【自动驾驶】路径规划—— Dubins 曲线公式总结及python代码实现(基于几何的方法)

文章目录

  • 参考资料
  • 1. Dubins 曲线计算
    • 1.1 坐标变换
    • 1.2 LSL路径
    • 1.3 RSR路径
    • 1.4 RSL路径
    • 1.5 LSR路径
    • 1.6 RLR路径
    • 1.7 LRL路径

参考资料

  • Classification of the Dubins set
  • Dubins 曲线计算笔记
  • Dubins path planning

前文

Dubins 曲线推导(基于向量的方法)

前文已经从向量的角度进行了Dubins曲线公式的推导,这篇博客主要基于这篇论文《Classification of the Dubins set》介绍基于几何的关系下的dubins曲线公式,论文每一种情况都有详细的推导,因此这里只给出每种情况的公式,推导过程大家参考论文即可。


1. Dubins 曲线计算

dubins路径集合为 { L S L , R S R , R S L , L S R , R L R , L R L } \{LSL, RSR, RSL, LSR, RLR, LRL\} {LSLRSRRSLLSRRLRLRL} L L L表示向左转的圆弧运动, R R R表示向右转的圆弧运动, S S S表示沿直线运动。

1.1 坐标变换

设起点为 s ( x i , y i , α i ) s\left(x_{i}, y_{i}, \alpha_{i}\right) s(xi,yi,αi) ,终点为 g ( x g , y g , β g ) g\left(x_{g}, y_{g}, \beta_{g}\right) g(xg,yg,βg) , 先坐标变换将起点平移至原点,并旋转 θ \theta θ 角,则终点也落在 x \mathrm{x} x 轴上,起点和终点的坐标为 s ( 0 , 0 , α ) , g ( d , 0 , β ) s(0,0, \alpha), g(d, 0, \beta) s(0,0,α),g(d,0,β) ,其中:
θ = atan ⁡ 2 ( y g − y i x g − x i )   m o d   { 2 π } D = ( x i − x g ) 2 + ( y i − y g ) 2 d = D / R α = ( α i − θ )   m o d   { 2 π } β = ( β g − θ )   m o d   { 2 π } (1) \tag{1} \begin{gathered} \theta=\operatorname{atan} 2\left(\frac{y_{g}-y_{i}}{x_{g}-x_{i}}\right) \bmod\{2\pi\} \\ D=\sqrt{\left(x_{i}-x_{g}\right)^{2}+\left(y_{i}-y_{g}\right)^{2}} \\ d=D / R \\ \alpha= \left(\alpha_{i}-\theta\right) \bmod\{2\pi\} \\ \beta= \left(\beta_{g}-\theta\right) \bmod\{2\pi\} \end{gathered} θ=atan2(xgxiygyi)mod{2π}D=(xixg)2+(yiyg)2 d=D/Rα=(αiθ)mod{2π}β=(βgθ)mod{2π}(1)

几点说明

  • 其中 θ \theta θ 为起点和终点航向角度差,上面的角度都在 [ 0 , 2 π ] [0,2 \pi] [0,2π] 之间。

  • 上面用 D D D 除上 R R R这样处理可以使每个最小转弯半径 R R R都为 1 ,由角度计算弧长时更方便,弧长即等于角度的弧度,因此在后面看到的 cos ⁡ ( α ) \cos (\alpha) cos(α) ,其实是前面省略了 R R R

  • m o d ( ) mod() mod()是取模运算,例如:   m o d   ( 3 π , 2 π ) = 3 π   m o d   2 π = π \bmod(3\pi,2\pi)=3\pi \bmod 2 \pi=\pi mod(3π,2π)=3πmod2π=π。下述的 β (   m o d   2 π ) \beta(\bmod 2 \pi) β(mod2π)也是这个意思,即 β \beta β 2 π 2\pi 2π取模。python实现方式很简单,如下:

    def mod2pi(theta):
        """对2pi取模运算
        """
        return theta - 2.0 * math.pi * math.floor(theta / 2.0 / math.pi)
    
