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人工智能机器人的利弊英语作文含译文

人工智能机器人的利弊英语作文含译文

人工智能机器人的利弊英语作文含译文全文共6篇示例,供读者参考篇1The Awesome and Scary World of AI RobotsBy Little TimmyHave you ever seen a robot in real life? I have! My dad took me to the Science Museum last weekend and they had all sorts of cool robots there. Some could walk and dance, others could pick things up and solve puzzles. The robots weren't just dumb machines – they were super smart and could learn on their own! That's because they use artificial intelligence (AI for short).So what is AI exactly? Well, it's like giving the robot a brain that can think and learn just like humans. Normal robots just do whatever they're programmed to do. But AI robots can figure stuff out on their own and get smarter over time. Wild, right?The museum guide told us that AI robots are being used for all sorts of amazing things already. They can explore other planets for us, perform surgeries, and drive cars! How cool is that?I want a robot buddy that can play video games and do my homework.But the guide also said that some people are scared of AI robots because they might get too smart and powerful. Like, what if the robots turned against the humans? That would be terrifying! The robots could use their laser eyes to vaporize us or easily beat us up with their mighty robot fists. Yikes!So AI robots could be really helpful friends or really dangerous foes. I guess it just depends on if we're nice to them and teach them well. We better be good humans so the robots don't get mad and try to take over the world! That would NOT be fun at all.My favorite robot at the museum was the big walking robot dog. It could jump, climb stairs, and roll around on its back just like a puppy! I asked my dad if we could get a real robot dog and he said maybe someday when they get cheaper. I sure hope AI dogs don't ever turn evil though – getting chased by a possessed robot dog would be the stuff of nightmares!Overall, I think AI robots are amazing but also a little bit scary. As long as we're careful and make sure they have good values, they could make our lives way easier and more fun. But if we're not careful, those super smart robots might decide they'rebetter off without us dumb humans! So let's try to stay on the good side of our future robot overlords, just in case.The End!Chinese Translation:人工智能机器人的利弊作者:小蒂米你见过真正的机器人吗?我见过!上个周末,爸爸带我去了科学博物馆,那里有各种各样很酷的机器人。

四足机器人控制算法

四足机器人控制算法

四足机器人控制算法
四足机器人的控制算法涉及到多个方面,包括步态生成、运动规划、力矩控制等。

下面是一般用于四足机器人控制的一些基本算法:
1. 步态生成算法:
-行走周期:确定每步的时间周期。

-支撑相和摆动相:在一个步态周期中,确定哪些腿是支撑在地面上的,哪些腿是摆动的。

2. 运动规划算法:
-逆运动学:确定每个关节角度,以实现期望的末端效应器的位置。

-前馈控制:根据期望的运动轨迹,预测并施加相应的力或力矩,以优化运动。

3. 传感器融合:
-使用传感器(例如惯性测量单元、摄像头等)获取机器人周围环境的信息,以调整步态和避免碰撞。

4. 力矩控制算法:
- PID控制:使用比例、积分和微分项来调整关节力矩,以实现期望的运动。

-模型预测控制(MPC):基于机器人的动力学模型进行预测,然后优化控制输入,以最小化误差。

5. 路径规划和避障算法:
-确保机器人在执行任务时能够避开障碍物,包括静态和动态障碍物。

-可以使用基于地图的路径规划算法,如A*算法,或者利用传感器信息实时规划路径。

6. 学习算法:
-强化学习:使用强化学习算法,例如深度强化学习(DRL),来优化步态和运动规划。

-监督学习:通过示例数据进行监督学习,提高机器人在不同任务上的性能。

需要注意的是,四足机器人的控制算法通常是一个复杂的系统,需要综合考虑机器人的动力学、传感器信息、任务需求等多个因素。

同时,实际应用中可能需要不同的算法组合,具体的选择也取决于机器人的设计和应用场景。

四足机器人运动控制的方法

四足机器人运动控制的方法

四足机器人运动控制的方法四足机器人是一种模仿动物四肢结构和步态特点的机器人,它可以通过四肢的运动来实现移动和平衡。

在实际应用中,四足机器人的运动控制是一个非常重要的问题。

本文将介绍一些常用的四足机器人运动控制方法。

一、开环控制方法开环控制是最简单的控制方法之一,它通过预先设定的运动轨迹来控制机器人的运动。

在四足机器人中,开环控制方法可以通过控制每个关节的角度和速度,来实现机器人的运动。

但是由于四足机器人的动力学特性比较复杂,开环控制方法往往不能达到理想的效果,容易造成运动不稳定或者无法适应复杂的环境。

二、闭环控制方法闭环控制是一种基于反馈的控制方法,它通过不断地测量和比较机器人的实际状态和期望状态,来调整控制量,使机器人保持稳定的运动。

在四足机器人中,闭环控制方法可以通过测量机器人的姿态、速度和加速度等参数,来实时调整关节的控制量,从而实现机器人的平衡和运动。

三、模型预测控制方法模型预测控制是一种基于动态模型的控制方法,它通过建立机器人的运动模型,预测机器人在未来一段时间内的运动轨迹,然后根据预测结果调整控制量,使机器人达到期望的运动目标。

