機(jī)器人 外文翻譯 外文文獻(xiàn) 英文文獻(xiàn) 采用模糊邏輯控制使自主機(jī)器人避障設(shè)計(jì)

1、autonomous robot obstacle avoidance using a fuzzy logic control schemewilliam martinsubmitted on december 4, 2009cs311 - final project1. introductionone of the considerable hurdles to overcome, when trying to describe a real-world control scheme with first-order logic, is the strong ambiguity found

2、in both semantics and evaluations. although one option is to utilize probability theory in order to come up with a more realistic model, this still relies on obtaining information about an agents environment with some amount of precision. however, fuzzy logic allows an agent to exploit inexactness i

3、n its collected data by allowing for a level of tolerance. this can be especially important when high precision or accuracy in a measurement is quite costly. for example, ultrasonic and infrared range sensors allow for fast and cost effective distance measurements with varying uncertainty. the propo

4、sed applications for fuzzy logic range from controlling robotic hands with six degrees of freedom1 to filtering noise from a digital signal.2 due to its easy implementation, fuzzy logic control has been popular for industrial applications when advanced differential equations become either computatio

5、nally expensive or offer no known solution. this project is an attempt to take advantage of these fuzzy logic simplifications in order to implement simple obstacle avoidance for a mobile robot. 2. physical robot implementation2.1. chassis and sensorsthe robotic vehicles chassis was constructed from

6、an excalibur ei-msd2003 remote control toy tank. the device was stripped of all electronics, gears, and extraneous parts in order to work with just the empty case and two dc motors for the tank treads. however, this left a somewhat uneven surface to work on, so high-density polyethylene (hdpe) rods

7、were used to fill in empty spaces. since hdpe has a rather low surface energy, which is not ideal for bonding with other materials, a propane torch was used to raise surface temperature and improve bonding with an epoxy adhesive. three sharp gp2d12 infrared sensors, which have a range of 10 to 80 cm

8、, were used for distance measurements. in order to mount these appropriately, a 2.5 by 15 cm piece of aluminum was bent into three even pieces at 135 degree angles. this allows for the ir sensors to take three different measurements at 45 degree angles (right, middle, and left distances). this senso

9、r mount was then attached to an hdpe rod with mounting tape and the rod was glued to the tank base with epoxy. since the minimum distance that can be reliably measured with these sensors is 10 cm, the sensors were placed about 9 cm from the front of the vehicle. this allowed measurements to be taken

10、 very close to the front of the robot.2.2. electronicsin order to control the speed of each motor, pulse-width modulation (pwm) was used to drive two l2722 op amps in open loop mode (fig. 1). the high input resistance of these ics allow for the motors to be powered with very little power draw from t

11、he pwm circuitry. in order to isolate the motors power supply from the rest of the electronics, a 9.6 v nicad battery was used separately from a standard 9 v that demand on the op amps led to a small amount of overheating during continuous operation. this was remedied by adding small heat sinks and

12、a fan to the forcibly disperse heat. fig. 1. the control circuit used for driving each dc motor. note that the pwm signal was between 0 and 5 v.2.3. microcontrollercomputation was handled by an arduino duemilanove board with an atmega328 microcontroller. the board has low power requirements and modi

13、fications. in addition, it has a large number of prototyping of the control circuit and based on the wiring language. this board provided an easy and low-cost platform to build the robot around.3. fuzzy control scheme forin order to apply fuzzy logic to the robot to interpret measured distances. whi

14、le the final algorithm depended critically on the geometry of the robot itself and how it operates, some basic guidelines were followed. similar research projects provided both simulation results and ideas for implementing fuzzy control.3,4,53.1. membership functionsthree sets of membership function

15、s were created to express degrees of membership for distances, translational speeds, and rotational speeds. this made for a total of two input membership functions and eight output membership functions (fig. 2). triangle and trapezoidal functions were used exclusively since they are quick to compute

16、 and easy to modify. keeping computation time to a minimum was essential so that many sets of data could be analyzed every second (approximately one every 40 milliseconds). the distance membership functions allowed the distances from the ir sensors to be quickly fuzzified, while the eight speed memb

17、ership functions converted fuzzy values back into crisp values.3.2.rule baseonce the input data was fuzzified, the eight defined fuzzy logic rules (table i) were executed in order to assign fuzzy values for translational speed and rotation. this resulted in multiple values for the each of the fuzzy