    

坐标变换的python实现很简单,如下:

    # 坐标变换
    dx = g_x-s_x
    dy = g_y-s_y
    D = math.hypot(dx, dy)
    d = D * curvature

    theta = mod2pi(math.atan2(dy, dx))
    alpha = mod2pi(s_yaw - theta)
    beta = mod2pi(g_yaw - theta)

设车辆在起始圆上走过的长度为 t t t ,直线段长度为 p p p ,第二个圆上的圆弧长度为 q q q,整个路径长度 L = t + p + q L=t+p+q L=t+p+q。下面依次给出6种情况的轨迹公式。

1.2 LSL路径

【自动驾驶】路径规划—— Dubins 曲线公式总结及python代码实现(基于几何的方法)_第1张图片

L S L LSL LSL路径的轨迹长度公式如下:
t l s l = − α + arctan ⁡ cos ⁡ β − cos ⁡ α d + sin ⁡ α − sin ⁡ β {   m o d   2 π } p l s l = 2 + d 2 − 2 cos ⁡ ( α − β ) + 2 d ( sin ⁡ α − sin ⁡ β ) q l s l = β − arctan ⁡ cos ⁡ β − cos ⁡ α d + sin ⁡ α − sin ⁡ β {   m o d   2 π } (2) \tag{2} \begin{aligned} &t_{l s l}=-\alpha+\arctan \frac{\cos \beta-\cos \alpha}{d+\sin \alpha-\sin \beta}\{\bmod 2 \pi\} \\ &p_{l s l}=\sqrt{2+d^{2}-2 \cos (\alpha-\beta)+2 d(\sin \alpha-\sin \beta)} \\ &q_{l s l}=\beta-\arctan \frac{\cos \beta-\cos \alpha}{d+\sin \alpha-\sin \beta}\{\bmod 2 \pi\} \end{aligned} tlsl=α+arctand+sinαsinβcosβcosα{mod2π}plsl=2+d22cos(αβ)+2d(sinαsinβ) qlsl=βarctand+sinαsinβcosβcosα{mod2π}(2)
总长度等于:
L l s l = t l s l + p l s l + q l s l = − α + β + p l s l (3) \tag{3} \mathcal{L}_{l s l}=t_{l s l}+p_{l s l}+q_{l s l}=-\alpha+\beta+p_{l s l} Llsl=tlsl+plsl+qlsl=α+β+plsl(3)

python实现如下

def left_straight_left(alpha, beta, d):
    """LSL路径
    """
    sa = math.sin(alpha)
    sb = math.sin(beta)
    ca = math.cos(alpha)
    cb = math.cos(beta)
    c_ab = math.cos(alpha - beta)

    tmp0 = d + sa - sb

    mode = ["L", "S", "L"]
    p_squared = 2 + (d * d) - (2 * c_ab) + (2 * d * (sa - sb))
    if p_squared < 0:
        return None, None, None, mode
    tmp1 = math.atan2((cb - ca), tmp0)
    t = mod2pi(-alpha + tmp1)
    p = math.sqrt(p_squared)
    q = mod2pi(beta - tmp1)

    return t, p, q, mode

1.3 RSR路径

【自动驾驶】路径规划—— Dubins 曲线公式总结及python代码实现(基于几何的方法)_第2张图片

R S R RSR RSR路径的轨迹长度公式如下:

