本文關(guān)鍵詞： 自主勘探車輛 路徑規(guī)劃 柵格地圖 A星算法　出處：《吉林大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：勘探是人們獲得礦產(chǎn)資源的有效手段,但是勘探車輛的行駛條件惡劣,勘探車輛自動化是未來的發(fā)展趨勢。路徑規(guī)劃是自主行駛車輛的關(guān)鍵技術(shù),是自主車輛感知、規(guī)劃、控制三層中必不可少的一層,對路徑規(guī)劃的研究具有重要的理論和現(xiàn)實意義。本文使用了圖搜索方法解決路徑規(guī)劃問題。圖搜索方法分為兩步:一是圖的構(gòu)建,將現(xiàn)實環(huán)境抽象成規(guī)劃地圖;二是圖的搜索,在規(guī)劃地圖中搜索出符合條件的路徑。針對二維環(huán)境,本文使用了障礙敏感法進行規(guī)劃地圖的構(gòu)建,分別提出了基于離散狀態(tài)的增速A星算法(DCAA*)和基于混合狀態(tài)的增速A星算法(HCAA*)進行規(guī)劃地圖的搜索。在離散A星算法部分,總結(jié)了避障實現(xiàn)的三種方式并加以比較,限制轉(zhuǎn)向角并使用曲線過渡保證了路徑的可行性;在混合A星算法部分,闡述了混合A星算法的子節(jié)點擴展方式,給出了代價值體系和已歷代價值的計算方法,并解釋了通過代價值影響A星算法的原理,闡述了主副啟發(fā)值的計算方法,并分析了啟發(fā)值權(quán)重的影響。針對三維環(huán)境,本文提出了通行性分級法進行規(guī)劃地圖的構(gòu)建,調(diào)整了混合A星算法進行規(guī)劃地圖的搜索。通行性分級法首先將車輛通過性失效因素分類為階躍、坡度、連續(xù)階躍和附著力不足,并相應(yīng)提出了評估環(huán)境通行性等級的四種參數(shù):階躍δ,坡度ψ,崎嶇度ω和地質(zhì)因數(shù)τ,然后給出了以這四種參數(shù)對環(huán)境模型評估分級的計算方法。為了能夠快速構(gòu)建場景測試算法,本文提出了模塊化仿真環(huán)境的建立方法,環(huán)境模型建立后,就可以使用前述通行性分級法進行預(yù)處理得到規(guī)劃地圖�；旌螦星算法的調(diào)整主要在代價值體系、已歷代價值和啟發(fā)代價值公式方面,調(diào)整后可實現(xiàn)三維環(huán)境的降維規(guī)劃。最后本文搭建了仿真模型進行了一系列仿真實驗,實驗結(jié)果表明本文提出的算法理論切實有效,能夠完成車輛在野外環(huán)境中的路徑規(guī)劃。
[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.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：U463.6

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本文編號：1520324