复杂机电系统的集成智能研究

1. 复杂机电系统的集成智能研究 范衠 广东省数字信号及图像处理重点实验室 汕头大学 2013年9月12日

2. 2007-2010 项目负责人 高级机电系统的自动设计 2008-2011 项目负责人 基于视觉感知的熔池边缘提取和 自动焊接控制 2008-2011 项目负责人 商业化医用交通机器人系统关键技术研究 资助金额164万丹麦克朗 资助金额205.4万丹麦克朗 资助金额206.4万丹麦克朗 智能系统开发 复杂机电系统的集成智能研究 智能设计

3. 高级机电系统的自动设计 设计 规范 设计 仓库 知识结合 键合图 表示 子代 生成 验证 人机交互 演化 综合 评价 引导 重组 积累 成功的概念 设计方案 设计 仓库 知识提取 领域知识结合 最终设计实现 • 提出了机电系统设计自动化的框架，提升了传统的计算机辅助设计的智能水平 • 解决了对系统拓扑结构和参数的并行自动设计及多领域统一建模的问题 • 研究了层次化设计、协同设计、稳健设计等设计方法 • 对多种机电系统的应用实例进行了自动设计，发表SCI期刊论文多篇 机电系统设计自动化的框架

4. 表现型 树的表示形式 键合图 集成了基因编程和键 合图的智能设计过程 基因型 键合图的表达方法

5. 设计案例：模拟RLC滤波器电路设计 键合图 电路 问题 性能

6. (3) TF2 TF1 TF TF (1) 截止时间大约为50ms (2) 0 MSe 1 0 1 Constant1 I R C R C IS I RS 物理结构 CS1 RL CS2 IL ò K lim Gain1 Integrate1 键合图 性能 设计案例：打印机机械 子系统再设计 表示进化前 表示进化后

7. 设计案例 车辆悬挂 Fs szs K11(s) K2(s) ms ms u + R4  A C5 mu + kt mu szu Fr kt - +  机械系统 控制器协同设计 主动控制系统

8. 演化综合 键合图 设计案例：DC-DC转换器电路混合控制器协同设计 电路 性能 键合图 性能 传统方法 电路 进化后 研究成果发表在计算机理论顶级期刊IEEE Transactions on Evolutionary Computation （影响因子：4.810）

9. 设计案例：微机电系统 研究成果发表在电子工程顶级期刊IEEE Transactions on Industrial Electronics （影响因子：5.165 ）和 Applied Soft Computing（影响因子：2.860）

10. 在基于基因编程和键合图的机电系统设计自动化方面开展了开创性的研究。在基于基因编程和键合图的机电系统设计自动化方面开展了开创性的研究。 • 经过在该领域超过10年的研究，目前已经出版了一本英文专著，发表了12篇国际期刊论文，6篇著作章节，及26篇会议论文。 • 研究成果受到包括加州伯克利大学Prof. Alice Agogino和大不列颠哥伦比亚大学的Prof. Clarence De Silva教授的关注，并在现有结果的基础上取得了新的研究成果。 • 受到美国BEACON国家科技中心主任Prof. Erik Goodman的邀请，就该项目研究做了特邀报告，并与中心达成协议开展合作研究。 科研成果

11. 智能系统开发I：服务机器人 Lb3 Lb4 SB B Lb2 Lb1 C La3 La4 A La1 La2 SA • 服务机器人能够在非结构化环境下进行工作 • 采用分而治之的模块化集成方法，提供对移动机器人工作环境可扩充问题的解决方案 • 该方案没有采用昂贵的传感器和技术，极具大规模商业应用的潜力 • 开发出原型机设备通过验收

12. Motivation Aged population and increasing lack of working force In hospitals, nearly 30% of expense is from logistic activities The aim of the project is to propose design of a transportation system using mobile robots to reduce the human working load. A robot in every home Service robot industry will soon experience what PC industry experienced in last century Software and hardware technology already mature, standard and platform needed Quite many robotic systems exist, but They still work in constrained environment It is difficult to scale up the system as needed Reliability and adaptability needs to be continuously improved Our ‘divide and conquer’ approach is based on hybrid map