t r s r = α − arctan ⁡ cos ⁡ α − cos ⁡ β d − sin ⁡ α + sin ⁡ β {   m o d   2 π } p r s r = 2 + d 2 − 2 cos ⁡ ( α − β ) + 2 d ( sin ⁡ β − sin ⁡ α ) q r s r = − β (   m o d   2 π ) + arctan ⁡ cos ⁡ α − cos ⁡ β d − sin ⁡ α + sin ⁡ β {   m o d   2 π } (4) \tag{4} \begin{aligned} t_{r s r} &=\alpha-\arctan \frac{\cos \alpha-\cos \beta}{d-\sin \alpha+\sin \beta}\{\bmod 2 \pi\} \\ p_{r s r} &=\sqrt{2+d^{2}-2 \cos (\alpha-\beta)+2 d(\sin \beta-\sin \alpha)} \\ q_{r s r} &=-\beta(\bmod 2 \pi)+\arctan \frac{\cos \alpha-\cos \beta}{d-\sin \alpha+\sin \beta}\{\bmod 2 \pi\} \\ \end{aligned} trsrprsrqrsr=αarctandsinα+sinβcosαcosβ{mod2π}=2+d22cos(αβ)+2d(sinβsinα) =β(mod2π)+arctandsinα+sinβcosαcosβ{mod2π}(4)

总长度等于:
L r s r = t r s r + p r s r + q r s r = α − β + p r s r (5) \tag{5} \mathcal{L}_{r s r} =t_{r s r}+p_{r s r}+q_{r s r}=\alpha-\beta+p_{r s r} Lrsr=trsr+prsr+qrsr=αβ+prsr(5)

python实现如下

def right_straight_right(alpha, beta, d):
    """RSR路径
    """
    sa = math.sin(alpha)
    sb = math.sin(beta)
    ca = math.cos(alpha)
    cb = math.cos(beta)
    c_ab = math.cos(alpha - beta)

    tmp0 = d - sa + sb
    mode = ["R", "S", "R"]
    p_squared = 2 + (d * d) - (2 * c_ab) + (2 * d * (sb - sa))
    if p_squared < 0:
        return None, None, None, mode
    tmp1 = math.atan2((ca - cb), tmp0)
    t = mod2pi(alpha - tmp1)
    p = math.sqrt(p_squared)
    q = mod2pi(-mod2pi(beta) + tmp1)

    return t, p, q, mode

1.4 RSL路径

【自动驾驶】路径规划—— Dubins 曲线公式总结及python代码实现(基于几何的方法)_第3张图片

R S L RSL RSL路径的轨迹长度公式如下:

t r s l = α − arctan ⁡ ( cos ⁡ α + cos ⁡ β d − sin ⁡ α − sin ⁡ β ) + arctan ⁡ ( 2 p r s l ) {   m o d   2 π } p r s l = d 2 − 2 + 2 cos ⁡ ( α − β ) − 2 d ( sin ⁡ α + sin ⁡ β ) q r s l = β (   m o d   2 π ) − arctan ⁡ ( cos ⁡ α + cos ⁡ β d − sin ⁡ α − sin ⁡ β ) + arctan ⁡ ( 2 p r s l ) {   m o d   2 π } (6) \tag{6} \begin{aligned} t_{r s l} &=\alpha-\arctan \left(\frac{\cos \alpha+\cos \beta}{d-\sin \alpha-\sin \beta}\right)+\arctan \left(\frac{2}{p_{r s l}}\right)\{\bmod 2 \pi\} \\ p_{r s l} &=\sqrt{d^{2}-2+2 \cos (\alpha-\beta)-2 d(\sin \alpha+\sin \beta)} \\ q_{r s l} &=\beta(\bmod 2 \pi)-\arctan \left(\frac{\cos \alpha+\cos \beta}{d-\sin \alpha-\sin \beta}\right)+\arctan \left(\frac{2}{p_{r s l}}\right)\{\bmod 2 \pi\} \\ \end{aligned} trslprslqrsl=αarctan(dsinαsinβcosα+cosβ)+arctan(prsl2){mod2π}=d22+2cos(αβ)2d(sinα+sinβ) =β(mod2π)arctan(dsinαsinβcosα+cosβ)+arctan(prsl2){mod2π}(6)