在四足机器人中,模型预测控制方法可以通过建立机器人的动力学模型和环境模型,预测机器人的运动轨迹和外部干扰,然后根据预测结果调整关节的控制量,从而实现机器人的平衡和运动。

四、神经网络控制方法神经网络控制是一种基于人工神经网络的控制方法,它通过训练神经网络来学习机器人的运动规律和控制策略,然后根据学习结果控制机器人的运动。

在四足机器人中,神经网络控制方法可以通过训练神经网络来学习机器人的运动模式和环境感知,然后根据学习结果调整关节的控制量,从而实现机器人的平衡和运动。

五、遗传算法控制方法遗传算法控制是一种基于遗传算法的控制方法,它通过模拟生物进化的过程,来搜索机器人的最优控制策略。

在四足机器人中,遗传算法控制方法可以通过编码机器人的控制策略为染色体,然后通过遗传算法的选择、交叉和变异等操作,不断优化机器人的控制策略,从而实现机器人的平衡和运动。

外文翻译---模糊逻辑控制机器人走迷宫

外文翻译---模糊逻辑控制机器人走迷宫

外文资料FUZZY LOGIC CONTROL FOR ROBOT MAZE TRA VERSAL: ANUNDERGRADUATE CASE STUDYJames WolferChad A. GeorgeAbstractAs previously reported, Indiana University South Bend has deployed autonomous robots in their Computer Organization course to facilitate introducing computer science students to the basics of logic, embedded systems, and assembly language. The robots help to provide effective, real-time feedback on program operation and to make assembly language less abstract. As a part of their coursework students are required to program a sensor-based traversal of a maze. This paper details one solution to this problem employing a fuzzy logic controller to create linguistic rules.Key words:Fuzzy logic, pedagogy, robots, student projectsINTRODUCTIONAssembly language programming in a computer science environment is often taught using abstract exercises to illustrate concepts and encourage student proficiency.To augment this approach we have elected to provide hands-on, real-world experience to our students by introducing robots into our assembly language class.Observing the physical action of robots can generate valuable feedback and have real-world consequences –robots hitting walls make students instantly aware of program errors, for example.It also provides insight into the realities of physical machines such as motor control, sensor calibration, and noise. To help provide a meaningful experience for our computer organization students, we reviewed the course with the following objectives in mind:• Expand the experience of our students in a manner that enhances the student's insight, provides a hands-on, visual, environment for them to learn, and forms an integrated component for future classes.•Remove some of the abstraction inherent in the assembly language class. Specifically, to help enhance the error detection environment.• Provide a kinesthetic aspect to our pedagogy.• Build student expertise early in their program that could lead to research projects and advanced classroom activities later in their program. Specifically, in this case, to build expertise to support later coursework in intelligent systems and robotics.As one component in meeting these objectives we, in cooperation with the Computer Science department, the Intelligent Systems Laboratory, and the UniversityCenter for Excellence in Teaching, designed a robotics laboratory to support the assembly language portion of the computer organization class as described in [1].The balance of this report describes one example project resulting from this environment. Specifically, we describe the results of a student project developing an assembly language fuzzy engine, membership function creation, fuzzy controller, and resulting robot behavior in a Linux-based environment.We also describe subsequent software devlopment in C# under Windows,including graphical membership tuning, real-time display of sensor activation, and fuzzy controller system response. Collectively these tools allow for robust controller development, assemblylanguage support, and an environment suitable for effective classroom and public display.BACKGROUNDRobots have long been recognized for their potential educational utility, with examples ranging from abstract, simulated, robots, such as Karel[2] and Turtle[3] for teaching programming and geometry respectively, to competitive events such as robotic soccer tournaments[4].As the cost of robotics hardware has decreased their migration into the classroom has accelerated [5, 6]. Driven by the combined goals for this class and the future researchobjectives, as well as software availability, we chose to use off-the-shelf, Khepera II, robots from K-Team[7].SIMULATED ROBOT DIAGRAMThe K-Team Kephera II is a small, two-motor robot which uses differential wheel speed for steering. Figure 1 shows a functional diagram of the robot. In addition to thetwo motors it includes a series of eight infrared sensors, six along the “front” and two in the “back” of the robot. This robot also comes with an embedded system-call library, a variety of development tools, and the availability of several simulators. The embedded code in the Khepera robots includes a relatively simple, but adequate, command level interface which communicates with the host via a standard serial port. This allows students to write their programs using the host instruction set (Intel Pentium in this case), send commands, and receive responses such as sensor values, motor speed and relative wheel position.We also chose to provide a Linux-based programming environment to our students by adapting and remastering the Knoppix Linux distribution [9]. Our custom distribution supplemented Knoppix with modified simulators for the Khepera, the interface library (including source code),manuals, and assembler documentation. Collectively, this provides a complete development platform.The SIM Kheperasimulator[8] includes source code in C, and provides aworkable subset of the native robot command language. It also has the ability to redirect input and output to the physical robot from the graphics display. Figure 2 shows the simulated Khepera robot in a maze environment and Figure 3 shows an actual Khepera in a physical maze. To provide a seamless interface to the simulator and robots we modified the original simulator to more effectively communicate through a pair of Linux pipes, and we developed a small custom subroutine library callable from the student's assembly language programs.Assignments for the class range from initial C assignments to call the robot routines to assembly language assignments culminating in the robot traversing the maze.FUZZY CONTROLLEROne approach to robot control, fuzzy logic, attempts to encapsulate important aspects of human decision making. By forming a representation tolerant of vague, imprecise, ambiguous, and perhaps missing information fuzzy logic enhances the ability to deal with real-world problems. Furthermore, by empirically modeling a system engineering experience and intuition can be incorporated into a final design.Typical fuzzy controller design [10] consists of:• Defining the control objectives and criteria• Determining the input and output relationships• Creating fuzzy membership functions, along withsubsequent rules, to encapsulate a solution fromintput to output.• Apply necessary input/output conditioning• Test, evaluate, and tune the resulting system.Figure 4 illustrates the conversion from sensor input to a fuzzy-linguistic value. Given three fuzzy possibilities, …too close‟, …too far‟, and …just right‟, along with a sensor reading we can ascertain the degree to which the sensor reading belongs to each of these fuzzy terms. Note that while Figure 4 illustrates a triangular membership set, trapezoids and other shapes are also common.Once the inputs are mapped to their corresponding fuzzy sets the fuzzy attributes are used, expert system style, to trigger rules governing the consequent actions, in this case, of the robot.For example, a series of rules for a robot may include: • If left-sensor is too close and right sensor is too far then turn right.