外文翻譯--輪式挖掘裝載機自動控制

1、<p>　　An automated digging control for a wheel loader</p><p><b>　　Summary</b></p><p>　　An Automated Digging Control System (ADCS) for a wheel loader is developed that utilizes a

2、 behavior-based control structure combined with fuzzy logic. This controller exhibits the real-time reactive responses necessary for executing digging tasks in an uncertain, unstructured and dynamic excavation environmen

3、t. This paper presents field test results of a prototype ADCS that was developed and implemented on a Caterpillar 980G wheel loader. Test results show that the performance of the automated s</p><p>　　Key Wor

4、ds: Fuzzy behavior control; Automated digging; Robotic excavation</p><p>　　Introduction</p><p>　　Automating the dig component of the excavation cycle on earth moving machines such as wheel loade

5、rs, hydraulic shovels and mass-excavators, and cable shovels, has many potential benefits. Typically, when these machines are used in mining or construction applications they load large quantities of material (soil, rock

6、, etc.) into a fleet of circulating trucks. Here, digging difficulty can vary dramatically and in these difficult digging situations effective loading performance is only achieved by </p><p>　　The use of an

7、effective automated digging control system would give every machine operator the capabilities of an expert operator, and generate the following benefits. First, consistent operation over the duration of the shift, since

8、the control system does not get tired or lose concentration. Second, improve machine availability because the controller will always operate the machine within design limits during digging. Third, reduced wheel slippage

9、during digging. However, to achieve these bene</p><p>　　The sensors and actuators used should be limited to those currently available on a modern loading machine. For a wheel loader this includes electro-hyd

10、raulic actuation of bucket motions, bucket position sensors and measurement of a limited number of drive train parameters. Complex sensing and actuation systems may be prone to failure in the harsh environment. Next, the

11、 system should require no input from the operator related to characterizing digging difficulty. This would require operators to m</p><p>　　Automatic digging control of loading machines is particularly diffic

12、ult because they operate in dynamic and unstructured environments where conditions are unknown, extremely variable and difficult to detect. On the other hand, expert human operators can achieve sophisticated control of l

13、oading machines in these difficult environments. Repeated excavation experiences help the operator to learn machine operational skills and how to adapt their operating modes to the dynamic conditions. The complex</p&g

14、t;<p>　　Several years ago, the University of Arizona researchers started a project funded by Caterpillar Inc. to use CARE as the basis to develop, implement and test an Automated Digging Control System (ADCS) on a

15、 wheel loader. The implementation platform for the prototype ADCS was a Caterpillar 980G wheel loader (see Figure 1). This wheel loader weighs 29,497 kg, is 9.5 m long, 3.75 m high and has a 4.7 m³bucket. The criter

16、ia listed above were used for the designing ADCS.</p><p>　　Fig. 1. The Caterpillar 980G Wheel Loader Test Platform</p><p>　　In this paper, we show how the CARE approach has been used to develop

17、the prototype Automated Digging Control System on the Caterpillar 980G. The ADCS utilizes only existing production sensors and actuators and has only modest computational needs. The first half of the paper details the co

18、ntrol structure of the ADCS, while the remaining sections present data from field tests. These show that the performance of the automated system is comparable to that of an expert human operator over a wide ran</p>

19、<p>　　Overview of related automated digging control work</p><p>　　The many potential applications for automated earth moving systems has attracted a significant amount of research in this area. Typica

20、lly, research has fallen into two major areas: digging process modeling and planning, and automated digging. A comprehensive summary of the current research in the field is given in Singh. This section concentrates on wo

21、rk related to the automated digging direction.</p><p>　　In general, the simple trajectory planning and control approach is not effective, therefore several researchers measure forces during digging which are

22、 used to adjust the digging trajectory. Bullock and Huang use these forces to initiate digging trajectory actions when fixed force thresh-olds are met. These techniques are not effective and often do not fill the bucket

23、in a wide variety of excavation situations. Alternatively, other researchers have selected digging con-trol actions using a set o</p><p>　　A fuzzy logic controller has been developed by Sameshima et al . whi

24、ch controls the actuation of each degree of freedom relative to bucket motion during the digging process. Thus the fuzzy rules are evaluated at each control cycle and joint velocity commands are the weighted output of th

25、e rules. The Autodig approach used by Rocke uses the actual forces from hydraulic cylinder measurements. These forces are then related to forces inferred from bucket velocities. Commands for each degree of freedo</p&g

26、t;<p>　　Autodig algorithm for dig execution in their Autonomous Loading System (ALS) which completely automates the task of loading trucks with a mass excavator.</p><p>　　Another Autodig approach by S

27、hull also uses actual forces measured from the hydraulic cylinders. These forces are used to determine a force vector passing through a point on the bucket that represents the resultant material forces resisting bucket m

28、otion. A target angle is also generated on the basis of accumulated energy and then bucket motion commands are generated in response to differences in the target angle and the force vector. This approach can cause the bu

