ray_dirs_world[i],
|
||||
None, 1, body_id, self._geomid
|
||||
)
|
||||
if d >= 0:
|
||||
self.distances[i] = float(d)
|
||||
|
||||
return self.distances, self.angles
|
||||
|
||||
|
||||
# ===================== 速度融合与滤波 =====================
|
||||
class VelocityFilter:
|
||||
"""EMA 滤波 + 速率限制，平滑控制输出"""
|
||||
"""EMA + 速率限制，输出更平滑，避免 ctrl 抖动。"""
|
||||
|
||||
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 __init__(self, alpha: float, max_dv: float, max_dw: float):
|
||||
self.alpha = float(alpha)
|
||||
self.max_dv = float(max_dv)
|
||||
self.max_dw = float(max_dw)
|
||||
self.vx = 0.0
|
||||
self.vy = 0.0
|
||||
self.wz = 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
|
||||
def reset(self):
|
||||
self.vx = self.vy = self.wz = 0.0
|
||||
|
||||
# 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)
|
||||
def update(self, vx_cmd: float, vy_cmd: float, wz_cmd: float) -> Tuple[float, float, float]:
|
||||
vx_f = self.alpha * self.vx + (1.0 - self.alpha) * vx_cmd
|
||||
vy_f = self.alpha * self.vy + (1.0 - self.alpha) * vy_cmd
|
||||
wz_f = self.alpha * self.wz + (1.0 - self.alpha) * wz_cmd
|
||||
|
||||
dvx = float(np.clip(vx_f - self.vx, -self.max_dv, self.max_dv))
|
||||
dvy = float(np.clip(vy_f - self.vy, -self.max_dv, self.max_dv))
|
||||
dwz = float(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
|
||||
|
||||
|
||||
def goal_attraction_vel(x: float, y: float, yaw: float, goal_x: float, goal_y: float, cfg: NavCfg):
|
||||
"""
|
||||
大角度：先转向后前进；小角度：前进时顺带转向
|
||||
"""
|
||||
dx = goal_x - x
|
||||
dy = goal_y - y
|
||||
dist = float(np.hypot(dx, dy)) + 1e-6
|
||||
|
||||
target_yaw = float(np.arctan2(dy, dx))
|
||||
yaw_err = wrap_pi(target_yaw - yaw)
|
||||
yaw_err_abs = float(np.abs(yaw_err))
|
||||
|
||||
thresh_rad = np.deg2rad(cfg.yaw_turn_first_deg)
|
||||
|
||||
# 线速度：大角度时抑制，小角度时正常
|
||||
gx_w = dx / dist
|
||||
gy_w = dy / dist
|
||||
c = np.cos(-yaw)
|
||||
s = np.sin(-yaw)
|
||||
gx_b = c * gx_w - s * gy_w
|
||||
gy_b = s * gx_w + c * gy_w
|
||||
scale = float(np.tanh(dist) * 0.8 + 0.2)
|
||||
vx_raw = cfg.k_attract * scale * gx_b
|
||||
vy_raw = cfg.k_attract * scale * gy_b
|
||||
|
||||
if yaw_err_abs > thresh_rad:
|
||||
# 大角度：先转，几乎不走（留 0.05 防止完全不动时滤波器卡死）
|
||||
linear_scale = 0.05
|
||||
else:
|
||||
# 小角度：边走边调，角度越小线速越足
|
||||
linear_scale = 1.0 - 0.85 * (yaw_err_abs / thresh_rad) ** 2
|
||||
|
||||
vx = vx_raw * linear_scale
|
||||
vy = vy_raw * linear_scale
|
||||
|
||||
# 角速度：大角度时略加强，小角度时弱增益边走边调
|
||||
if yaw_err_abs > thresh_rad:
|
||||
wz = cfg.k_yaw_goal * 1.3 * float(np.tanh(yaw_err))
|
||||
else:
|
||||
wz = cfg.k_yaw_goal * 0.7 * float(np.tanh(yaw_err))
|
||||
|
||||
return vx, vy, wz
|
||||
|
||||
|
||||
def blend_and_clamp(
|
||||
vx_a: float, vy_a: float, wz_a: float,
|
||||
vx_g: float, vy_g: float, wz_g: float,
|
||||
dist_to_goal: float,
|
||||
stuck: bool,
|
||||
cfg: NavCfg,
|
||||
dists: Optional[np.ndarray] = None,
|
||||
angles: Optional[np.ndarray] = None,
|
||||
):
|
||||
vx = vx_a + vx_g
|
||||
vy = vy_a + vy_g
|
||||
wz = wz_a + wz_g
|
||||
|
||||
lin = float(np.hypot(vx, vy))
|
||||
|
||||
# 前方扇区限速：只用前方 ±20° 最小距离
|
||||
if dists is not None and angles is not None and len(dists) > 0:
|
||||
front = np.abs(angles) < np.deg2rad(20)
|
||||
if np.any(front):
|
||||
d_front = float(np.min(dists[front]))
|
||||
ratio = np.clip(
|
||||
(d_front - cfg.obstacle_threshold) / max(cfg.safe_distance - cfg.obstacle_threshold, 1e-6),
|
||||
0.0, 1.0
|
||||
)
|
||||
speed_limit = ratio * cfg.max_linear
|
||||
if lin > speed_limit + 1e-6:
|
||||
s = speed_limit / lin
|
||||
vx *= s
|
||||
vy *= s
|
||||
lin = float(np.hypot(vx, vy))
|
||||
|
||||
# 强制最小速度：未到目标且输出太小
|
||||
if dist_to_goal > cfg.goal_threshold and lin < cfg.min_linear:
|
||||
if lin > 1e-6:
|
||||
s = cfg.min_linear / lin
|
||||
vx *= s
|
||||
vy *= s
|
||||
else:
|
||||
# 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
|
||||
|
||||
|
||||
# ===================== 主程序 =====================
|
||||
def main():
|
||||
model = mujoco.MjModel.from_xml_path(SCENE_PATH)
|
||||
data = mujoco.MjData(model)
|
||||
@@ -199,160 +442,150 @@ 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()
|
||||
|
||||
hfov_rad = np.deg2rad(CFG.hfov_deg)
|
||||
depth_sim = DepthSimulator(CFG.num_sectors, hfov_rad)
|
||||
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：仿真深度图 + DDA 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)
|
||||
|
||||
# ========== 仿真深度图 → 扇区 → DDA ==========
|
||||
depth_img = depth_sim.render(model, data, body_id, site_id)
|
||||
dists, angles = depth_to_sectors(
|
||||
depth_img, hfov_rad, CFG.num_sectors,
|
||||
CFG.depth_z_min, CFG.depth_z_max, CFG.depth_percentile
|
||||
)
|
||||
|
||||
# ========== 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_g, vy_g, wz_g = goal_attraction_vel(x, y, yaw, goal_x, goal_y, CFG)
|
||||
vx_a, vy_a, wz_a = dda_avoidance_from_depth(dists, angles, vx_g, vy_g, 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,
|
||||
dists=dists, angles=angles
|
||||
)
|
||||
|
||||
# ========== 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()
|
||||
|
||||
|
||||
Reference in New Issue
Block a user