多旋翼物流无人机节能轨迹规划（Python代码实现）

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💥1 概述

多旋翼物流无人机的节能轨迹规划是一项重要的技术，可以有效减少无人机的能量消耗，延长飞行时间，提高物流效率。下面是一些常见的节能轨迹规划方法：

总之，节能轨迹规划为多旋翼物流无人机提供了较大的优化空间，通过合理规划飞行路径、优化充电策略以及使用新能源技术，可以显著减少能量消耗，提高物流效率。

本文考虑静态环境下无人机轨迹轨迹的可行性和能耗特性。

📚2 运行结果

部分代码：

def VelDataAboutTime():
    blocks = []
    b1 = Block(0, 0, 0, 150, 200, 200)   # (x1, y1, z1, x2, y2, z2)
    b2 = Block(100, 150, 120, 300, 400, 450)  # (x1, y1, z1, x2, y2, z2)
    b3 = Block(250, 350, 400, 500, 480, 500)   # (x1, y1, z1, x2, y2, z2)
    b4 = Block(420, 220, 200, 650, 400, 450)   # (x1, y1, z1, x2, y2, z2)
    b5 = Block(550, 80, 150, 650, 400, 300)   # (x1, y1, z1, x2, y2, z2)
    b6 = Block(600, 80, 50, 800, 150, 200)   # (x1, y1, z1, x2, y2, z2)
    blocks.append(b1)
    blocks.append(b2)
    blocks.append(b3)
    blocks.append(b4)
    blocks.append(b5)
    blocks.append(b6)
    block2Ds = []
    for b in blocks:
        block2Ds.append(Block2D(b.x1, b.y1, b.x2, b.y2))
    goal = [800, 100, 60]
    c_x = []  # 每段 (x1, x2)
    c_y = []  # 每段 (y1, y2)
    c_z = []  # 每段 (z1, z2)
    corridor = []
    for block in blocks:
        c_x.append([block.x1, block.x2])  # 提取出每一段的 (x1,x2)
        c_y.append([block.y1, block.y2])  # 提取出每一段的 (y1,y2)
        c_z.append([block.z1, block.z2])  # 提取出每一段的 (z1,z2)
    corridor.append(c_x)
    corridor.append(c_y)
    corridor.append(c_z)

    time = [
        [13, 21, 9, 12, 12, 13],
        [16, 23, 10, 13, 13, 15],
        [18, 25, 11, 15, 15, 16],
        [20, 27, 13, 16, 16, 18],
        [22, 29, 14, 18, 18, 19]
    ]

    # time = [13, 21, 9, 12, 12, 13]  # 80  2.68677585e+04
    # time = [16, 23, 10, 13, 13, 15] # 90  2.88795396e+04
    # time = [18, 25, 11, 15, 15, 16]  # 100  3.10684295e+04
    # time = [20, 27, 13, 16, 16, 18]  # 110   3.33565508e+04
    # time = [22, 29, 14, 18, 18, 19]  # 120  3.57001138e+04

    for i in range(5):
        print("============================================")
        energy, power, s, vel = UAV3D(time[i], goal, corridor)
        print(energy)

        """ save vel to excel """
        vel_x = list(np.array(vel[0]).flatten())
        vel_y = list(np.array(vel[1]).flatten())
        vel_z = list(np.array(vel[2]).flatten())

        for index in range(len(vel_x)):
            velocity = math.sqrt(vel_x[index] ** 2 + vel_y[index] ** 2 + vel_z[index] ** 2)
            CVXsheet.write(index, i, velocity)
    workbook.save('Velocity.xls')

def plot_blocks(blocks):
    plt.figure(1)
    ax = plt.axes(projection='3d')
    ax.set_xlabel('X(m)')
    ax.set_ylabel('Y(m)')
    ax.set_zlabel('Z(m)')
    # ax.set_xticks(np.linspace(0, 100, 4))
    # ax.set_yticks(np.linspace(0, 100, 4))
    # ax.set_zticks(np.linspace(0, 100, 4))
    ax.set_xlim(0, 1000)
    ax.set_ylim(0, 1000)

3 Python代码实现

🎉4 参考文献

部分理论来源于网络，如有侵权请联系删除。

[1]Wu Kunpeng (2022) Energy-Efficient Trajectory Planning for Multi-rotor Logistics UAVs