TA的案例
more英国机器人专业介绍
分类:留学新闻2021-05-25
机器人技术在许多领域(如制造业、卫生和对空间和深海等恶劣环境的远程探索)以及作为与人进行身体和社会互动的自治和半自治系统,越来越突出。并且该方向涉及了各种先进的工程和计算机科学概念,越来越多同学研究生专业选择该方向。这篇博客将为大家结构该专业学哪些课程。
以布里斯托大学举例,该校的机器人实验室是英国最全面的机器人创新设施之一,也是机器人研究的领先中心。
布里斯托大学MSc Robotics 机器人课程设置
Robotics Systems 机器人系统
This unit introduces robotic systems through a project-based coursework concerning a real mobile robot. Students are guided through a combination of laboratory worksheets and lectures. Students are supported to study the sub-components of a robotic system (e.g. robotic sensing, motion control, intelligent decision making), and to bring these together into an integrated autonomous system. These learning objectives are focused around achieving the performance of the mobile robot to complete a challenge task autonomously. 本单元通过有关实际移动机器人的基于项目的课程介绍机器人系统。 通过实验室工作表和讲座的组合来指导学生。 支持学生学习机器人系统的子组件(例如,机器人感应,运动控制,智能决策),并将它们组合到一个集成的自治系统中。 这些学习目标集中于实现移动机器人的性能以自主完成挑战任务。
Introduction to Artificial Intelligence 人工智能
It will provide an overview of the most established AI and Machine Learning approaches and paradigms and give students the opportunity to implement AI algorithms and use relevant software tools. Areas covered will included supervised learning (classification and regression, e.g. neural networks), unsupervised learning (clustering), probabilistic methods (e.g. Bayesian networks and Markov decision processes), genetic algorithms, and multi-agent systems. 它将概述最成熟的AI和机器学习方法和范例,并为学生提供实施AI算法和使用相关软件工具的机会。 涵盖的领域将包括监督学习(分类和回归,例如神经网络),无监督学习(聚类),概率方法(例如贝叶斯网络和马尔可夫决策过程),遗传算法和多智能体系统。
Dissertation 毕业论文
要求学生对相关文献进行深入调查,并进行一些基础研究,以确保他们的调查对该领域的现有研究有所帮助。 初步研究可能涉及广泛的活动,例如:进行定量调查,评估案例研究或行动研究,或者开发实验性的软件或硬件。 书面论文应明确如何设计和进行基础研究,并且对基础研究成果的讨论应与现有文献明确相关。 论文的正文应辅以对研究过程各个方面的严格审查,包括报告本身的设计和制作。
Robotics Research Technology and Methods 机器人研究科技和方法
Introduce all students to social, industrial and research context of robotics and autonomous systems, preparing students to perform independent research in these areas. 向所有学生介绍机器人技术和自治系统的社会,工业和研究环境,为学生做好在这些领域进行独立研究的准备。
Machine Vision 机械视觉
The definition and scope of what is meant by the term ‘machine vision’ is changing rapidly as, via increasing capabilities often enabled through innovation in machine learning, new and exciting contributions are being made in applications across a wide variety of disciplines - such as robot navigation, human-robot interaction, healthcare technologies and in precision agriculture. Given the ubiquity of camera equipped smartphones and the wide availability and variety of alternative imaging devices (e.g. thermal and RGB-D cameras), one should not be surprised to notice that machine vision technology is increasingly becoming a part of everyday life. Just as how a visual sense is important to human beings, it is arguably just as important to new forms of AI enabled systems. Therefore, the ability to “observe” the world with visual sensors, to “describe” the world from pictures or sequences of pictures, and to use this information to make useful decisions, is core to machine vision applications today. 机器视觉”一词的定义和范围正在迅速变化,因为通过机器学习中的创新可以提高功能,在各种学科(例如机器人)的应用程序中做出了令人兴奋的新贡献。导航,人机交互,医疗技术以及精准农业。鉴于配备摄像头的智能手机无处不在以及替代成像设备(例如热成像和RGB-D摄像头)的广泛可用性和多样性,人们不应该惊讶地注意到机器视觉技术正日益成为日常生活的一部分。正如视觉对人类的重要性一样,可以说,视觉对新形式的支持AI的系统也同样重要。因此,使用视觉传感器“观察”世界,从图片或图片序列“描述”世界以及使用此信息做出有用决策的能力是当今机器视觉应用程序的核心。