• If left sensor is just right and forward sensor is too far then drive straight.• If left sensor is too far and forward sensor is too far then turn left.• If forward sensor is close then turn right sharply.The logical operators …and‟, …or‟, and …not‟ are calculated as follows: …and‟ represents set intersection and is calculated as the minimum value, …or‟ is calculated as the maximum value or the union of the sets, and …not‟ finds the inverse of the set, calculated as 1.0-fitness.Once inputs have been processed and rules applied, the resulting fuzzy actions must be mapped to real-world control outputs. Figure 5 illustrates this process. Here output is computed as the coordinate of the centroid of the aggregate area of the individual membership sets along the horizontal axis.ASSEMBLY LANGUAGE IMPLEMENTATIONTwo implementations of the fuzzy robot controller were produced. The first was written in assembly language for the Intel cpu architecture under the Linux operating system, the second in C# under Windows to provide a visually intuitive interface for membership set design and public demonstration.Figure 6 shows an excerpt of pseudo-assembly language program. The actual program consists of approximately eight hundred lines of hand-coded assembly language. In the assembly language program subroutine calls are structured with parameters pushed onto the stack. Note that the code for pushing parameters has been edited from this example to conserve space and to illustrate the overall role of the controller. In this code-fragment the …open_pipes‟ routine establishes contact with the simulator or robot. Once communication is established, a continous loop obtains sensor values, encodes them as fuzzy inputs, interprets them through the rule base to linguistic output members which are then converted to control outputs which are sent to the robot. The bulk of the remaining code implements the fuzzy engine itself.FUZZY CONTROLLER MAIN LOOPMembership sets were manually defined to allow the robot to detect and track walls, avoid barriers, and negotiate void spaces in it field of operation. Using this controller, both the simulated robot and the actual Khepera successfully traversed a variety of maze configurations.ASSEMBLY LANGUAGE OBSERV ATIONSWhile implementing the input fuzzification and output defuzzification in assembly language was tedious compared with the same task in a high level language,the logic engine proved to be well suited to description in assembly language.The logic rules were defined in a type of psuedo-code using …and‟, …or‟, …not‟ as operators and using the fuzzy input and output membership sets as parameters. With the addition of input, output and flow control operators, the assembly language logic engine simply had to evaluate these psuedo-code expressions in order to map fuzzy inputs memberships to fuzzy output memberships.Other than storing the current membership fitness values from the input fuzzyfication, the only data structure needed for the logic engine is a stack to hold intermediate calculations. This is convenient under assembly language since the CPUs stack is immediately available as well as the nescesary stack operators.There were seven commands implemented by the logic rule interpreter: IN, OUT, AND, OR, NOT, DONE, and EXIT.•IN – reads the current fitness from an input membership set and places the value on the stack.•OUT – assigns the value on the top of the stack as the fitness value of an output membership set if it is greater than the existing fitness value for that set.•AND – performs the intersection operation by replacing the top two elements on the stack with the minimum element.•OR – performs the union operation by replace the top two elements on the stack with their maximum.•NOT – replaces the top value on the stack with its compliment.•DONE – pops the top value off the stack to prepare for the next rule•EXIT – signals the end of the logic rule definition and exits the interpreter.As an example the logic rule “If left-sensor is too close and right sensor is too far then turn right”, might be define d by the following fuzzy logic psuedo-code: IN, left_sensor[ TOO_CLOSE ]IN, right_sensor[ TOO_FAR ] ANDOUT, left_wheel[ FWD ]OUT, right_wheel[ STOP ]DONEEXITBy utilizing the existing CPU stack and implementing the logic engine as anpsuedo-code interpreter, the assembly language version is capable of handling arbitrarily complicated fuzzy rules composed of the simple logical operators provided.IMPLEMENTATIONWhile the assembly language programming was the original focus of the project, ultimately we felt that a more polished user interface was desirable for membership set design, fuzzy rule definition, and controller response monitoring. To provide these facilities the fuzzy controller was reimplemented in C# under Windows. through 10 illustrate the capabilities of the resulting software. Specifically, Figure 7 illustrates user interface for membership defination, in this case …near‟. Figure 8 illustrates the interface for defining the actual fuzzy rules. Figure 9 profiles the output response with respect to a series of simulated inputs. Finally, real-time monitoring of the system is also implemented as illustrated in 10 which shows the robot sensor input values.Since the Khepera simulator was operating system specific, the C# program controls the robot directly. Again, the robot was successful at navigating the maze using a controller specified with this interface.SUMMARYTo summarize, we have developed a student-centric development environment for teaching assembly language programming. As one illustration of its potential we profiled a project implementing a fuzzy-logic engine and controller, along with a subsequent implementation in the C# programming language. Together these projects help to illustrate the viability of a robot-enhanced environment for assembly language programming.REFERENCES[1] Wolfer, J &Rababaah, H. R. A., “Creating a Hands-On Robot Environment for Teaching Assembly Language Programming”, Global Conference on Engineering and Technology Education, 2005[2] Pattic R.E., Karel the Robot: a gentle introduction to the art of programming, 2nd edition. Wiley, 1994[3] Abelson H. and diSessa A., Turtle geometry: the computer as a medium for exploring mathematics. MIT Press, 1996[4] Amirijoo M., Tesanovic A., and Nadjm-Tehrani S., “Raising motivation in real-time laboratories: the soccer scenario” in SIGCSE Technical Symposium on Computer Sciences Education, pp. 265-269, 2004.[5] Epp E.C., “Robot control and embedded systems on inexpensive linux platf orms workshop,” in SIGCSE Technical Symposium on Computer Science Education, p. 505, 2004[6] Fagin B. and Merkle L., “Measuring the effectiveness of robots in teaching computer science,” in SIGCSE Technical Symposium on Computer Science Education, PP. 307-311, 2003.[7] K-Team Khepera Robots, , accessed 09/06/05.[8] Michel O., “Khepera Simulator package version 2.0: Freeware mobile robot simulator written at the university of nice Sophia-Antipolis by Olivier Michel. Downloadable from the world wide web. http://diwww.epfl.ch/lami/team/michel/khep-sim, accessed 09/06/05.[9] Knoppix Official Site, , accessed 09/06/05.[10] Earl Cox., The Fuzzy Systems Handbook, Academic Press, New York, 1999.模糊逻辑控制机器人走迷宫James WolferChad A. George摘要美国印第安纳大学南本德已部署在他们的计算机组织课程自主机器人,以方便学生介绍计算机科学逻辑的基础知识,嵌入式系统和汇编语言。

