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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).采用模糊逻辑控制使自主机器人避障设计William Martin 威廉马丁Submitted on December 4, 2009 提交于2009年12月4日CS311 - Final Project CS311 -最终项目1. 1 INTRODUC引言 One of the considerable hurdles to overcome, when trying to describe a real-world 其中一个很大的障碍需要克服，当试图用控制逻辑一阶来描述一个真实
32、世界设计在发现在这两个语义evaluations.评价中control scheme with first-order logic, is the strong ambiguity found in both semantics and设计设计设计是个强大的模糊区。Although one option is to utilize probability theory in order to come up with a mo虽然一个方案是利用概率论，以便得到一个更realistic model, this still relies on obtaining information about
33、 an agents environment with some现实的模型，这种获得信息的方法的精度仍然依赖于外部环境amount of precision.。然而，在其收集data by allowing for a level of tolerance.数据的公差允许的范围内,模糊逻辑允许利用不精确的间接方法来实现。在需要高精度or accuracy in a measurement is quite costly.测量时它是相当昂贵的。例如，超声波和红外线传感器在其距离内allow for fast and cost effective distance measurements wit
34、h varying uncertaint允许快速和有效的测量但其测量结果存在很大的不确定性。模糊逻辑控制的应用范围从利用六自由度1控制机器人手臂到从数字信号2中过滤噪音。2Due to its easy implementation, fuzzy logic control has由于其易于实现，模糊逻辑控制been popular for industrial applications when advanced differential equations become either一直流行在工业应用中,尤其是高级微分方程computationally expensive or offer
35、 no known solution.计算复杂或难以解决时。这个项目企图利用advantage of these fuzzy logic simplifications in order to implement simple obstacle avoidance简化模糊逻辑，以实现移动机器人的简单避障for a mobile robot.。2. 2 机器人具体实现 2.1. 2.1 底盘和传感器 The robotic vehicles chassis was constructed from an Excalibur EI-MSD2003 remote 机器人汽车的底盘由Excalibur
36、 MSD2003远程control toy tank.控制玩具坦克构成。该装置去除了所有电子，齿轮和其他多余的部分，只留下了个空架和两个带动坦克履带的直流电动机。然而，这使得它的表面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）棒来fill in empty spaces.填补空白处。由于高密度聚乙烯具有较低的表面能，当与其他材料粘接时它并不理想，所以用一丙烷棒与环氧粘合剂来提高表面温度，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红外传感器用于测量距离，其测量范围是10至80厘米，分别。为了更好安装，将一块2.5*15厘米铝被弯曲成3个135度角的小块。用户既可以将红外传感器采取三种（右，中，左）不同的测量距离分别是45度角。这种传感器通过安装带和高密度聚乙烯棒安装到坦克的底部。由于可靠地最小测量距离是10厘米，传感器放置在距离小车前面约9厘米处。这使得measurements to be taken very close to the front
38、of the robot.测量点非常接近机器人的前面。2.2. 2.2 电子器件In order to control the speed of each motor, pulse-width modulation (PWM) was used to 为了控制电机的速度，脉冲宽度调制（PWM）用来驱动两个L2722运算放大器以使其工作在开环模式（图1）。IC的高输入电阻使马达从供电的PWM电路中得到非常小功率。为了使电机的供电与其他器件的供电相分开，专门一个9.6 V镍镉电池为电机供电，而其余的电子器件用一个标准的9伏的电源供电。这样运算放大器在持续工作时就产生了很少的过热。这部分热通过一个小
39、热槽和一个散热风扇使其保持平衡。Fig. 图1.控制电路用于驱动每个直流电动机注：PWM信号电压在 between 0 and 5 V.0至5V之间。 2.3. 2.3 微控制器 Computation was handled by an Ardu 计算是由Arduino Duemilanove板和ATmega328微控制器完成的。该电路板具有低功耗和支持原始PWM信号的特点。此外，它还有一个适应快速变化的输入端。Arduino的编程语言具有C语言的形式。这个电路板提供了简易低成本的机器人开发电路。3. 3 避障的模糊控制方案 In order to apply fuzzy logic to
40、the rob 为了将模糊逻辑应用于机器人运作，而开发了一个诠释测量距离的设计。虽然最终的算法在很大程度上取决于of the robot itself and how it operates, some basic guidelines were followed.该机器人本身以及它如何运作，但还是要遵循一些基本准则的。类似的研究项目既提供模拟结果及模糊控制的执行方法。3,4,53,4,53.1. 3.1 附属功能 Three sets of membership functions were created to express degrees of membership for 由此引出了