Human-Robot Interaction 人机交互
This module will provide an overview of human-robot interaction (HRI) as a research field. It will cover different contexts in which humans interact with robots now and in the future and how these contexts shape the physical and social constraints of the interaction. For example, we will look at the assisted living context, in which robots support humans in their homes and thus have to display socially appropriate behaviours. In contrast to that, we will look at collaborative robots in industrial settings, in which knowledge about task planning and part assembly is more important. The module also introduces the technologies needed in a HRI system, for example vision processing, speech recognition and natural language understanding, reasoning, output generation, and cognitive robot architectures. We will introduce the human factors that are relevant for a successful HRI (e.g., acceptance, trust, cognitive load) and how to measure these factors. Finally, the module describes how to set up, execute, and analyse HRI user studies. 本模块将概述人机交互(HRI)作为研究领域。它将涵盖人类现在和将来与机器人进行交互的不同环境,以及这些环境如何影响交互的物理和社会约束。例如,我们将研究辅助生活环境,在这种环境中,机器人在家里支撑人类,因此必须表现出与社会相适应的行为。与此相反,我们将研究工业环境中的协作机器人,其中有关任务计划和零件装配的知识更为重要。该模块还介绍了HRI系统中所需的技术,例如视觉处理,语音识别和自然语言理解,推理,输出生成和认知机器人体系结构。我们将介绍与成功的HRI相关的人为因素(例如,接受,信任,认知负担)以及如何衡量这些因素。最后,该模块描述了如何设置,执行和分析HRI用户研究。
选修课程
Bio-Inspired Artificial Intelligence 生物启发的人工智能
Nature has found clever solutions for the design of intelligent systems. Chemical networks, cells, brains and societies are able to self-organise to perform seemingly complex tasks. These behaviours result from evolution, development, and learning.
大自然为智能系统的设计找到了明智的解决方案。 化学网络,细胞,大脑和社会能够自我组织以执行看似复杂的任务。 这些行为来自进化,发展和学习。
With this course we aim to take inspiration from nature to engineer intelligent systems for real-world applications. Each lecture looks at a biological system and extracts basic principles that can be implemented in reality. Topics covered include neural networks, machine learning, artificial evolution, cellular systems, DNA computing, swarm intelligence, and bio-inspired robotics. 通过本课程,我们旨在从自然界中汲取灵感,为实际应用设计智能系统。 每堂课都着眼于一个生物系统,并提取出可以在现实中实现的基本原理。 涵盖的主题包括神经网络,机器学习,人工进化,细胞系统,DNA计算,群智能和受生物启发的机器人技术。
Intelligent Information Systems 智能信息系统
This unit will introduce AI techniques for knowledge representation, information processing, fusion and decision making. It will adopt and application centred approach aimed at giving students experience at designing information systems. It also aims to provide students with a background in the following key areas: 本单元将介绍用于知识表示,信息处理,融合和决策的AI技术。它将采用以应用程序为中心的方法,旨在为学生提供设计信息系统的经验。它还旨在为学生提供以下关键领域的背景知识:
Knowledge Representation, including a brief overview of first order logic, probabilistic logic, semantic networks, and event reasoning under uncertainty (event representation, event correlation and event reasoning). 知识表示,包括一阶逻辑,概率逻辑,语义网络和不确定性下的事件推理的简要概述(事件表示,事件相关性和事件推理)。
Agent-based models for developing intelligent autonomous systems such as Belief–desire–intention models, or Markov Decision Process (MDPs). 用于开发智能自主系统的基于代理的模型,例如信念-愿望-意图模型或马尔可夫决策过程
Handling inconsistency in knowledge 处理知识不一致
