使用Microsoft Robotics Development Studio开发机器人技术

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"Robot Development Using Microsoft Robotics Development Studio" 本书《Robot Development Using Microsoft Robotics Developer Studio》深入探讨了使用微软机器人开发工作室（Microsoft Robotics Developer Studio，简称MRDS）进行机器人开发的编程技术。作为一本专注于MRDS的稀缺资源，它由Shih-Chung Kang、Wei-Tze Chang、Kai-Yuan Gu和Hung-Lin Chi合著，由CRC Press出版，是Taylor & Francis Group的一个分支。 MRDS是一个综合的、基于Windows的开发环境，旨在简化和促进各种技能水平的开发者对机器人的编程。它提供了一个仿真平台，允许开发者在实际硬件部署之前测试和调试他们的代码。MRDS支持多种编程语言，包括C#和VB.NET，使得熟悉这些语言的程序员能够快速上手。书中详细讲解了如何利用MRDS创建、设计和控制机器人系统。内容可能涵盖了基础的机器人概念，如传感器和执行器的工作原理，以及如何在MRDS中集成这些组件。此外，可能还讨论了机器人导航、避障算法、路径规划和行为建模等高级主题。读者将了解到如何利用MRDS的可视化工具来构建和模拟机器人的行为，以及如何通过网络和通信协议与外部设备交互。此外，书中的实例和项目可能引导读者实践从简单任务到复杂自主行为的机器人程序设计。本书还可能强调了软件工程的最佳实践，如模块化设计、错误处理和测试，这些都是确保机器人程序可靠性和稳定性的关键因素。对于有兴趣在学术研究、工业应用或娱乐领域探索机器人技术的读者来说，这本书是一个宝贵的资源。尽管MRDS已经在2011年后不再更新，但其理念和方法对于理解现代机器人开发仍然有价值。许多概念和技术，如仿真、多机器人协作和实时系统控制，都是当今机器人学的核心部分。因此，即便MRDS软件本身已经过时，书中介绍的原理和实践经验仍然适用于其他类似开发平台，如ROS（Robot Operating System）。《Robot Development Using Microsoft Robotics Developer Studio》是一本为那些希望通过编程实现机器人梦想的人提供的指南，无论他们是初次接触机器人学还是有经验的开发者。通过学习这本书，读者将获得机器人开发的坚实基础，以及在这一领域进一步探索的能力。

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1Chapter

Challenges and Solutions

in Robotics

  ro bot   the ctional novel titled Rossum’s Universal

Robots

published in 1921 by the Czechoslovakian writer Karel Capek. In the novel,

an R.U.R. (i.e., Rossum’s Universal Robot), which is the equivalent of a robot, is described

to be composed of a collection of components and is smart enough to replace the humans

working in a designed area. Soon aer Capek’s novel was released, the concept of robots

was illustrated in theaters and movies, stimulating the public’s interest in the emergence of

robots and their development and encouraging further research into this eld.

Nevertheless, in the real world, it is not easy to create a smart robot as envisioned in

Karel Capek’s novel. A smart robot is an entity that can operate autonomously and com-

bines technological breakthroughs in machinery, automation, electrical, optics, electron-

ics, soware, communication, safety mechanisms, and creative design. It is a product that

merges high-end technologies and has a high commercial value. As illustrated in Figure 1.1,

knowledge about robot development crosses several domains and can be broadly classied

into four layers, namely hardware, soware, applications, and user layers.

1. Hardware layer: is determines the hardware capabilities of the robot, which is

analogous to the bodily functions of a human being. is layer includes the manip-

ulators, sensors, actuators, controllers, and so on. Hardware construction requires

expert knowledge about machinery, automation, electrics, optics, and electronics.

2. Soware layer: is determines the responsiveness of the robot, which is analogous

to the senses and reactions of a human being. e categories in this layer include sub-

jects such as concurrent programming and the robot’s perception and simulations. It

requires soware and communication protocols for construction.

Capek, Karel (2001). R.U.R., translated by Paul Selver and Nigel Playfair, Dover Publications, p. 49.

2 ◾ Robot Development Using Microsoft Robotics Developer Studio

3. Application layer: is determines the usage of the robot and the application domain,

which is analogous to a human being’s specialization and profession. is layer

concerns domain problems, engineering design, and system integration. As a result,

during construction of the robot, participation is required from engineers and experts

of various domains.