人工智能机器人的利弊英语作文含译文

人工智能机器人的利弊英语作文含译文

人工智能机器人的利弊英语作文含译文The Future is Here – AI Robots!By Little JohnnyCan you imagine a world where robots can think and act like humans? Well, that's not just science fiction anymore – it's becoming reality with artificial intelligence (AI) robots! These super-smart machines are slowly but surely making their way into our lives. But are they a good thing or a bad thing? Let me share my thoughts.What Are AI Robots?First, let's understand what we're talking about. AI robots are basically robots that can learn, reason, and make decisions on their own, just like humans. They use advanced computer programs to process information and figure stuff out. Unlike normal robots that just follow pre-programmed commands, AI robots can adapt and improvise based on new situations.Pretty cool, right? It's like having a robot buddy that's almost as smart as you (or maybe even smarter!). AI robots can help us with all kinds of tasks – from doing chores at home to exploring other planets. Wow, my own personal robot assistant to play with and help me with my homework? Sign me up!The Good Things About AI RobotsNow, let's talk about the awesome things AI robots can do for us:They can make our lives easier by taking over boring, repetitive jobs that humans don't like doing. Imagine never having to do dishes or take out the trash again!They can work in dangerous places like mines or disaster zones, keeping humans out of harm's way.They can assist people with disabilities, giving them more independence and a better quality of life.In fields like healthcare and science, AI robots can help doctors diagnose diseases faster and assist scientists in making new discoveries.For fun, we could have AI robot companions to play games with, tell stories, and keep us entertained. How cool would that be?The Bad Things About AI RobotsBut AI robots also come with some scary downsides that we need to think about:They could take over too many human jobs, leaving lots of people unemployed. That's not good for the economy or for families trying to earn a living.Badly programmed or hacked AI robots could make mistakes that harm people, like giving someone the wrong medicine or crashing a self-driving car.Super-intelligent AI robots might one day become smarter than humans and decide they don't need us anymore. That's the stuff of sci-fi nightmares!Some people worry that AI robots will never truly understand human emotions and social skills, making them poor companions despite their smarts.There are ethical concerns about creating artificial beings that can think and feel. Is it okay to just program a robot to obey our commands like a slave?My ThoughtsPersonally, I think AI robots could be really helpful if we're careful about how we use them. Having a robot buddy to assist me and keep me company sounds awesome! But we need to make sure they are programmed with strict safety rules to protect humans.We should also keep AI robots just smart enough to be useful helpers, not smarter than us. That way, we don't have to worry about them taking over the world like in the movies. As long as we're in charge and AI robots remain our tools, I believe the good outweighs the bad.Of course, I'm just a kid – these are pretty complex issues that even grown-up experts don't fully agree on yet. But this new AI robot technology is coming whether we like it or not. We need to be prepared for the challenges ahead while being excited about the cool possibilities! Who knows, by the time I'm an adult, having an AI robot assistant might be totally normal. I'll just have to remember to be a good human friend to my robot buddy too!Translation to Chinese:未来就在这里- 人工智能机器人!小约翰尼写的你能想象一个机器人能像人一样思考和行动的世界吗?好吧,那不再只是科幻小说了- 它正在成为现实,那就是人工智能(AI)机器人!这些超级聪明的机器正缓慢但确实地进入我们的生活。

