基于改进OpenPlanner算法的移动机器人局部路径规划

张志伟; 马小平; 白亚腾; 雷震亚; 李佳明

doi:10.13272/j.issn.1671-251x.18151

基于改进OpenPlanner算法的移动机器人局部路径规划

doi: 10.13272/j.issn.1671-251x.18151

1.
中国矿业大学信息与控制工程学院，江苏徐州　221000
2.
中国中煤能源集团有限公司，北京　100083

基金项目: 中央高校基本科研业务费专项资金资助项目（2020ZDPY0303）；国家自然科学基金项目（61976218）。

详细信息

作者简介:
张志伟（1977—），男，山西朔州人，硕士研究生，主要研究方向为智能控制、无人驾驶，E-mail：13934995257@163.com

通讯作者:
雷震亚（2000—），男，湖北宜昌人，硕士研究生，主要研究方向为移动机器人路径规划，E-mail:04181254@cumt.edu.cn。

中图分类号: TD67
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出版历程
- 收稿日期: 2023-08-30
- 修回日期: 2023-12-26
- 网络出版日期: 2024-01-04

Local path planning for mobile robots based on improved OpenPlanner algorithm

1.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221000, China
2.
China National Coal Group Corporation, Beijing 100083, China

摘要

摘要:
现有局部路径规划算法仅实现了移动机器人在场景内自由移动，但局部路径生成并未考虑场景内道路限制，对于一些规则化的结构道路并不适用。OpenPlanner算法很好地解决了该问题，但传统OpenPlanner算法规划的局部路径不满足移动机器人最大转向曲率约束而无法被移动机器人有效跟踪。针对上述问题，从状态采样和评价函数2个方面对传统OpenPlanner算法进行改进，并将改进OpenPlanner算法用于移动机器人局部路径规划。在状态采样阶段，通过设计双层局部路径簇来扩大最优局部路径解空间，其中首层局部路径簇入段纵向采样距离与行驶速度呈分段线性关系，次层局部路径簇入段纵向采样距离为首层局部路径簇的1.5倍；在路径筛选阶段，将路径曲率代价（由局部路径上各采样点曲率求和得到）引入评价函数，确保局部路径簇满足移动机器人的最大转向曲率约束，从而使局部路径被移动机器人所跟踪。实验结果表明：与传统OpenPlanner算法相比，改进OpenPlanner算法筛选的最优局部路径转向更加平缓，在无障碍物、有障碍物场景下平均曲率分别减小了31.3%，6.2%，且局部路径能够被移动机器人较好地跟踪。
- 移动机器人 /
- 局部路径规划 /
- 改进OpenPlanner算法 /
- 路径簇 /
- 状态采样 /
- 评价函数
Abstract:
The existing local path planning algorithms only achieve free movement of mobile robots in the scenario. But local path generation does not consider road constraints in the scenario, which is not applicable to some regularized structured roads. The OpenPlanner algorithm solves this problem well. But the local path planned by the traditional OpenPlanner algorithm does not meet the maximum turning curvature constraint of the mobile robot and cannot be effectively tracked by the mobile robot. In order to solve the above problem, the traditional OpenPlanner algorithm is improved from two aspects: state sampling and evaluation function. The improved OpenPlanner algorithm is applied to local path planning of mobile robots. In the state sampling stage, the optimal local path solution space is expanded by designing a double-layer local path cluster. The longitudinal sampling distance of the first layer local path cluster is linearly related to the driving speed in sections. The longitudinal sampling distance of the second layer local path cluster is 1.5 times that of the first layer local path cluster. In the path selection stage, the curvature cost of the path (obtained by summing the curvatures of each sampling point on the local path) is introduced into the evaluation function to ensure that the local path cluster satisfies the maximum turning curvature constraint of the mobile robot, thereby making the local path tracked by the mobile robot. The experimental results show that compared with the traditional OpenPlanner algorithm, the improved OpenPlanner algorithm filters the optimal local path with smoother turning. The average curvature is reduced by 31.3% and 6.2% in obstacle free and obstacle present scenarios, respectively. Moreover, the local path can be well tracked by mobile robots.
- mobile robots /
- local path planning /
- improved OpenPlanner algorithm /
- path cluster /
- state sampling /
- evaluation function

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图 1 局部路径簇分段结构

Figure 1. Sections of local path cluster

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图 2 沿参考线的状态采样过程

Figure 2. State sampling process along reference line

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图 3 参考线代价计算原理

Figure 3. Calculation principle of reference line cost

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图 4 过渡代价计算原理

Figure 4. Calculation principle of transition cost

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图 5 障碍物代价计算原理

Figure 5. Calculation principle of obstacle cost

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图 6 改进OpenPlanner算法局部路径簇分段结构

Figure 6. Sections of local path cluster of improved OpenPlanner algorithm

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图 7 有障碍物场景下局部路径规划仿真结果

Figure 7. Simulation results of local path planning in scenario with obstacle

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图 8 无障碍物场景下局部路径规划仿真结果

Figure 8. Simulation results of local path planning in scenario without obstacle

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图 9 局部路径跟踪对比

Figure 9. Comparison of local path tracking

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图 10 改进OpenPlanner算法实物实验

Figure 10. Physical experiment of improved OpenPlanner algorithm

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表 1 OpenPlanner算法改进前后实验结果

Table 1. Experimental results before and after improvement of OpenPlanner algorithm

场景	算法	路径长度/m	规划耗时/ms	平均曲率/m⁻¹	最大曲率/m⁻¹
无障碍物	传统OpenPlanner算法	16.566	0.42	0.064 3	0.280 8
无障碍物	改进OpenPlanner算法	16.413	0.55	0.044 2	0.171 9
有障碍物	传统OpenPlanner算法	16.135	0.47	0.035 5	0.155 4
有障碍物	改进OpenPlanner算法	16.012	0.64	0.033 3	0.128 1

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