18、output components. it was then necessary to take the maximum of these values as the fuzzy value for each component. finally, these fuzzy output values were defuzzified using the max-product technique and the result was used to update each of the motor speeds.(a)(b)(c)fig. 2. the membership functions

19、 used for (a) distance, (b) translation speed, and (c) rotational speed. these functions were adapted from similar work done in reference 3.4. resultsthe fuzzy control scheme allowed for the robot to quickly respond to obstacles it could detect in its environment. this allowed it to follow walls and

20、 bend around corners decently without hitting any obstacles. however, since the ir sensors measurements depended on the geometry of surrounding objects, there were times when the robot could not detect obstacles. for example, when the ir beam hit a surface with oblique incidence, it would reflect aw

21、ay from the sensor and not register as an object. in addition, the limited number of rules used may have limited the dynamics of the robots responses. some articles suggest as many as forty rules6 should be used, while others tend to present between ten and twenty. since this project did not explore

22、 complex kinematics or computational simulations of the robot, it is difficult to determineexactly how many rules should be used. however, for the purposes of testing fuzzy logic as a navigational aide, the eight rules were sufficient. despite the many problems that ir and similar ultrasonic sensors

23、 have with reliably obtaining distances, the robustness of fuzzy logic was frequently able to prevent the robot from running into obstacles.5. conclusionthere are several easy improvements that could be made to future iterations of this project in order to improve the robots performance. the most dr

24、amatic would be to implement the ir or ultrasonic sensors on a servo so that they could each scan a full 180 degrees. however, this type of overhaul may undermine some of fuzzy logics helpful simplicity. another helpful tactic would be to use a few types of sensors so that data could be taken at mul

25、tiple ranges. the ir sensors used in this experiment had a minimum distance of 10 cm, so anything in front of this could not be reliably detected. similarly, the sensors had a maximum distance of 80 cm so it was difficult to react to objects far away. ultrasonic sensors do offer significantly increa

26、sed ranges at a slightly increased cost and response time. lastly, defining more membership functions could help improve the rule base by creating more fine tuned responses. however, this would again increase the complexity of the system.thus, this project has successfully implemented a simple fuzzy

27、 control scheme for adjusting the heading and speed of a mobile robot. while it is difficult to determine whether this is a worthwhile application without heavily researching other methods, it is quite apparent that fuzzy logic affords a certain level of simplicity in the design of a system. further

28、more, it is a novel approach to dealing with high levels of uncertainty in real-world environments.6. references1 ed. m. jamshidi, n. vadiee, and t. ross, fuzzy logic and control: software and hardware applications, (prentice hall: englewood cliffs, nj) 292-328.2 ibid, 232-261.3 w. l. xu, s. k. tso,

29、 and y. h. fung, fuzzy reactive control of a mobile robot incorporating a real/virtual target switching strategy, robotics and autonomous systems, 23(3), 171-186 (1998).4 v. peri and d. simon, “fuzzy logic control for an autonomous robot,” 2005 annual meeting of the north american fuzzy information

30、processing society, 337-342 (2005).5 a. martinez, e. tunstel, and m. jamshidi, fuzzy-logic based collision-avoidance for a mobile robot, robotica, 12(6) 521527 (1994).6 w. l. xu, s. k. tso, and y. h. fung, fuzzy reactive control of a mobile robot incorporating a real/virtual target switching strateg

31、y, robotics and autonomous systems, 23(3), 171-186 (1998).采用模糊邏輯控制使自主機(jī)器人避障設(shè)計(jì)william martin 威廉馬丁submitted on december 4, 2009 提交于2009年12月4日cs311 - final project cs311 -最終項(xiàng)目1. 1 introduc引言 one of the considerable hurdles to overcome, when trying to describe a real-world 其中一個很大的障礙需要克服，當(dāng)試圖用控制邏輯一階來描述一個真實(shí)

32、世界設(shè)計(jì)在發(fā)現(xiàn)在這兩個語義evaluations.評價(jià)中control scheme with first-order logic, is the strong ambiguity found in both semantics and設(shè)計(jì)設(shè)計(jì)設(shè)計(jì)是個強(qiáng)大的模糊區(qū)。although one option is to utilize probability theory in order to come up with a mo雖然一個方案是利用概率論，以便得到一個更realistic model, this still relies on obtaining information about