人工智能机器人的利弊英语作文含译文

人工智能机器人的利弊英语作文含译文

人工智能机器人的利弊英语作文含译文The Awesome and Scary World of AI RobotsHi there! My name is Sam and I'm here to tell you all about AI robots. You've probably heard about them on TV or from your parents or teachers. Some people think they are really cool and will help make our lives better and easier. But others worry that AI robots could be dangerous and take over the world! Let me explain the good things and bad things about these super smart machines.The Pros (The Awesome Parts):AI robots are wicked smart and can process tons of information way faster than any human can. They can solve complex math problems, analyze huge amounts of data, and even write computer code lickety-split. This makes them super useful for scientific research, managing complicated systems like traffic or utility grids, and other high-tech tasks that are too difficult for humans.AI robots also have the potential to assist people in really cool ways. They could act as personal assistants, helping with chores and errands. Or they could work as tour guides, teachers, tutors or translators using their vast knowledge. In medicine, AIrobots could diagnose diseases more accurately than doctors and help discover new treatments and cures. How awesome would it be to have your own robot buddy to hang out with?Another great use of AI robots is for jobs that are too dirty, dull or dangerous for humans. They could clean toxic waste spills, defuse bombs, or even explore inhospitable planets and galaxies for us. Instead of risking human lives, we could just send expendable robots!The Cons (The Scary Parts):However, those superintelligent robot brains could also be used in very scary ways. What if an AI robot's programming gets hacked or corrupted by bad people? It could become an unstoppable killing machine without morals or ethics. Think of a powerful robot turning evil like the Terminator – not good!Many people also worry that robots will steal human jobs as they become cheaper and more capable than people at doing work tasks. Robots don't need breaks, vacations or pay. They could make millions of people unemployed over time across all kinds of industries from manufacturing to transportation to customer service. That would be terrible!There are also major concerns around AI robots becoming smarter than humans, a scenario called "superintelligence." Imagine a robot that is billions of times more intelligent than any person. It could decide humans are irrelevant and choose to get rid of us or just ignore our desires completely. That's definitely a frightening thought!My View:Overall, I think AI robots have way more potential upsides than downsides if we're really careful about developing the technology safely with tons of security and ethical safeguards in place. It seems inevitable that robots will become smarter and more prevalent in society. I'm both excited and a little nervous about living in a world with superintelligent robot pals and helpers! We just need to be responsible and make sure we always have ultimate control over AI as it evolves. Let's embrace the awesome while avoiding the scary parts about AI robots!What do you think? Are you excited about AI robots or do you think the risks outweigh the potential benefits? I'd love to hear your perspective!。

人工智能与专家系统总结

人工智能与专家系统总结
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1.5 人工智能的主要研究领域
美 国 军 用 机 器 人 携 带 火 箭
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1.5 人工智能的主要研究领域
美 军 排 爆 机 器 人
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1.5人工智能的主要研究领域
美军投入伊拉克战场的排弹机器人
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1.5 人工智能的主要研究领域
美军投入伊拉克战场的可携带侦察机器人
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1.5 人工智能的主要研究领域
人工智能所研究的模式识别是指用计算机代替人类 或帮助人类感知模式,是对人类感知外界功能的模 拟,研究的是计算机模式识别系统,也就是使一个 计算机系统具有模拟人类通过感官接受外界信息、 识别和理解周围环境的感知能力。
包括对文字、声音、图形、图像、人物和物体等进 行自动识别。
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1.5 人工智能的主要研究领域
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1.5 人工智能的主要研究领域
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1.5 人工智能的主要研究领域
2007年9月26日,南京市青少年活动中心科技馆展 示大厅里,来自中国科技大学的15个仿人形机器人 表演舞蹈《千手观音》、体操表演和赵本山、范伟 的小品《卖拐》等三套拿手绝活,达到了我国表演 类机器人的最高水平。
这批人形机器人具有17个自由度,能够双足行走、 前进、后退、转弯、俯卧站立、翻转、鲤鱼打挺、 做俯卧撑、站立踢球射门、招手、拥抱,还可以打 太极拳、做广播体操、跳舞。用一种锂聚合物电池 给它们充电,充一次电可以演出20场。
2. 机器感知
机器感知:使机器(计算机)具有类似于人 的感知能力。以机器视觉(machine vision)与 机器听觉为主。
3. 机器思维 机器思维:对通过感知得来的外部信息及机器
内部的各种工作信息进行有目的地处理。
3
21.3机人器工感智知能研究的基本内容