13. Our Robot NESTOR - I

14. NESTOR I

15. 已有的机器人 LOG FILE Lb3 Lb4 SB B Lb2 Lb1 C La3 La4 A La1 La2 SA 研发的机器人平台 0101010100101 10100101010 1001010100101 0101010100101 Swisslog Transcar Aethon Tug Care-o-bot HelpMate Speciminder

17. NESTOR-II: Navigation

18. Features and Potential • NESTOR-II is developed based on off-the-shelf software and hardware components (Standardization) • Scalability is largely enhanced • One corridor -> multiple corridors • One floor -> multiple floors • Working areas can be conveniently added or removed as needed (adaptability) • A network of wireless connected robots can be considered as movable agents in the ubiquitous computing environment of smart buildings (Reliability) • Gather information from their own sensors • Communicate with various sensors installed in the environment

19. Extension of the Project • Can be suitable for an industrial oriented project, with commercialization of products as a target • To be implemented in collaboration with • Local hospitals, communes • Research centers

20. 智能系统开发II：基于视觉感知的焊接机器人 • 融合图像处理和机器学习的方法，使机器视觉能够代替人的视觉，感知并判断焊接质量的好坏 • 引入包含用户先验知识的概率模型，提高了视觉处理的准确率 • 在实时条件下同时进行焊缝跟踪和熔池边缘提取 • 开发出一套原型机系统

21. The First Automated Design Robot Lipson, H., Pollack J. B., (2000), "Automatic Design and Manufacture of Artificial Lifeforms",Nature 406, pp. 974-978.

22. A typical Mechatronic System 22

23. The State of the Art in Mechatronics Research • Two major themes • Conceptual design • Hardware embodiment • Research activity is biased • More work is on hardware embodiment • Focus is on motion control • Automated conceptual design is gaining importance • Towards real ‘mechatronic design’ philosophy • To meet growing competitive challenges (e.g. to shorten time-to-market, etc) • Using design automation and optimization as a mainstream discipline in high-tech product development • May be the next wave after EDA (Electronic Design Automation)

24. Extending Design Capability • Most Conventional CAD and CAE only verify products on a testbed based on computational simulation • Most Important decisions are made by human designers • What components to be used in design • How to configure topology of components • What parameters to be assigned to components

25. Intelligence-Embedded CAD • We seek an intelligence-embedded CAD that can • gather and process information • foster design insights • guide the design process • support the human designers to make decisions • Bio-inspired and statistic-based learning appear to be very promising approaches

26. Where does the intelligencecome from? • Human’s cognitive process • the most direct answer • establishment of cognitive theory of human design is very intriguing, but complex • Methods based on statistic learning are emerging • applied not only in intelligent design but also in intelligent control • Nature’s evolution process • nature invented many wonderful designs of species that far exceed human designs in terms of complexity, adaptability, without any intervention of humans

27. Nature-Inspired Computing and Design Nature spends a prohibitively long period of time to “evolve” its designs current computer technology provides a possible answer to shorten the time consumption to an acceptable range - digital evolution algorithms based on the principles of biological evolution and natural selection **Evolutionary Computation**

28. Design Automation of Mechatronic Systems • Many design automation tools only seek to optimize parameters for a fixed topology • Many engineering design methods can only be used in a single physical domain So: • An automated, multi-domain and open-ended design methodology with BG/GP is introduced • Bond graphs: represent dynamic systems across mixed physical domains • Genetic programming: strong topology and parameter exploration capability to create and evolve structures representing dynamic systems

29. What is a Bond Graph? R x k F(t) L m i b C Bond graphs consist of elements and bonds: C, I, and R elements Power source elements includingSe and Sf Transformer (TF) and gyrator (GY) 0-junctions and 1-junctions Bonds