总长度等于:
L r s l = t r s l + p r s l + q r s l = − α + β + 2 t r s l + p r s l (7) \tag{7} \mathcal{L}_{r s l} =t_{r s l}+p_{r s l}+q_{r s l}=-\alpha+\beta+2 t_{r s l}+p_{r s l} Lrsl=trsl+prsl+qrsl=α+β+2trsl+prsl(7)

python实现如下

def right_straight_left(alpha, beta, d):
    """RSL路径
    """
    sa = math.sin(alpha)
    sb = math.sin(beta)
    ca = math.cos(alpha)
    cb = math.cos(beta)
    c_ab = math.cos(alpha - beta)

    p_squared = (d * d) - 2 + (2 * c_ab) - (2 * d * (sa + sb))
    mode = ["R", "S", "L"]
    if p_squared < 0:
        return None, None, None, mode
    p = math.sqrt(p_squared)
    tmp2 = math.atan2((ca + cb), (d - sa - sb)) - math.atan2(2.0, p)
    t = mod2pi(alpha - tmp2)
    q = mod2pi(mod2pi(beta) - tmp2)

    return t, p, q, mode

1.5 LSR路径

【自动驾驶】路径规划—— Dubins 曲线公式总结及python代码实现(基于几何的方法)_第4张图片

L S R LSR LSR路径的轨迹长度公式如下:

t l s r = ( − α + arctan ⁡ ( − cos ⁡ α − cos ⁡ β d + sin ⁡ α + sin ⁡ β ) − arctan ⁡ ( − 2 p l s r ) ) {   m o d   2 π } p l s r = − 2 + d 2 + 2 cos ⁡ ( α − β ) + 2 d ( sin ⁡ α + sin ⁡ β ) q l s r = − β (   m o d   2 π ) + arctan ⁡ ( − cos ⁡ α − cos ⁡ β d + sin ⁡ α + sin ⁡ β ) − arctan ⁡ ( − 2 p l s r ) {   m o d   2 π } (8) \tag{8} \begin{aligned} t_{l s r} &=\left(-\alpha+\arctan \left(\frac{-\cos \alpha-\cos \beta}{d+\sin \alpha+\sin \beta}\right)-\arctan \left(\frac{-2}{p_{l s r}}\right)\right)\{\bmod 2 \pi\} \\ p_{l s r} &=\sqrt{-2+d^{2}+2 \cos (\alpha-\beta)+2 d(\sin \alpha+\sin \beta)} \\ q_{l s r} &=-\beta(\bmod 2 \pi)+\arctan \left(\frac{-\cos \alpha-\cos \beta}{d+\sin \alpha+\sin \beta}\right)-\arctan \left(\frac{-2}{p_{l s r}}\right)\{\bmod 2 \pi\} \\ \end{aligned} tlsrplsrqlsr=(α+arctan(d+sinα+sinβcosαcosβ)arctan(plsr2)){mod2π}=2+d2+2cos(αβ)+2d(sinα+sinβ) =β(mod2π)+arctan(d+sinα+sinβcosαcosβ)arctan(plsr2){mod2π}(8)

总长度等于:
L l s r = t l s r + p l s r + q l s r = α − β + 2 t l s r + p l s r (9) \tag{9} \mathcal{L}_{l s r} =t_{l s r}+p_{l s r}+q_{l s r}=\alpha-\beta+2 t_{l s r}+p_{l s r} Llsr=tlsr+plsr+qlsr=αβ+2tlsr+plsr(9)

python实现如下

def left_straight_right(alpha, beta, d):
    """LSR路径
    """
    sa = math.sin(alpha)
    sb = math.sin(beta)
    ca = math.cos(alpha)
    cb = math.cos(beta)
    c_ab = math.cos(alpha - beta)