4. User interface layer: e rst three layers—that is, the hardware, soware, and appli-

cation layers—form the main components of the industrial robot. However, as robot-

ics advances technologically and applications spread, service robots, which is a new

type of robot, cannot be built using knowledge from the three previous layers alone.

Service robots require the inclusion of the user interface layer. is layer embeds a

user-centered design (UCD), human-robot interaction (HRI), robot education, and

other similar topics. Construction of this layer requires knowledge from the peda-

gogical, psychological, and scientic elds.

Due to the fact that robotics crosses multiple knowledge domains, diculty and com-

plexity are introduced into its research and development. e following sections discuss

the challenges faced from the perspective of smart-robot development.

1.1 RESEARCH CHALLENGES: MOVING FROM INDUSTRIAL-TYPE

TO SERVICE-TYPE ROBOTS

As mentioned in the previous section, we can classify robots based on their functionalities

into industrial-type robots and service-type robots. In Figure 1.2, the le image represents

a typical industrial robot and the right image represents a service robot.

Industrial robots are focused mainly on manufacturing. As their deployed environ-

ment is known and basic, the robots are usually xed at a certain location. An indus-

trial robot’s design goal is focused on precision control, rather than on goals such as

articial intelligence. Consequently, these robots do not require large amounts of com-

putational ability, although they are considered the most mature type of robot technol-

ogy available, with very stable and reliable system functionalities. At present, industrial

robots are automated to the point that they replace the work of humans in performing

Knowledge

of industiral

robot

Knowledge

of service

robot

User Interface

Applications

Software

Hardware

User-centered designHuman-robot interaction Education

Domain problems Engineering designSystem integration

Concurrent programming Perception Simulation

Manipulators Sensors Actuators Controller

FIGURE 1.1 e four layers of knowledge in robot development.

Challenges and Solutions in Robotics ◾ 3

tasks such as component assembly, processing, and welding. Industrial robots have

been widely used in industries such as automobile manufacturing and precision elec-

tronics manufacturing.

In contrast to industrial robots, the goal of a service robot is to provide a service to

address the needs of humans. Being closer to human habitats, the environment to which

service robots are deployed is unknown and there is a need for reactions to sudden changes.

In terms of executing tasks, the industrial robot need only repeat the execution of a single

task, whereas service robots may need to execute a range of tasks and perform decision

making in unknown situations. As a consequence, the design of a service robot is much

more complicated than that of an industrial robot in terms of both hardware and soware.

For instance, service robots require a highly exible hardware construction, a exible

design, multitasking capabilities, and the capability to make adjustments and corrections

quickly and easily. In addition, it is necessary for service robots to appear sincere and

friendly, as they will interact with humans directly.

Since the 1960s, an increasing number of domain experts have invested time and eort

in robotic research work. However, during those earlier years, computers had very lim-

ited computational capabilities and this limited the growth of associated robotic research

areas such as articial intelligence. Industrial robots were the main driving force behind

robotic research during these early stages. In the past two decades, signicant advances

have been made to computational capabilities and robotics technology has progressed rap-

idly from experimental forms to real-life applications. Robots, like humans, are now able

(a) (b)

FIGURE 1.2 Robots classied as industrial-type and service-type based on their functionalities.

(a) Industrial-type robots: single purpose, sequential procedure, goal-oriented, known environ-

ment, high precision. (b) Service-type robots: multiple purpose, concurrent procedure, task-

oriented, uncertain environment, fuzzy decision-making.

4 ◾ Robot Development Using Microsoft Robotics Developer Studio

to perform actions independently. As such, service robots that can autonomously make

decisions have attracted both research and industrial interests. eir possible applications

are actively being explored and discovered.