Information fusion under uncertainty approaches and their comparisons, and applications
Real-world application scenarios: covering how to scope a problem/scenario, how to elicit domain knowledge, how to identify data items, how to develop a data-driven intelligent system given a specific real-world problem. 不确定性方法下的信息融合及其比较,应用
实际应用场景:涵盖在特定现实问题下,如何确定问题/场景的范围,如何获取领域知识,如何识别数据项,如何开发数据驱动的智能系统。
Assistive Robotics 辅助机器人
This module will introduce you to the fundamentals of assistive robotics. Starting with understanding a range of typical assistive scenarios and robots, together with the associated human physical, sensory and cognitive conditions and disabilities that need to be considered when designing and deploying such systems, you will also gain knowledge of key areas within robotics that are core to realising assistive robotic solutions. 本模块将向您介绍辅助机器人技术的基础知识。 从了解一系列典型的辅助场景和机器人开始,以及在设计和部署此类系统时需要考虑的相关人体物理,感官和认知状况以及残疾,您还将获得有关机器人技术中核心领域的知识。 实现辅助机器人解决方案。
Advanced Control & Dynamics 先进的控制与动力学
Control mathematics, such as matrix algebra, Laplace transform, z-transformer, differential equations, and difference equations, for control system modelling, analysis, and design. 控制数学,例如矩阵代数,Laplace变换,z变压器,微分方程和差分方程,用于控制系统建模,分析和设计。
Use of computational packages, such as Matlab, to analyse and design control systems.
使用诸如Matlab之类的计算包来分析和设计控制系统
Advanced control concepts such state-space representations, solution of state equations, controllability and observability; state-feedback, (pole placement) control design. 高级控制概念,例如状态空间表示,状态方程解,可控性和可观察性; 状态反馈(极点放置)控制设计。
Modelling and analysis of multivariable control systems, to convert from the transfer function model to state space representation, and vice versa. Evaluation of dynamic plant performance in aspect of controllability and observability. 对多变量控制系统进行建模和分析,以从传递函数模型转换为状态空间表示,反之亦然。 在可控性和可观察性方面评估动态工厂绩效。
Soft Robotics 机器人软件
Soft Robotics has recently emerged as an important new field that extends the design possibilities of conventional robotics. Instead of only using fully rigid components and actuators, soft robotics uses a much wider range of materials and morphologies while often taking inspiration from nature. This includes polymers, silicones, hydrogels, smart materials, shape memory alloys and many others. This opens an entirely new design space and enables novel robotic systems that can deal with uncertainty in their environment more reliably. 软机器人技术最近已成为一个重要的新领域,它扩展了传统机器人技术的设计可能性。 软机器人不仅使用完全刚性的部件和致动器,还使用范围更广泛的材料和形态,同时常常从自然界中汲取灵感。 这包括聚合物,硅酮,水凝胶,智能材料,形状记忆合金等。 这开辟了一个全新的设计空间,并使新型机器人系统可以更可靠地应对环境中的不确定性。
This course presents different aspects of soft robotics including technologies, materials, actuation and sensing mechanisms. It will explore how nonlinear dynamics inherent to soft structures can be exploited to improve performance. Furthermore, students will be introduced with state-of-the-art modelling and control techniques available for soft robots. 本课程介绍了软机器人技术的各个方面,包括技术,材料,致动和传感机制。 它将探索如何利用软结构固有的非线性动力学来改善性能。 此外,还将向学生介绍可用于软机器人的最新建模和控制技术。
以上信息均来自布里斯托大学官网https://www.bristol.ac.uk/study/postgraduate/2021/eng/msc-robotics/
如需进一步了解,或有任何相关疑问,欢迎大家在线咨询专业老师;也可以进入答疑中心给我留言,我会尽快与您联系为您解答。如果您对自己或孩子是否适合出国留学还有疑虑,欢迎参与前途出国免费评估,以便给您进行准确定位。点击新东方前途官网,获取更多新鲜留学资讯。