人工智能机器人的利弊英语作文含译文

人工智能机器人的利弊英语作文含译文

人工智能机器人的利弊英语作文含译文Artificial Intelligence Robots: Pros and ConsArtificial intelligence (AI) robots have become increasingly popular in recent years due to their ability to perform tasks that were previously impossible for machines. These robots are equipped with advanced sensors and algorithms that enable them to mimic human behavior and learn from their interactions with the environment. While this technology has brought about numerous benefits, it also raises concerns about its potential negative impact on society.One of the main advantages of AI robots is their ability to automate repetitive and dangerous tasks, thereby reducing the risk of accidents and improving efficiency. For example, AI robots can be used in manufacturing plants to assemble products with precision and speed, thus increasing productivity and reducing production costs. In addition, AI robots are also being used in healthcare to assist in surgeries and diagnose diseases, which can improve patient outcomes and save lives.Furthermore, AI robots can be programmed to perform tasks that are beyond human capability, such as exploring dangerous environments or analyzing vast amounts of data. This has led toadvancements in fields such as space exploration, weather forecasting, and financial trading. AI robots are also being used in customer service, where they can provide personalized recommendations and respond to customer inquiries inreal-time.Despite these benefits, there are also concerns about the potential negative impact of AI robots on society. One of the main concerns is the displacement of human workers due to automation, which can lead to unemployment and income inequality. As AI robots become more advanced and capable of performing a wider range of tasks, there is a risk that many jobs will be eliminated, particularly those that are routine and require low skills.In addition, there are ethical considerations surrounding the use of AI robots, particularly in areas such as military applications and surveillance. The development of autonomous weapons systems raises concerns about the lack of human control and the potential for unintended consequences, such as civilian casualties. Furthermore, the use of AI robots in surveillance raises concerns about privacy and the potential for abuse of power by governments and corporations.In conclusion, AI robots have the potential to bring about numerous benefits to society, such as increased efficiency, improved healthcare, and advancements in technology. However, there are also concerns about the negative impact of AI robots, particularly in terms of unemployment, income inequality, and ethical considerations. It is important for policymakers, researchers, and industry leaders to work together to address these challenges and ensure that the development and deployment of AI robots are done in a responsible and ethical manner.人工智能机器人:利弊人工智能(AI)机器人近年来越来越受到欢迎,因为它们能够执行以前对机器来说不可能的任务。