33、 an agents environment with some現(xiàn)實(shí)的模型，這種獲得信息的方法的精度仍然依賴于外部環(huán)境amount of precision.。然而，在其收集data by allowing for a level of tolerance.數(shù)據(jù)的公差允許的范圍內(nèi),模糊邏輯允許利用不精確的間接方法來實(shí)現(xiàn)。在需要高精度or accuracy in a measurement is quite costly.測量時(shí)它是相當(dāng)昂貴的。例如，超聲波和紅外線傳感器在其距離內(nèi)allow for fast and cost effective distance measurements wit

34、h varying uncertaint允許快速和有效的測量但其測量結(jié)果存在很大的不確定性。模糊邏輯控制的應(yīng)用范圍從利用六自由度1控制機(jī)器人手臂到從數(shù)字信號2中過濾噪音。2due to its easy implementation, fuzzy logic control has由于其易于實(shí)現(xiàn)，模糊邏輯控制been popular for industrial applications when advanced differential equations become either一直流行在工業(yè)應(yīng)用中,尤其是高級微分方程computationally expensive or offer

35、 no known solution.計(jì)算復(fù)雜或難以解決時(shí)。這個項(xiàng)目企圖利用advantage of these fuzzy logic simplifications in order to implement simple obstacle avoidance簡化模糊邏輯，以實(shí)現(xiàn)移動機(jī)器人的簡單避障for a mobile robot.。2. 2 機(jī)器人具體實(shí)現(xiàn) 2.1. 2.1 底盤和傳感器 the robotic vehicles chassis was constructed from an excalibur ei-msd2003 remote 機(jī)器人汽車的底盤由excalibur

36、 msd2003遠(yuǎn)程control toy tank.控制玩具坦克構(gòu)成。該裝置去除了所有電子，齒輪和其他多余的部分，只留下了個空架和兩個帶動坦克履帶的直流電動機(jī)。然而，這使得它的表面somewhat uneven surface to work on, so high-density polyethylene (hdpe) rods were used to有些不平滑，所以用高密度聚乙烯（hdpe）棒來fill in empty spaces.填補(bǔ)空白處。由于高密度聚乙烯具有較低的表面能，當(dāng)與其他材料粘接時(shí)它并不理想，所以用一丙烷棒與環(huán)氧粘合劑來提高表面溫度，提高易焊接能力。three sha

37、rp gp2d12 infrared sensors, which have a range of 10 to 80 cm, were used for 三夏普gp2d12紅外傳感器用于測量距離，其測量范圍是10至80厘米，分別。為了更好安裝，將一塊2.5*15厘米鋁被彎曲成3個135度角的小塊。用戶既可以將紅外傳感器采取三種（右，中，左）不同的測量距離分別是45度角。這種傳感器通過安裝帶和高密度聚乙烯棒安裝到坦克的底部。由于可靠地最小測量距離是10厘米，傳感器放置在距離小車前面約9厘米處。這使得measurements to be taken very close to the front

38、of the robot.測量點(diǎn)非常接近機(jī)器人的前面。2.2. 2.2 電子器件in order to control the speed of each motor, pulse-width modulation (pwm) was used to 為了控制電機(jī)的速度，脈沖寬度調(diào)制（pwm）用來驅(qū)動兩個l2722運(yùn)算放大器以使其工作在開環(huán)模式（圖1）。ic的高輸入電阻使馬達(dá)從供電的pwm電路中得到非常小功率。為了使電機(jī)的供電與其他器件的供電相分開，專門一個9.6 v鎳鎘電池為電機(jī)供電，而其余的電子器件用一個標(biāo)準(zhǔn)的9伏的電源供電。這樣運(yùn)算放大器在持續(xù)工作時(shí)就產(chǎn)生了很少的過熱。這部分熱通過一個小