    p_squared = -2 + (d * d) + (2 * c_ab) + (2 * d * (sa + sb))
    mode = ["L", "S", "R"]
    if p_squared < 0:
        return None, None, None, mode
    p = math.sqrt(p_squared)
    tmp2 = math.atan2((-ca - cb), (d + sa + sb)) - math.atan2(-2.0, p)
    t = mod2pi(-alpha + tmp2)
    q = mod2pi(-mod2pi(beta) + tmp2)

    return t, p, q, mode

1.6 RLR路径

【自动驾驶】路径规划—— Dubins 曲线公式总结及python代码实现(基于几何的方法)_第5张图片

R L R RLR RLR路径的轨迹长度公式如下:
t r l r = α − arctan ⁡ ( cos ⁡ α − cos ⁡ β d − sin ⁡ α + sin ⁡ β ) + p r l r 2 {   m o d   2 π } p r l r = arccos ⁡ 1 8 ( 6 − d 2 + 2 cos ⁡ ( α − β ) + 2 d ( sin ⁡ α − sin ⁡ β ) ) q r l r = α − β − t r l r + p r l r {   m o d   2 π } (10) \tag{10} \begin{aligned} &t_{r l r}=\alpha-\arctan \left(\frac{\cos \alpha-\cos \beta}{d-\sin \alpha+\sin \beta}\right)+\frac{p_{r l r}}{2}\{\bmod 2 \pi\} \\ &p_{r l r}=\arccos \frac{1}{8}\left(6-d^{2}+2 \cos (\alpha-\beta)+2 d(\sin \alpha-\sin \beta)\right) \\ &q_{r l r}=\alpha-\beta-t_{r l r}+p_{r l r}\{\bmod 2 \pi\} \\ \end{aligned} trlr=αarctan(dsinα+sinβcosαcosβ)+2prlr{mod2π}prlr=arccos81(6d2+2cos(αβ)+2d(sinαsinβ))qrlr=αβtrlr+prlr{mod2π}(10)

总长度等于:
L r l r = t r l r + p r l r + q r l r = α − β + 2 p r l r (11) \tag{11} \mathcal{L}_{r l r}=t_{r l r}+p_{r l r}+q_{r l r}=\alpha-\beta+2 p_{r l r} Lrlr=trlr+prlr+qrlr=αβ+2prlr(11)

python实现如下

def right_left_right(alpha, beta, d):
    """RLR路径
    """
    sa = math.sin(alpha)
    sb = math.sin(beta)
    ca = math.cos(alpha)
    cb = math.cos(beta)
    c_ab = math.cos(alpha - beta)

    mode = ["R", "L", "R"]
    tmp_rlr = (6.0 - d * d + 2.0 * c_ab + 2.0 * d * (sa - sb)) / 8.0
    if abs(tmp_rlr) > 1.0:
        return None, None, None, mode

    p = mod2pi(math.acos(tmp_rlr))
    t = mod2pi(alpha - math.atan2(ca - cb, d - sa + sb) + mod2pi(p / 2.0))
    q = mod2pi(alpha - beta - t + p)
    return t, p, q, mode

上述实现方式得不到最优结果,很奇怪,下面这种写法倒是可以得到最优结果:

def right_left_right(alpha, beta, d):
    """RLR路径,这部分的实现与公式不一致,改成与公式一致后反而得不到最优路径
    """
    sa = math.sin(alpha)
    sb = math.sin(beta)
    ca = math.cos(alpha)
    cb = math.cos(beta)
    c_ab = math.cos(alpha - beta)

    mode = ["R", "L", "R"]
    tmp_rlr = (6.0 - d * d + 2.0 * c_ab + 2.0 * d * (sa - sb)) / 8.0
    if abs(tmp_rlr) > 1.0:
        return None, None, None, mode

    p = mod2pi(2 * math.pi - math.acos(tmp_rlr))
    t = mod2pi(alpha - math.atan2(ca - cb, d - sa + sb) + mod2pi(p / 2.0))
    q = mod2pi(alpha - beta - t + mod2pi(p))
    return t, p, q, mode

