Research on Kinematics Analysis and Positioning System of Double Steering-wheel Parking Robot
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摘要: 针对泊车机器人定位特点, 使用惯导、里程计、二维码模块, 基于Visual C++平台开发了一种泊车机器人定位系统。针对双舵轮泊车机器人构型, 采用速度-几何法建立了机器人运动学模型, 在此基础上, 推导了惯导传感器数据的姿态变换矩阵, 建立了里程计传感器运动模型, 结合运动学航位推算与卡尔曼滤波方法, 提出了一种多传感器组合定位方法。泊车机器人定位实验表明, 应用多传感器组合定位方法的泊车机器人可以较好地实现机器人稳定工作, 相比航位推算定位方法, 其平均定位偏差降低了92%, 达到14.04 mm, 定位精度显著提高, 可以较好地满足泊车机器人定位需求。Abstract: Aiming at the positioning feature of parking robot, by using inertial navigation, odometer and two-dimensional code module, a parking robot positioning system was developed based on Visual C + + platform. The speed geometry method was adopted and the robot kinematic model was established. On this basis, the attitude transformation matrix of inertial navigation sensor data was derived, and the odometer sensor motion model was established. Combined Kalman filter method with kinematic dead reckoning, a multi-sensor combined positioning method was proposed. The positioning experiment of parking robot shows that, the parking robot that adopts multi-sensor combined positioning method can better realize the operation; Compared with dead reckoning positioning method, its average positioning deviation is reduced by 92%, reaching 14.04 mm, and the positioning accuracy is significantly improved, which can better meet the positioning requirements of parking robot.
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Key words:
- parking robot /
- kinematics /
- mobile robot positioning /
- speed geometry method
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表 1 运动学航位推算定位方法实验数据
Table 1. Experimental data of kinematic dead reckoning positioning method
实测坐标 计算坐标 误差d/mm X/mm Y/mm X/mm Y/mm 0.01 -1.19 0.00 0.00 1.19 0.43 -695.08 -0.64 -700.66 5.68 2.01 -1 399.31 -1.33 -1 373.73 25.80 4.37 -2 104.78 -2.00 -2 047.07 58.06 4.85 -2 809.72 -2.62 -2 720.80 89.24 4.48 -3 514.16 -3.31 -3 394.15 120.26 3.90 -4 218.63 -3.94 -4 066.94 151.90 4.17 -4 923.16 -4.48 -4 740.78 182.58 4.73 -5 627.95 -5.11 -5 413.98 214.20 5.99 -6 333.23 -5.68 -6 087.69 245.82 7.83 -6 637.20 -6.03 -6 491.74 146.12 447.03 -6 633.10 263.83 -6 491.99 231.24 1 150.98 -6 633.22 937.00 -6 492.72 255.99 1 853.42 -6 632.98 1 610.57 -6 493.51 280.06 2 490.43 -6 634.39 2 284.48 -6 494.05 249.22 2 490.32 -6 634.39 2 418.81 -6 494.26 157.32 2 490.33 -6 634.39 2 418.81 -6 494.26 157.32 2 483.62 -6 624.79 2 417.48 -6 493.82 146.72 2 489.64 -4 514.31 2 419.13 -4 650.77 153.60 2 489.38 -3 810.98 2 419.79 -3 977.71 180.67 2 488.39 -3 105.48 2 420.38 -3 304.12 209.95 2 487.89 -2 400.05 2 421.04 -2 631.19 240.60 2 489.14 -1 694.51 2 421.63 -1 957.33 271.35 2 489.51 -988.93 2 422.35 -1 283.84 302.46 2 487.83 -282.71 2 423.05 -610.63 334.25 表 2 多传感器组合定位方法实验数据
Table 2. Experimental data of multi-sensor combined positioning method
实测坐标 计算坐标 误差d/mm X/mm Y/mm X/mm Y/mm -0.02 0.01 0.00 0.00 0.29 5.71 -813.90 2.85 -828.41 15.04 14.40 -1 800.33 12.34 -1 817.10 17.14 22.35 -2 787.31 20.54 -2 801.02 14.08 27.90 -3 773.44 26.34 -3 789.14 16.02 34.73 -4 759.40 35.36 -4 776.09 16.92 41.77 -5 745.98 44.99 -5 766.02 20.49 49.62 -6 591.78 50.99 -6 608.03 16.52 206.50 -6 656.84 222.54 -6 655.94 15.86 772.61 -6 655.64 782.30 -6 664.49 13.13 1 333.01 -6 652.03 1 344.76 - 6 670.20 21.72 1 894.90 -6 648.24 1 904.85 -6 660.54 15.88 2 509.28 -6 645.60 2 505.80 -6 669.92 24.82 2 504.31 -5 756.20 2 498.52 -5 774.02 19.01 2 498.92 -4 769.71 2 494.07 -4 787.60 18.81 2 492.97 -3 785.05 2 487.91 -3 799.48 15.57 2 484.93 -2 797.57 2 481.89 -2 822.93 25.79 2 480.36 -1 809.09 2 475.80 -1 825.96 17.74 2 475.23 -821.64 2 472.53 -844.66 23.43 2 468.92 25.08 2 466.59 2.58 22.86 -
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