自主行驶资源勘探车辆路径规划算法研究

发布时间：2018-02-20 23:23

本文关键词： 自主勘探车辆路径规划栅格地图 A星算法　出处：《吉林大学》2016年硕士论文　论文类型：学位论文

【摘要】：勘探是人们获得矿产资源的有效手段,但是勘探车辆的行驶条件恶劣,勘探车辆自动化是未来的发展趋势。路径规划是自主行驶车辆的关键技术,是自主车辆感知、规划、控制三层中必不可少的一层,对路径规划的研究具有重要的理论和现实意义。本文使用了图搜索方法解决路径规划问题。图搜索方法分为两步:一是图的构建,将现实环境抽象成规划地图;二是图的搜索,在规划地图中搜索出符合条件的路径。针对二维环境,本文使用了障碍敏感法进行规划地图的构建,分别提出了基于离散状态的增速A星算法(DCAA*)和基于混合状态的增速A星算法(HCAA*)进行规划地图的搜索。在离散A星算法部分,总结了避障实现的三种方式并加以比较,限制转向角并使用曲线过渡保证了路径的可行性;在混合A星算法部分,阐述了混合A星算法的子节点扩展方式,给出了代价值体系和已历代价值的计算方法,并解释了通过代价值影响A星算法的原理,阐述了主副启发值的计算方法,并分析了启发值权重的影响。针对三维环境,本文提出了通行性分级法进行规划地图的构建,调整了混合A星算法进行规划地图的搜索。通行性分级法首先将车辆通过性失效因素分类为阶跃、坡度、连续阶跃和附着力不足,并相应提出了评估环境通行性等级的四种参数:阶跃δ,坡度ψ,崎岖度ω和地质因数τ,然后给出了以这四种参数对环境模型评估分级的计算方法。为了能够快速构建场景测试算法,本文提出了模块化仿真环境的建立方法,环境模型建立后,就可以使用前述通行性分级法进行预处理得到规划地图。混合A星算法的调整主要在代价值体系、已历代价值和启发代价值公式方面,调整后可实现三维环境的降维规划。最后本文搭建了仿真模型进行了一系列仿真实验,实验结果表明本文提出的算法理论切实有效,能够完成车辆在野外环境中的路径规划。
[Abstract]:Exploration is an effective means for people to obtain mineral resources, but the driving conditions of exploration vehicles are poor, the automation of exploration vehicles is the development trend in the future. Path planning is the key technology of autonomous vehicles, and it is the perception and planning of autonomous vehicles. The study of path planning is of great theoretical and practical significance to the study of path planning. In this paper, a graph search method is used to solve the path planning problem. The graph search method is divided into two steps: one is the construction of the graph, and the other is the construction of the graph. Abstract the realistic environment into the planning map; second, search the map, search the path that meets the conditions in the planning map. For the two-dimensional environment, this paper uses the obstacle sensitive method to construct the planning map. In the part of discrete A star algorithm, three methods of obstacle avoidance are summarized and compared. In the part of hybrid A-star algorithm, the expansion mode of sub-nodes of hybrid A-star algorithm is expounded, and the generation value system and the method of calculating the value of previous generations are given. The principle of A-star algorithm is explained, the calculation method of the principal and secondary heuristic value is explained, and the influence of heuristic value weight is analyzed. According to the three-dimensional environment, this paper puts forward the method of traffic classification to construct the planning map. The mixed A-star algorithm is adjusted to search the planning map. Firstly, the passability classification method classifies the vehicle passing failure factors as step, slope, continuous step and insufficient adhesion. Four parameters for evaluating the environmental traffic grade are put forward: step 未, slope 蠄, rugged degree 蠅 and geo-factor 蟿. Then, the calculation method for evaluating the classification of environmental model by these four parameters is given. In order to be able to construct quickly. Build scenario testing algorithms, In this paper, a method of building modular simulation environment is proposed. After the environmental model is built, the planning map can be obtained by preprocessing the method of traffic classification. The adjustment of mixed A-star algorithm is mainly in the system of generation value. The dimensionality reduction programming of 3D environment can be realized by adjusting the value formula and heuristic value formula. Finally, a series of simulation experiments are carried out in this paper. The experimental results show that the proposed algorithm theory is practical and effective. Able to complete the vehicle path planning in the field environment.
【学位授予单位】：吉林大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：U463.6

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