41、三种附属功能,它们分别是测量distances, translational speeds, and rotational speeds.距离，平移速度和旋转速度。 This made for a total of two input 他们由2各输入隶属函数和8个输出隶属函数（图2）组成。三角形和梯形函数是专用的，因为它们计算速度快，易于修改。保持计算时间到最低限度是必要的，以便多组数据每秒（大约每40毫秒/个）都可以得到分析。距离隶属函数可以使来自距离红外传感器的距离信息迅速“模糊化”，而8速模糊值函数将其转换回准确的数值。 3.2.Rule base 3.2 基本规则Once the in
42、put data was fuzzified, the eight defined fuzzy logic rules (Table I) were 一旦输入数据模糊化，模糊逻辑定义的8个规则（表一）就被执行，以便将模糊值分配给平移速度和旋转速度。这导致每个组件的模糊输出有多个值。 It was then necessary to take the 因此有必要采用每个组件的最大值作为模糊值。最后，通过最大输出技术将这些模糊输出值“解模糊”，其结果是用来更新每个马达的速度。 5 (a) （一） (b) （二） (c) （三） Fig. 图2.附属函数分别用来测量（1）距离，（二）翻译速度，以及（
43、c）转动速度。 These functions were adapted from similar work done in reference 3. 这些函数从参考文献3类似的内容中借鉴而来。 6 Table I. The fuzzy logic rule base used for the control scheme 表1 用于控制的模糊逻辑规则库 4. 4 结果 The fuzzy control scheme allowed for the robot to quickly respond to obstacles it could 该设计使模糊控制的机器人能够迅速回应检测到的障碍。
44、这使得它能沿着墙壁和角落行动而不会撞击到任何障碍物。然而，由于红外传感器的尺寸取决于周围物体几何形状，很多时候机器人也无法检测到障碍物。例如，当红外线光束击中地面的倾斜处事，则反映出远离传感器和不认为是一个障碍对象。此外，使用有限数量的规则可能限制机器人的一些反应。有些文章建议多达40条规则应加以使用，而另一些倾向于10和20条之间。由于该项目不探索复杂的机器人运动学或模拟计算，以至于它是难以确定exactly how many rules should be used到底有多少规则应该被使用。但是，作为一个模糊逻辑的宗旨测试导航助手，八个规则就足够了。尽管有许多相似的问题，IR和超声波传感器
45、的距离有可靠地获取途径，鲁棒性模糊逻辑通常是能够能防止机器人跑向障碍物。5. 5 结论 There are several easy improvements that could be made to future iterations of this 这有几个简单的改进方法可以借鉴到未来的迭代项目中以提高机器人的性能。最引人注目的将是实现红外线或超声波传感器在伺服使他们能够每次扫描一个完整的180度。然而，这种类型的改革可能会破坏一些模糊逻辑的简化。另一个有用策略是使用一些不同的传感器，使数据获取多元化。该红外传感器在设计中使用的是最小测量距离为10厘米的，所以很多障碍在前面无法可靠地检测
46、到。同样，最大测量距离是80厘米的传感器也是对远处的物体difficult to react to objects far away.很难作出反应。超声波传感器在稍微增加成本和反应时间的同时，可以有效增大测量范围。最后，定义更多附属函数可以help improve the rule base by creating more fine tuned responses.改善机器人的反应能力。但是，这样做将再次增加系统的复杂性。因此，一个简单的模糊控制机器人的移动方向和移动速度的方案，通过本设计成功的实现了。在没有经过大量的其他方法的研究时，很难确定这是否是一个值得的方法这是很明显，但是模糊逻辑系
47、统的设计，提供了一种对某些层面的简单化研究的方法。此外，它是一个处理环境中各种实际不确定性的novel approach to dealing with high levels of uncertainty in real-world environments.新方法。6. 6 参考文献 1 1 Ed. 埃德米贾姆希迪，注Vadiee，并吨罗斯和控制，模糊逻辑：软件和硬件应用，（普伦蒂斯霍尔：黄俊英，新泽西州）292-328。 2 2 Ibid, 232-261. 同上，232-261。 3 3 WL Xu, SK Tso, and YH Fung, Fuzzy reactive contro
48、l of a mobile robot incorporating a 轮候册许，水库草，和YH丰，“模糊控制结合被动移动机器人真实/虚拟目标转换“战略，机器人和自主系统，23（3），171-186 (1998).（1998年）。 4 4 V. Peri and D. Simon, “Fuzzy logic control for an autonomous robot,” 2005 Annual Meeting of 五围和D.西蒙，“模糊逻辑控制的自主机器人为”2005年年度会议北美模糊信息处理学会，337-342（2005）。 5 5 A. Martinez, E. Tunstel, a
49、nd M. Jamshidi, Fuzzy-logic based collision-avoidance for a mobile 答：马丁内兹，大肠杆菌Tunstel和M.贾姆希迪，“模糊逻辑基础的防撞为移动机器人“Robotica，12（6）521-527（1994）。 6 6 WL Xu, SK Tso, and YH Fung, Fuzzy reactive control of a mobile robot incorporating a 轮候册许，水库草，和YH丰，“模糊控制结合被动移动机器人真实/虚拟目标转换“战略，机器人和自主系统，23（3），171-186 (1998). （1998年）。