29、cket to stall when high resisti</p><p>　　A position-based impedance control approach for operator assistance during digging with a teleoperated mini-excavator was developed by Salcudean et al . at the Univer

30、sity of British Columbia. The system follows an operator specified digging path until material resistance impedes its progress, then the impedance controller tries to follow the path as closely as possible. Alternatively

31、, Bernold proposes an impedance controller where an optimal trajectory for the bucket is generated using a plannin</p><p>　　Singh proposes using a trajectory planner that uses a pure position based control s

32、ystem during the digging process. A prediction of forces that will be encountered during digging is used to reject trajectories that exceed the limitations of the hydraulic actuators. Predicting forces in soil with unkno

33、wn inclusions or in blasted rock is extremely difficult if not impossible.</p><p>　　輪式挖掘裝載機自動控制</p><p><b>　　摘要</b></p><p>　　為輪式裝載機開發(fā)的自動挖掘控制系統(tǒng)，采用了基于行為的模糊邏輯控制結(jié)構(gòu)。在非結(jié)構(gòu)化和動態(tài)開挖

34、環(huán)境下執(zhí)行一個不確定的挖掘任務時，該控制器能夠?qū)崟r響應。本文將一個ADCS原型系統(tǒng)應用在980G卡特彼勒輪式裝載機進行現(xiàn)場試驗并得出結(jié)果結(jié)果。試驗結(jié)果表明，自動化系統(tǒng)的性能在廣泛的開挖情況下與專業(yè)操作員相媲美。</p><p>　　關(guān)鍵詞：模糊行為控制；自動挖掘；機器人挖掘；</p><p><b>　　引言</b></p><p>　　挖掘部

35、件在地面進行周期運動的機械如輪式裝載機、液壓挖掘機、大型挖掘機和電纜鏟子，它們的自動化有許多潛在的好處。通常，當這些機器是用于采礦或建筑應用，它們運載大量的材料（土壤，巖石，等）到一個循環(huán)的卡車車隊。在這里，挖掘的困難會急劇變化。這些困難的挖掘情況下，專業(yè)操作人員需要的是高效的裝載。在這些情況下，挖掘時間能達到兩倍或三倍，大大降低了機器的輸出。</p><p>　　一個有效的自動挖掘控制系統(tǒng)的使用會讓每個操作員擁

36、有專家操作者的能力，并產(chǎn)生以下好處。首先，由于控制系統(tǒng)不會累了或失去注意力，所以在換班的時間能夠?qū)崿F(xiàn)一致的操作。第二，控制器能在設計范圍內(nèi)的挖掘過程中操作這臺機器來提高機器的可用性。第三，減少了挖掘過程中車輪打滑。然而，在惡劣的施工環(huán)境下要實現(xiàn)這些好處并有效運作，則自動化系統(tǒng)的設計需要符合以下重要標準。</p><p>　　使用的傳感器和執(zhí)行器應限于那些目前可用的現(xiàn)代裝載機。輪式裝載機鏟斗運動包括電液驅(qū)動，斗式位

37、置傳感器和傳動有限數(shù)目的參數(shù)測量。復雜的傳感和驅(qū)動系統(tǒng)很容易在惡劣環(huán)境下出故障。其次，系統(tǒng)不應該從操作員獲得挖掘困難相關(guān)的特征。這就需要經(jīng)營者作出關(guān)于挖掘難度判斷。在一般情況下，材料的表面特性被加載和其滿斗挖掘過程中潛在的相互作用是開挖難度最大的影響。操作者不能看到表面的下面。因此，沒有操作員輸入的自動化系統(tǒng)必須能夠通過對開挖條件的變化反應調(diào)整其挖掘軌跡。</p><p>　　由于裝載機工作在動態(tài)的、非結(jié)構(gòu)化的環(huán)

38、境，并且環(huán)境條件未知，變化無常的，難以檢測，所以裝載機的自動挖掘控制是特別困難的。另一方面，人類操作者可以在這些艱難的環(huán)境中對裝載機實現(xiàn)復雜的控制。重復挖掘經(jīng)驗幫助操作者學習機器的操作技能和如何使其操作模式適應動態(tài)條件。挖掘機和其環(huán)境之間相互作用的復雜性，使得發(fā)展通常用在傳統(tǒng)的控制模式的數(shù)學模型不切實際或不可行。因此，亞利桑那大學的研究人員已經(jīng)開發(fā)的控制系統(tǒng)是采用了集合眾多技術(shù)熟練操作者的挖掘知識。機械挖掘控制結(jié)構(gòu)(CARE)是一種混合

39、的體系結(jié)構(gòu)，采用了基于行為的控制結(jié)構(gòu)。在最低水平時，它具有反應控制，以產(chǎn)生原始的鏟斗行為，和使用有限狀態(tài)機（FSM）的任務規(guī)劃（獲取行為仲裁要求的挖掘知識）。模糊邏輯與基于行為的控制的結(jié)合提供了能對在動態(tài)的，不確定的環(huán)境中的挖掘任務執(zhí)行必要的實時響應的挖掘控制。</p><p>　　幾年前，亞利桑那大學的研究人員開始了一個由卡特彼勒公司資助的項目，該項目以CARE為基礎(chǔ)上，開發(fā)、實施和測試基于輪式裝載機的自動挖掘

