一種針對大型凹型障礙物的組合導航算法
doi: 10.11999/JEIT190179
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1.
齊魯工業(yè)大學(山東省科學院)電子信息工程學院(大學物理教學部) 濟南 250353
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2.
齊魯工業(yè)大學(山東省科學院)電氣工程與自動化學院 濟南 250353
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3.
齊魯工業(yè)大學(山東省科學院)山東省科學院自動化研究所 濟南 250101
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4.
濟南市人機智能協(xié)同工程實驗室 濟南 250353
Integrated Navigation Algorithm for Large Concave Obstacles
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1.
School of Electronic and Information Engineering (Department of Physics), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China)
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2.
School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
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3.
Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250101, China
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4.
Jinan Engineering Laboratory of Human-machine Intelligent Cooperation, Jinan 250353, China
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摘要:
針對移動機器人導航過程中無法規(guī)避大型凹型障礙物問題，該文提出一種多狀態(tài)的組合導航算法。算法按照不同的運動環(huán)境，將移動機器人的運行狀態(tài)分類為運行態(tài)、切換態(tài)、避障態(tài)，同時定義了基于移動機器人運行速度和運行時間的狀態(tài)雙切換條件。當移動機器人處于運行態(tài)時，采用人工勢場法(APFM)進行導航，并實時觀測毗鄰障礙物的幾何構(gòu)型。在遭遇障礙物時，切換態(tài)用于判斷是否滿足狀態(tài)切換條件，以進入避障態(tài)執(zhí)行避障算法。避障完成后，狀態(tài)自動切換回運行態(tài)繼續(xù)執(zhí)行導航任務。多狀態(tài)的提出，可有效解決傳統(tǒng)人工勢場法在大型凹形障礙物的避障過程中存在局部震蕩的問題。基于運行速度和運行時間的雙切換條件判定算法，可實現(xiàn)多狀態(tài)間的平滑切換。實驗結(jié)果表明，該算法在解決局部震蕩問題的同時，還可降低避障時間，提升導航算法效率。
Abstract:For the problem that mobile robot can not avoid large concave obstacles during navigation, this paper proposes a multi-state integrated navigation algorithm. The algorithm classifies the running state of mobile robot into running state, switching state and obstacle avoidance state according to different moving environment, and defines the state double switching conditions based on the running speed and running time of the mobile robot. The Artificial Potential Field Method (APFM) is used to navigate and observe the geometric configuration of adjacent obstacles in real time. When encountering an obstacle, the switching state is used to determine whether the state switching condition is satisfied, and the obstacle avoidance algorithm is executed to enter the obstacle avoidance state and enter the obstacle avoidance state to implement the obstacle avoidance algorithm. After the obstacle avoidance is completed, the state automatically switches back to the running state to continue the navigation task. The proposal of multi-state can solve the problem of local oscillation of traditional artificial potential field method in the process of avoiding large concave obstacles. Furthermore, the double-switching condition determination algorithm based on running speed and running time can realize smooth switching between states and optimize the path. The experimental results show that the algorithm can not only solve the local oscillation problem, but also reduce the obstacle avoidance time and improve the efficiency of the navigation algorithm.
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表 1 人工勢場法符號含義
符號 符號含義 符號 符號含義 ${\rm{obs}}$ 障礙物 ${\bf{X}}{\rmq7j3ldu95}$ 目標位置 ${U_{{\rm{Xd}}}}(x)$ 引力勢能 ${U_{{\rm{obs}}}}(x)$ 斥力勢能 ${U_{{\rm{art}}}}(x)$ 總勢能 ${{F}}$ 合力 ${{{F}}_{{\rm{Xd}}}}$ 吸引力 ${{F}}_{\rm{obs}}$ 排斥力 $j$ 移動機器人感知到的
周邊障礙物的個數(shù)
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表 2 A*算法符號含義
符號 符號含義 符號 符號含義 $g(\cdot )$ 從初始節(jié)點到當前移動機器人
所在節(jié)點node的啟發(fā)式評估代價$h(\cdot )$ 從當前移動機器人所在節(jié)點node到
目標節(jié)點的啟發(fā)式評估代價$(x_{\rm{start}},y_{\rm{start}})$ 初始節(jié)點坐標 $(x_{\rm{goal}},y_{\rm{goal}})$ 目標節(jié)點坐標 $(x,y)$ 移動機器人實時位置坐標
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表 3 模型描述符號含義
符號 符號含義 符號 符號含義 $O_1$ 障礙物的左端點 $O_2$ 障礙物的右端點 $2\alpha $ 障礙物的長 $\beta $ 障礙物的寬 $v_t$ 移動機器人的實時線速度 $a$ 移動機器人在運行態(tài)時的最大速度 $\Delta d$ 移動機器人在某段時間內(nèi)的移動距離 $S_{\rm{obs}}$ 所有障礙物總面積 $S_{\rm{map}}$ 地圖面積 $\rho $ 障礙物密度
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