30. Why Genetic Programming? • Low-level Building Blocks  provide high-level functions • Improved Genetic Programming: powerful search capacity for open-ended topology search space • possibly achieve more innovative design solutions (independent of designer's past experience and design methods) • encodes Bond Graphs by GP trees as the basic modular building blocks Crossover Mutation

31. GP Functions for Constructing a Bond Graph add_C add_I add_R insert_J0 insert_J1 replace_C replace_ I replace_ R + - * / endn endb endr erc

32. Examples of a Bond Graph Constructor Modifiable Site (1) R add_R 1 1 (1) (3) (3) (2) ERC or + / - add_R function Modifiable Site (3) Modifiable Site (2) Modifiable Site (1)

33. Examples of a Bond Graph Constructor (1) insert_J0 (3) (1) (2) insert_J0 function R Modifiable Site (3) Modifiable Site (1) Modifiable Site (2) R 0 Modifiable Site (1) 1 1

34. How to Create a Bond Graph EMBRYO 2 a 1 add_C add_I end 3 1 2 b 4 c 3 4 +/- end end erc end end insert_J1 end 5 c d add_I erc erc end end 6 5 6 e end erc end insert_J1 • Key • Node site: • Bond site: • Node operator: • Bond operator: 7 e f add_C end end 7 8 8 g end erc end end

35. How to Create a Bond Graph C8 g f I6 17 e d C4 RS I3 15 c b 02 11 Se 1 RL a

36. Genotype-Phenotype Mapping Genetic Programming Tree Bond Graphs Models Of Dynamic Systems Physical Realization Of Dynamic Systems Genotype Phenotype Intermediate Stage

37. An Illustration of Genotype-Phenotype Mapping Phenotype Genotype

38. Overall Design Procedure A Design specifications, constraints, preferences Run GP operation Design analysis, Specify embryo design For each population Selection Create initial populations of GP Tree Reproduction - Crossover, Mutation B For each individual Termination condition? No B Fitness evaluation Yes Realize physical design A

39. Case Studies Analog Electronic RLC Filter Design Typewrite Mechanical Subsystem Redesign Vehicle Suspension Controller-Plant CoDesign Multiple Tank system Hybrid Controller Design Micro-Electromechanical Systems (MEMS) DC-DC Converter Design (hybrid controller and plant co-design) 39

40. Case Study I – Analog Filter Design Target Filter Characteristics: Embryo model GP Parameters Used: • High-pass filter : pass > 1kHz, suppress < 1kHz • Low-pass filter : pass <1kHz, suppress > 1kHz • Number of generations: 100 • Pop sizes: 300*13 + 2500*2 • Initial population: half_and_half • Max depth: 50 • Initial depth: 4-6 • Selection: Tournament (size=7) • Crossover: 0.9 • Mutation: 0.3 Problem Setup

41. Design Evolves!

42. Bond Graph Model of The High-Pass Filter

43. RLC Filter Design - Realized high-pass filter circuit Performance is shown in previous slide

44. Design Performance b) Low-pass filter a) High-pass filter

45. Repeatability of Results “A Novel Evolutionary Engineering Design Approach for Mixed-Domain Systems”, Engineering Optimization, Vol. 36, No.2, April 2004, pp127-147

46. Case Study II – Printer Redesign • Printer Redesign Problem • electromechanical system includes rotating type ball that must be rotated rapidly • The original problem was presented by C. Denny and W. Oates of IBM, Lexington, KY, in 1972

47. The Initial Design -- Printer Printer Drive Mechanism

48. Problem of Initial Design Input : step function Feedback gain : K =1 Output : position of rotational load(JL ) Unacceptable vibration for 1700-1800ms Position Response of Load(JL)

49. Design Specification • The problem with the design is that the position output of the load JL has poorly damped vibrations SPECIFICATION: • Reduce the vibration of the load • Want the settling time to be less than 70ms when the input voltage is stepped from zero to one

50. Subsystem for Redesign Through preliminary analysis, this subsystem is believed to be the key for System performance improvement.