40、控制系統(tǒng)（ADCS）。作為原型的ADC的實現(xiàn)平臺是980G卡特彼勒輪式裝載機（見圖1）。該輪式裝載機重29497公斤，長9.5米，高3.75米，4.7米³斗。以上所列標準進行ADCS的設計。</p><p>　　圖1 卡特彼勒980G輪式裝載機測試平臺</p><p>　　在本文中，我們展示了CARE的方法已如何被用于開發(fā)基于卡特彼勒980G的原型自動挖掘控制系統(tǒng)。ADCS利用現(xiàn)

41、有生產(chǎn)的傳感器和執(zhí)行器，只需要適度的計算。論文的上半部分詳細介紹了ADC的控制結(jié)構(gòu)，而其余部分的數(shù)據(jù)從現(xiàn)場測試。這些結(jié)果表明，自動化系統(tǒng)的性能在挖掘地址變化廣泛方面能與專業(yè)操作者相媲美。</p><p>　　概述相關(guān)的自動挖掘控制工作</p><p>　　地表自動移動系統(tǒng)的許多潛在的應用這個領(lǐng)域已經(jīng)吸引了大量的研究。通常情況下，研究了兩個主要領(lǐng)域：挖掘過程的建模和規(guī)劃，自動挖掘。在Sing

42、h對該領(lǐng)域的研究現(xiàn)狀進行了全面總結(jié)。這部分主要是自動挖掘方向相關(guān)的工作。</p><p>　　在一般情況下，軌跡規(guī)劃和控制簡單的方法是無效的，因此一些研究者在挖掘過程中測量力，用于調(diào)整挖掘軌跡。當固定力滿足上限時，布洛克和黃使用這些力量研究挖掘軌跡的行為。這些技術(shù)不是高興效的，而且在很多挖掘情況下，往往不能裝滿鏟斗。另外，其他研究人員已經(jīng)選擇了使用一套控制規(guī)則的挖掘控制行為。來自蘭卡斯特大學的自動挖掘機(LUCI

43、E)就是這種方法的一個例子。該挖掘機計劃試圖遵循初始挖掘軌跡，然后利用設定的軌跡對開挖條件做出反應。</p><p>　　模糊邏輯控制器已經(jīng)通過Sameshima等人開發(fā)。驅(qū)動挖掘過程中，它控制每個相對斗運動的自由度。因此，用于每個控制周期和關(guān)節(jié)速度命令的模糊規(guī)則進行的是規(guī)則的加權(quán)輸出。洛克的Autodig方法使用從液壓缸測量的實際的力。這些力與從鏟斗的速度推出的力相關(guān)。鏟斗每個自由度的命令是從一個基于人類操作者

44、在各種土壤條件下如何控制單個節(jié)點的查找表生成的。這種情況下，土壤條件必須早挖掘前提供給系統(tǒng)，而且為了有效的挖掘，材料必須保持相對均勻。土壤中的意外夾雜物是本系統(tǒng)需要處理的一個問題。Cannon實現(xiàn)增強了自主挖掘執(zhí)行加載系統(tǒng)（ALS）中Autodig的算法，實現(xiàn)大型挖掘機裝載卡車的任務完全自動化。</p><p>　　另一種Shull 的Autodig方法也使用實際從液壓缸測量的力。這些力被用來確定一個過一點的力向

45、量，它代表了合成材料抵抗斗運動的力。目標角是在積累的能量的基礎(chǔ)上產(chǎn)生的，鏟斗的運動指令是為響應目標角度與力矢量間的差異產(chǎn)生的。遇到復雜的斗、巖相互作用過程中的高阻力情況時，這種方法會使鏟斗停止轉(zhuǎn)動的時候。Dasys（數(shù)據(jù)系統(tǒng)環(huán)境模擬器）描述了一個基于負載牽引轉(zhuǎn)儲（LHD）機器的自動化的鏟斗裝載系統(tǒng)，也使用從鏟斗液壓缸的壓力傳感器獲得的反饋。該系統(tǒng)不使用廢石堆模型控制鏟斗運動，但會 “感覺”被加載的材料而傾斜或使鏟斗擺動。這種方法的性能特

46、點是未知的。</p><p>　　Salcudean等人開發(fā)了一個基于位置的阻抗控制方法，為操作員在用中小型遙控挖掘機挖掘時提供幫助。在英屬哥倫比亞大學。該系統(tǒng)會遵循操作員指定的挖掘軌跡直到物料阻力阻礙其進步，但是阻抗控制器會試圖遵循盡可能相近的路徑。另外，Bernold提出了一種阻抗控制器，鏟斗的最優(yōu)軌跡是使用基于土壤的特性和挖掘工具交互的規(guī)劃算法生成。</p><p>　　Singh提