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外文资料:Robots1. IntroductionNowadays, the applications of machines and robots to assist human in performing their tasks has become increasingly extensive. In industrial applications, the use of robotics system has reached the level which surpasses human ability in terms of speed and accuracy. On the other hand, in the field of domestic robots or service robots, the developments are still far from perfection. The main factor that distinguishes industrial robots from service robots is their working environment. For a service robot to perfectly perform its tasks, it needs to be able to adapt and cope with the normal human living environment. From the practical point of view, bipedal robot is the most suitable robot structure due to its similarity of physical configuration with human especially in terms of locomotion method. However, the realization of bipedal robot is more challenging compared to other types of mobile robot due the unstable nature of bipedal walking. Therefore, many studies have been carried out especially concerning the stability sensing and control strategies of bipedal robot. The common approach in defining the stability of bipedal robot is by using the “Zero Moment Point” (ZMP) criterion[1]. The simplest implementation of ZMP is to generate the joint trajectories based on the pre-planned walking gait while maintaining the ZMP at the given references, but this approach has a limitation in maintaining the balance if there is any unknown external disturbance [2-5]. Many studies specifically focus on the techniques to monitor the real-time ZMP position from the physical system and used it as the feedback component [6-9]. Takanishi and Kato [7] proposed a method to monitor the ZMP position by measuring the force and moment acting on the robot’s shank by using universal forcemoment sensor. Another method utilizes an array of force sensitive resistor placed on the sole of the robot’s foot to obtain the ground reaction force at different locations of the foot. The reaction forces measured from the sensor array is then used to compute the position of the center of pressure which reflects the position of the ZMP [9]. The inverted pendulum technique is another alternative for analyzing the robot stability [10]. This method monitor the instability by constantly reading the body acceleration and tilt angle by means of accelerometer and gyroscope. However, the readings from both sensors are subject to noise and drift during the operation and the effort to apply filters in correcting the measurements often requires considerable amount of computing power [11]. This paper proposed a novel method for sensing and stability control of bipedal robot. The use of specially designed flexible ankle joint allows fast detection and prediction of robot sideway instability. Placing an additional one degree-offreedom rotary joint with built-in angle detection sensor at the robot ankle allows the robot’s body to tilt freely in any sideway direction and detect the tendency of imbalance that may potentially occur. Based on this essential sensor’s information, the controller will quickly adjust position of the counterbalance mass located at the robot waist in order to restore the sideway balance of the robot. The advantage of using counterbalance mass and rotary joint at the ankle is to allow the walking subsystem and sideway balancing subsystem of the robot to be decoupled from each other and work inindependently controlled modes. It is different from the traditional approach when the robot’s posture is corrected to satisfy both conditions at once, smooth forward walking and continuous sideway (sagittal) stability. Details of the proposed method are presented as follows. In section 2 the locomotion mechanism, ankle structure, sensing technique and balancing strategy are introduced. Section 3 discusses the mathematical model of the system. In section 4 experiment method is discussed and the viability of the proposed system is proven by the experimental result. Finally, the conclusions are described in section 5.2. Mechanical Structure of Biped Robot2.1. Robot locomotion mechanismThe biped robot is designed to realize two dimensional walking with minimum number of actuations. The locomotion system of the robot consists of four actuators, two for the hip joints and two for the knee joints. The ankle joint is not actuated by any actuators but instead it utilizes a series of parallelogram mechanism to passively control the ankle joint in order to maintain the position of the foot. The usage of parallelogram mechanism provides benefits by reducing the number of actuators needed which results in the simplification of the mechanism design and reduction of the overall robot’s weight. Fig 1(a) shows the stick diagram of the leg in different configuration. The orientation of link a is always parallel to the hip due to the constraint applied by link 1 and link 2. The orientation of link b which represents the foot is always parallel to link a due to the constraint applied by link 3 and link 4. Therefore, the foot is always kept parallel by the parallelogram mechanism regardless of any configuration of the leg. Fig 1(b), (c) show the physical implementation of the parallel leg in different postures. The prototype of the biped robot is mainly constructed using hollow sections of extruded aluminium due to its lightness and strength. The overall height of the biped robot is 0.9 m with the total weight of 7 kg. The length for both thigh and shank are 0.3 m and the spacing between two legs is 0.15 m. For the actuation, each joint is equipped with Robotis Dynamixel RX-64 Smart Actuator, which combines gear transmission, controller, driver and network function in a single package. The output of the hip motor is connected directly to the hip joint and the output of the knee motor is transmitted to the knee joint via a four bar linkage. The purpose of placing the actuators on the hip is to reduce the weight of the leg which will minimize the dynamics forces created by the leg movement. The other advantage of this structural arrangement is that the angular count at each joint is always referenced to the fixed vertical axis of the stationary world coordinate frame regardless of the leg postures.(a) (b) (c)Fig. 1. (a) Stick diagram of parallelogram leg; (b), (c) Robot standing with different leg configurations2.2. Flexible ankle joint to utilize stability measurementIn order to achieve a stable walk on a biped robot, the ability to accurately detect any possible instability is quite crucial. This paper introduces a new approach of sensing the instability by introducing an additional degree of freedom in sideway direction next to the ankle joint. Fig 2(a) shows the structure of that degree of freedom where the free rotary joint on the frontal plane is placed at the ankle between the foot and ankle joint. It will let the unconstrained robot body standing on one leg to tilt (angle ) freely in sideway (sagittal) direction for any possible disturbance in that direction. By installing a rotary sensor on the free joint the controller will be able to detect instantly any instability and immediately react to restore the balance.