1.7 LRL路径

【自动驾驶】路径规划—— Dubins 曲线公式总结及python代码实现(基于几何的方法)_第6张图片

L R L LRL LRL路径的轨迹长度公式如下:
t l r l = ( − α + arctan ⁡ ( − cos ⁡ α + cos ⁡ α d + sin ⁡ α − sin ⁡ β ) + p l r l 2 ) {   m o d   2 π } p l r l = arccos ⁡ 1 8 ( 6 − d 2 + 2 cos ⁡ ( α − β ) + 2 d ( sin ⁡ α − sin ⁡ β ) ) {   m o d   2 π } q l r l = β (   m o d   2 π ) − α + 2 p l r l {   m o d   2 π } (12) \tag{12} \begin{aligned} &t_{l r l}=\left(-\alpha+\arctan \left(\frac{-\cos \alpha+\cos \alpha}{d+\sin \alpha-\sin \beta}\right)+\frac{p_{l r l}}{2}\right)\{\bmod 2 \pi\} \\ &p_{l r l}=\arccos \frac{1}{8}\left(6-d^{2}+2 \cos (\alpha-\beta)+2 d(\sin \alpha-\sin \beta)\right)\{\bmod 2 \pi\} \\ &q_{l r l}=\beta(\bmod 2 \pi)-\alpha+2 p_{l r l}\{\bmod 2 \pi\} \end{aligned} tlrl=(α+arctan(d+sinαsinβcosα+cosα)+2plrl){mod2π}plrl=arccos81(6d2+2cos(αβ)+2d(sinαsinβ)){mod2π}qlrl=β(mod2π)α+2plrl{mod2π}(12)

总长度等于:
L l r l = t l r l + p l r l + q l r l = − α + β + 2 p l r l . (13) \tag{13} \mathcal{L}_{l r l}=t_{l r l}+p_{l r l}+q_{l r l}=-\alpha+\beta+2 p_{l r l} . Llrl=tlrl+plrl+qlrl=α+β+2plrl.(13)
python实现如下

def left_right_left(alpha, beta, d):
    """LRL路径
    """
    sa = math.sin(alpha)
    sb = math.sin(beta)
    ca = math.cos(alpha)
    cb = math.cos(beta)
    c_ab = math.cos(alpha - beta)

    mode = ["L", "R", "L"]
    tmp_lrl = (6.0 - d * d + 2.0 * c_ab + 2.0 * d * (- sa + sb)) / 8.0
    if abs(tmp_lrl) > 1:
        return None, None, None, mode
    p = mod2pi(math.acos(tmp_lrl))
    t = mod2pi(-alpha - math.atan2(ca - cb, d + sa - sb) + p / 2.0)
    q = mod2pi(mod2pi(beta) - alpha +2*p)

    return t, p, q, mode

同样上述实现方式得不到最优结果,下面这种写法可以得到最优结果:

def left_right_left(alpha, beta, d):
    """LRL路径,这部分的实现与公式不一致,改成与公式一致后反而得不到最优路径
    """
    sa = math.sin(alpha)
    sb = math.sin(beta)
    ca = math.cos(alpha)
    cb = math.cos(beta)
    c_ab = math.cos(alpha - beta)

    mode = ["L", "R", "L"]
    tmp_lrl = (6.0 - d * d + 2.0 * c_ab + 2.0 * d * (- sa + sb)) / 8.0
    if abs(tmp_lrl) > 1:
        return None, None, None, mode
    p = mod2pi(2 * math.pi - math.acos(tmp_lrl))
    t = mod2pi(-alpha - math.atan2(ca - cb, d + sa - sb) + p / 2.0)
    q = mod2pi(mod2pi(beta) - alpha - t + mod2pi(p))

    return t, p, q, mode

以上完整代码文件见github仓库

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