四舵轮移动机器人分段预测路径跟踪系统研究

钟浩翔; 钟磊; 焦向东

doi:10.13433/j.cnki.1003-8728.20220178

四舵轮移动机器人分段预测路径跟踪系统研究

doi: 10.13433/j.cnki.1003-8728.20220178

钟浩翔^1,,
钟磊²,
焦向东^1, ,

1.
北京石油化工学院机械工程学院，北京　102617
2.
首都航天机械公司，北京　100076

详细信息

作者简介:
钟浩翔(1997−)，硕士研究生，研究方向为移动机器人技术，1483928836@qq.com

通讯作者:
焦向东，教授，博士生导师，jiaoxiangdong@bipt.edu.cn

中图分类号: TP242.2
计量
- 文章访问数: 110
- HTML全文浏览量: 63
- PDF下载量: 42
- 被引次数: 0
出版历程
- 收稿日期: 2021-11-22
- 刊出日期: 2023-12-25

Exploring Piecewise Predictive Path Tracking Control of Four Steering Wheel Mobile Robot

1.
College of Mechanical Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, China
2.
Capital Aerospace Machinery Corporation, Beijing, 100076

摘要

摘要: 针对四舵轮移动机器人路径跟踪特点，使用工控机、运动控制卡，基于Visual C + + 平台开发了一种移动机器人路径跟踪系统。针对四舵轮移动机器人构型，采用速度-几何法建立了机器人运动学控制模型。提出了一种基于最优控制的分段预测路径跟踪算法，避免了对所需参数进行试凑的同时，降低了车体远离目标路径情况下的距离偏差最大超调与纠偏总步数。路径跟踪实验表明，应用所提出的分段预测路径跟踪算法的移动机器人可以较好地跟踪目标路径，相比未使用分段预测路径跟踪算法的多步预测控制，其位置偏差最大超调降低了29.81%，达到35.563 mm，纠偏用时降低了38.10%，达到13 s，且定位精度达到11.292 mm、0.51°，可以较好地满足四舵轮移动机器人定位需求。
- 四舵轮移动机器人 /
- 运动学 /
- 路径跟踪 /
- 分段预测控制
Abstract: According to the path trackingcharacteristics of a four steering wheel mobile robot, themobile robot’s path tracking system was developed based on the Visual C++ platform with an industrial computer and a motion control card. Based on the configuration of the four steering wheel mobile robot, its kinematics control model was established with the velocity geometry method. On this basis, a piecewise predictive path tracking algorithm based on optimal control was proposed. It not only avoids the trial of required parameters but also reduces the maximum overshoot of distance deviation and the total number of deviation correctionsteps when the vehicle body was away from its target path. The experimental results on path tracking show that the mobile robot using thepiecewise predictive path tracking algorithm can well track the target path. Compared with the multi-step predictive control that does not usethe piecewise predictive path tracking algorithm, the piecewise predictive path tracking algorithm reduces the maximum overshoot of position deviationby 29.81%, reaching 35.563 mm, the time for deviation correction by 38.10%, reaching 13 s, its positioning accuracy being 11.292 mm and 0.51°, better meeting the positioning requirements of a four steering wheel mobile robot.
- four steering wheel mobile robot /
- kinematics /
- path tracking /
- piecewise predictive control

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图 1 运动状态示意图

Figure 1. Schematic diagram of motion state

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图 2 初始偏差示意图

Figure 2. Schematic diagram of initial deviation

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图 3 路径跟踪实验流程

Figure 3. Path tracking experimental procedures

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图 4 实验环境

Figure 4. Experiment environment

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图 5 机器人实验系统软件界面

Figure 5. Interface of software of a robotic experimental systen

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图 6 路径跟踪实验结果

Figure 6. Path tracking experiment results

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表 1 分段预测跟踪算法测量数据

Table 1. Measurement data of segmented prediction tracking algorithm

采样时间/s	实测坐标/mm		距离偏差/mm
采样时间/s	X	Y	距离偏差/mm
0	0.00349	−0.07014	199.92986
1	106.00286	25.12481	225.12481
2	214.93878	35.56286	235.56286
3	324.14235	34.40946	234.40946
4	432.67459	22.52917	222.52917
5	540.09852	2.91928	202.91928
6	646.53675	−22.82002	177.17998
7	751.82725	−52.57517	147.42483
8	960.75699	−115.41268	84.58732
9	1065.94173	−144.45835	55.54165
10	1172.5166	−168.64139	31.35861
11	1280.34719	−184.07387	15.92613
12	1389.16727	−189.22472	10.77528
13	1511.88128	−188.97767	11.02233

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表 2 多步预测跟踪算法测量数据

Table 2. Measurement data of multi-step predictive tracking algorithm

采样时间/s	实测坐标/mm		距离偏差/mm
采样时间/s	X	Y	距离偏差/mm
0	0.04521	0.00328	200.00328
1	96.59258	18.75479	218.75479
2	194.81261	36.39371	236.39371
3	294.06749	46.82344	246.82344
4	393.87914	50.66718	250.66718
5	493.87692	48.6926	248.6926
6	593.78852	41.73392	241.73392
7	693.43019	30.63436	230.63436
8	792.69534	16.20659	216.20659
9	891.54410	−0.79079	199.20921
10	989.99182	−19.66355	180.33645
11	1088.09793	−39.78136	160.21864
12	1185.95494	−60.56924	139.43076
13	1283.67755	−81.49330	118.5067
14	1381.39184	−102.0431	97.9569
15	1479.22459	−121.7130	78.287
16	1577.29257	−139.9841	60.0159
17	1675.69182	−156.3103	43.6897
18	1774.48667	−170.1084	29.8916
19	1873.6987	−180.7575	19.2425
20	1973.29609	−187.60731	12.39269
21	2073.18178	−188.50837	11.49163

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