1.2 CHALLENGES IN THE SOFTWARE PLATFORM FOR SERVICE ROBOTS

Technological hardware advances made over the past decades have given consumers the

exibility of choice in hardware components. ese hardware advances are the result of

research in building precise sensing instruments, stable control devices, and highly e-

cient transmission motors. However, although these hardware improvements have vastly

extended robots’ physical functionalities, the resulting performance is still constrained

by the operating soware. Currently, soware development on robots is faced with three

research challenges:

1. How can the soware coordinate multiple tasks?

2. How can the soware process tasks and events in sync?

3. How can we unify the development platform?

1.2.1 Challenge 1: How Can the Software Coordinate Multiple Tasks?

A typical electronic appliance or equipment has only a single purpose—for example, an

automatic door is only required to open or shut and a fan only needs to spin to generate

wind. In comparison, an intelligent robot diers in that its design is to support multiple dif-

ferent tasks to fulll its goal of performing numerous complex duties. For example, a robot

that faces an obstacle while moving toward a target destination should be able to avoid

the obstacle and continue proceeding toward the destination without human intervention.

Although this seems to be a basic action that even a young child can accomplish, for a

robot (or should one say, for the robot designers) it is a tedious and complex procedure.

More specically, the robot in this situation is similar to the toddler: it needs to distin-

guish rst what is considered an obstacle, determine the dimensions of the obstacle, and

perform some calculations. According to its own capabilities, experiences, and knowledge

of avoidance plans from a repository, it then executes the avoidance task. Here, the avoid-

ance object and target destination are the two goals that the robot has to simultaneously

consider while moving. is example illustrates the signicance of the research problem

of coordinating multiple duties. Nevertheless, this is a very basic demonstrative example.

Instead, if the robot was assigned to a more complex job such as aged care or security

patrolling, the required duties would be more varied and the behavior design of these

robots would be signicantly more dicult.

1.2.2 Challenge 2: How Can the Software Process Tasks and Events in Sync?

A human conceptually constructs the place he or she is in through the information derived

from the ve senses, namely sight, hearing, smell, taste, and touch. Similarly, a robot utilizes

sensors with diering capabilities to collect data about its environment before deducing an

Challenges and Solutions in Robotics ◾ 5

appropriate reaction. However, the data provided by sensors can arrive asynchronously in

a complex and rapidly changing manner. erefore, for robot soware design, it is a chal-

lenge to combine all the data received while actively controlling the robot’s motion. is is

a limitation of the conventional approach of robot design to utilize sequential procedure

soware that enables a robot to perform only one task at a time. us the soware cannot

process sensed readings while also controlling the motion element. As a result, it is unable

to react to changes swily. As an example, consider how we normally cross a road. To cross

a road, we simultaneously observe trac coming from our le and right (i.e., sensing) and

quickly cross the road when it is clear (i.e., movement). ese two tasks occur simultane-

ously and do not interfere with each another, or in other words, they are performed in a

concurrent procedure. Although the concurrent procedure concept, commonly referred to

as parallel computing, distributed computing, and multi-thread processing, is well devel-

oped, its applications in robots are still rudimentary. In particular, it is very dicult for a

concurrent program to extract the essence of sensed information (e.g., determining incom-

ing vehicles) and, at the same time, control movement (e.g., crossing the road). Issues such

as determining whether or not to sense and move a robot simultaneously, prioritizing the

tasks to perform, and dissecting and processing information at the same time collectively

present a major challenge to building a robot’s program design.

1.2.3 Challenge 3: How Can We Unify the Development Platform?

As early industrial robots incorporated only basic functionalities, the design of each robot

was targeted to meet industry demands and components. Consequently, dierent soware

systems were developed, such as the ERSP platform originating from the Evolution Robotics

and the commercial platform NI as proposed by National Instruments (NI). ere are

also some other platforms for research purposes, such as the Player/Stage platform origi-

nating from the National Science Foundation (NSF) and Stanford University. Although

these soware systems are incompatible with one another, each system performs well in

its respective area. However, this incompatibility is a soware barrier in the development

of universal robots. In addition, it would be unfortunate if the robot soware developed

for one system could not be easily ported for use in another system. e list of issues to be

addressed includes designing a single common interface for universal robots, enabling a

central public repository for robot soware, giving developers the exibility to reuse pre-

viously implemented soware components, and so on. All of these issues represent major

challenges for soware platform design.

1.3 EDUCATIONAL CHALLENGES IN THE DEVELOPMENT

OF SERVICE ROBOTS

e successful development of robotics research is heavily reliant on contributions from

domain experts, similar to many other elds of study. At the present stage of robotics

education, courses relating to the hardware layer (such as hardware organization design,

machine dynamics, and electronics) and soware design (such as parallel processing,

image recognition, and articial intelligence) have developed progressively over the years

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