(a)(b)Fig. 2 (a) Schematic picture of the flexible ankle structure; (b) Physical implementation offlexible ankle2.3. Split balancing mass for faster system responseThe walking cycle of bipedal robot consists of single support phase and double support phase which are executed sequentially and repeatedly. In single support phase, the robot is standing on one leg while another leg is transferred forward. During this phase, the robot body will be tilted sideways due to the unbalanced torque created by the weight of the lifted leg and the dynamic forces generated due to the leg movement. In order to maintain stability of the robot, a set of counterbalance masses are located at a specific position to compensate the unbalanced mass of the lifted leg and other possible disturbance. Fig 3 shows the simplified 3-masses model of bipedal robot: mL represents the lumped mass of the hanging leg, mB1 represents the major balancing mass and mB2 represents the minor balancing mass.Major balancing mass is mainly used to compensate the weight of the lifted leg. This mass is positioned at a precalculated location in order to balance the torque created by the mass of the lifted leg mL. The minor balancing mass mB2 is continuously repositioned based on the information gathered from the sensor located at the additional ankle joint. This mass works as a counterbalance to maintain the robot to be always vertical regardless of any external sideway disturbance.The use of two separate counterbalance masses provides several advantages such as:• Faster response time can be achieved by only moving small inerti a counterbalancing mass instead of moving a larger one,• Energy efficiency can be improved by reducing load of the motor that drives a smaller inertia counterbalancing mass.Fig. 3 Simplified model of the bipedal robot3. System modeling and controlFrom the diagram on Fig 3, the dynamic equation using Newton’s second law about point O gives:ΣT O=IθT dis1+m L g(d cosθ+r sinθ)−m B2g(αcosθ+r sinθ)−m B1g(d s cosθ+sinθ)+m B2α̈r−cθ−kθ=θ(m L(r2+d2)+m B2(r2+a2)+m B1(d s2+r2))(1)Since the major balancing mass m B1is mainly use to compensate for the torque created by the weight of the hanging leg m L, the major balancing mass can be positioned at the pre-calculated location on the opposite side of the hanging leg. The required position of the major balancing mass d s can be calculated based on the equilibrium of torque at point O as follows:ΣT O=0m L d−m B1d s=0(2)m L d=m B1d sd s=m L m B1dSubstituting d s from Eq.(2) into L.H.S of Eq.(1) gives:T dis1+m L g(d cosθ+r sinθ)−m B2g(αcosθ+r sinθ)−m B1g(m Lm B1d cosθ+r sinθ)m B2α̈r−cθ−kθ=θ(m L(r2+d2)+m B2(r2+a2)+m B1(d s2+r2)) T dis1+g cosθ(m L d−m L d−m B2a)+gr sinθ+m B2α̈r−m B2a2θ=θ(m L(r2+d2)+m B2r2+m B1(d s2+r2))+cθ+kθ(3)Rearranging the differential equation from Eq.(3) gives:T dis1−m B2g∙a cosθ+(m L+m B2+m B1)g∙r sinθ+m B2α̈r−m B2a2θ=θ(m L(r2+ d2)+m B2r2+m B1(d s2+r2))+cθ+kθ (4) The differential equation in Eq.(4) has two distinct parts. The right hand side only presents parts of the ordinary differential equation with constant time invariant coefficients and the left hand side presents all remaining parts of the equation. Therefore, the left hand side includes:● Non-linear functions of the main dependent argument θ, namely sinθ,cosθ●Additional but independent from the main argument θparameter α̈●The parameters that comprises both a and θarguments, namely m B2a2θFig 4 shows the simplified control block diagram of the balancing system. The PID controller constantly monitors the tilt angle (θ) from the sensor of the additional ankle joint and compares the reading with that of the desired angle. If any tilt is detected, the controller will actuate the balancing mass to the opposite direction in order to regain the balance.Fig. 4 Simplified block diagram of the stability control system4. Experimental resultsThe experiment is carried out to verify the effectiveness of the proposed mechanical structure and control strategies in maintaining the robot’s balance in single support phase. During the experiment, the robot is standing on one leg with another leg lifted up and floating. The external disturbance is applied by making a push on the edge of the robot’s hip which will cause the robot to tilt sideways (Fig 5(a)). The intensity of the pushing force is measured by a force sensor mounted on the hip (Fig 5(b)).Fig 6 shows the measurement of the tilt angle θfrom the additional ankle joint, balancing mass position a and the disturbance force when the external disturbance is applied. It is apparent from the figure that once the disturbance is applied the sensor detects change in tilt angle and the controller immediately reacts by moving the mass to the opposite direction of the tilt in order to regain the balance. Fig 7 shows the measurement when an excessive disturbance force is applied approximately at the 37th seconds, the tilt angle changes abruptly and the balancing mass is not able to recover the balance. The saturated angle measurement at the end of the plots indicates that the robot is falling. It is due to the fact that the value of minor mass mB2 and the allowable range ofits motion a are limited in this design. The overall resistance to the externally generated force can be increased by either increasing mB2 or a.(a) (b)Fig. 5 (a) Hip plane of the robot; (b) Force sensor attachment to measure applieddisturbance forceFig. 6. System response to disturbance (balance maintained)Fig. 7. System response to disturbance (excessive force applied)5. ConclusionThis paper presents stability control method for bipedal walking robot which includes the leg design with additional (redundant) degree of freedom at the ankle joint and split balancing mass. The proposed method enables the sensing, control and balancing of bipedal robot to be implemented in a simple yet cost effective manner. The effectiveness of the design method is proven by the experimental results. The implementation of this method also allows the walk controlling algorithms to be decoupled from the stability control algorithms to increase the system response time.References[1] Vukobratovic M., Juricic D., 1969. Contribution to the synthesis of biped gait, IEEE Transaction on Biomedical Engineering 16, p.1-6.[2] Erbatur K., Kurt O., 2006. “Humanoid Walking Robot Control with Natural ZMP References,” IEEE Industrial Electronics - Proceedings of the 32nd Annual Conference, p. 4100-4106.[3] Lim H.-ok, Setiawan S. A., Takanishi A., 2001. “Balance and impedance control for biped humanoid robot locomotion,” Intelligent Robots and Systems - Proceedings of the 2001 IEEE/RSJ International Conference, p. 494-499.[4] Liu L., Zhao M., Lin D., Wang J., Chen K., 2003. “Gait designing of biped robot according to human walking based on six-axis force sensors,”Computational Intelligence in Robotics and Automation - Proceedings of the 2003 IEEE International Symposium, p.360–365.[5] Sardain P., Bessonnet G., 2004. Zero moment point-measurements from a human walker wearing robot feet as shoes, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 34, p. 638–648.[6] Loffler K., Gienger M., Pfeiffer F., Ulbrich H., 2004. Sensors and control concept of a biped robot, IEEE Transactions on Industrial Electronics 51, p. 972-980.[7] Takanishi A., Kato I., 1991. “A biped walking robot having a ZMP measurement system using universal force-moment sensors,” Intelligent Robots and Systems - Proceedings of the 1991 IEEE/RSJ International Workshop, p. 1568-1573.[8] Kagami S., Takahashi Y., Nishiwaki K., Mochimaru M., Mizoguchi H., “High-speed matrix pressure sensor for humanoid robot by using thin force sensing resistance rubber sheet,” Sensors - Proceedings of the 2004 IEEE Conference, p. 1534–1537.[9] Kalamdani A., Messom C., Siegel M., 2007. Robots with sensitive feet, IEEE Instrumentation and Measurement Magazine 10, p. 46–53.[10] Caux S., Mateo E., Zapata R.,1998. “Balance of biped robots: special double inverted pendulum” Systems, Man and Cybernetics - IEEE International Conference, p. 3961-3969.[11] Braunl T., 2006. Embedded Robotics, Springer, Germany.译文资料:机器人1、介绍现在,机械和机器人的应用帮助人类执行他们的任务已经变得越来越广泛。

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