39、熱槽和一個散熱風(fēng)扇使其保持平衡。fig. 圖1.控制電路用于驅(qū)動每個直流電動機(jī)注：pwm信號電壓在 between 0 and 5 v.0至5v之間。 2.3. 2.3 微控制器 computation was handled by an ardu 計(jì)算是由arduino duemilanove板和atmega328微控制器完成的。該電路板具有低功耗和支持原始pwm信號的特點(diǎn)。此外，它還有一個適應(yīng)快速變化的輸入端。arduino的編程語言具有c語言的形式。這個電路板提供了簡易低成本的機(jī)器人開發(fā)電路。3. 3 避障的模糊控制方案 in order to apply fuzzy logic to

40、the rob 為了將模糊邏輯應(yīng)用于機(jī)器人運(yùn)作，而開發(fā)了一個詮釋測量距離的設(shè)計(jì)。雖然最終的算法在很大程度上取決于of the robot itself and how it operates, some basic guidelines were followed.該機(jī)器人本身以及它如何運(yùn)作，但還是要遵循一些基本準(zhǔn)則的。類似的研究項(xiàng)目既提供模擬結(jié)果及模糊控制的執(zhí)行方法。3,4,53,4,53.1. 3.1 附屬功能 three sets of membership functions were created to express degrees of membership for 由此引出了

41、三種附屬功能,它們分別是測量distances, translational speeds, and rotational speeds.距離，平移速度和旋轉(zhuǎn)速度。 this made for a total of two input 他們由2各輸入隸屬函數(shù)和8個輸出隸屬函數(shù)（圖2）組成。三角形和梯形函數(shù)是專用的，因?yàn)樗鼈冇?jì)算速度快，易于修改。保持計(jì)算時(shí)間到最低限度是必要的，以便多組數(shù)據(jù)每秒（大約每40毫秒/個）都可以得到分析。距離隸屬函數(shù)可以使來自距離紅外傳感器的距離信息迅速“模糊化”，而8速模糊值函數(shù)將其轉(zhuǎn)換回準(zhǔn)確的數(shù)值。 3.2.rule base 3.2 基本規(guī)則once the in

42、put data was fuzzified, the eight defined fuzzy logic rules (table i) were 一旦輸入數(shù)據(jù)模糊化，模糊邏輯定義的8個規(guī)則（表一）就被執(zhí)行，以便將模糊值分配給平移速度和旋轉(zhuǎn)速度。這導(dǎo)致每個組件的模糊輸出有多個值。 it was then necessary to take the 因此有必要采用每個組件的最大值作為模糊值。最后，通過最大輸出技術(shù)將這些模糊輸出值“解模糊”，其結(jié)果是用來更新每個馬達(dá)的速度。 5 (a) （一） (b) （二） (c) （三） fig. 圖2.附屬函數(shù)分別用來測量（1）距離，（二）翻譯速度，以及（

43、c）轉(zhuǎn)動速度。 these functions were adapted from similar work done in reference 3. 這些函數(shù)從參考文獻(xiàn)3類似的內(nèi)容中借鑒而來。 6 table i. the fuzzy logic rule base used for the control scheme 表1 用于控制的模糊邏輯規(guī)則庫 4. 4 結(jié)果 the fuzzy control scheme allowed for the robot to quickly respond to obstacles it could 該設(shè)計(jì)使模糊控制的機(jī)器人能夠迅速回應(yīng)檢測到的障礙。

44、這使得它能沿著墻壁和角落行動而不會撞擊到任何障礙物。然而，由于紅外傳感器的尺寸取決于周圍物體幾何形狀，很多時(shí)候機(jī)器人也無法檢測到障礙物。例如，當(dāng)紅外線光束擊中地面的傾斜處事，則反映出遠(yuǎn)離傳感器和不認(rèn)為是一個障礙對象。此外，使用有限數(shù)量的規(guī)則可能限制機(jī)器人的一些反應(yīng)。有些文章建議多達(dá)40條規(guī)則應(yīng)加以使用，而另一些傾向于10和20條之間。由于該項(xiàng)目不探索復(fù)雜的機(jī)器人運(yùn)動學(xué)或模擬計(jì)算，以至于它是難以確定exactly how many rules should be used到底有多少規(guī)則應(yīng)該被使用。但是，作為一個模糊邏輯的宗旨測試導(dǎo)航助手，八個規(guī)則就足夠了。盡管有許多相似的問題，ir和超聲波傳感器的距離有可靠地獲取途徑，魯棒性模糊邏輯通常是能夠能防止機(jī)器人跑向障礙物。5.