关键词： 四足机器人   对角小跑   结构设计   运动学   动力学  

摘要： 四足机器人对工作环境的要求低，具有环境适应性强、机动性好、运动稳定性好和承载能力强等优点。本文对四足机器人的结构设计、运动学模型与动力学模型进行了较为深入系统的研究，研究成果对于提高四足机器人的移动速度、稳定性以及环境适应性具有重要意义。 设计了整体拥有十六个主动自由度，四个被动自由度的四足机器人机构模型，在对机器人工作空间分析的基础上，设计了液压驱动的单腿拥有三个主动自由度的四足机器人结构。 通过定义坐标系及相关参数建立了四足机器人机构分析模型，采用齐次变换方法对机器人的支撑腿和摆动腿分别进行了正运动学与逆运动学分析，利用雅可比矩阵对机器人的摆动腿进行了速度分析与加速度分析。 针对四足机器人的对角小跑步态，利用虚拟腿代替机器人的对角双腿，将四足机器人的对角小跑步态转换为单足步态，使机器人各条腿之间的运动解耦。利用虚拟弹簧负载倒立摆模型中的弹性腿代替机器人的有质量的关节型腿，通过分析摆动腿的运动对四足机器人动力学模型的影响，提出将飞行相时摆动腿与机体之间的运动，以及着地相时摆动腿与机体、支撑腿之间的运动作解耦处理，得到了四足机器人飞行相与着地相的动力学简化模型。根据四足机器人对角小跑的运动特点，通过运动等效替代，建立了混合机体模型，进一步简化了机器人着地相时的动力学模型。依据四足机器人的动力学简化模型，利用拉格朗日方程建立了四足机器人飞行相与着地相的动力学方程。 在Adams中建立了四足机器人的仿真模型，应用四足机器人对角小跑步态的控制算法在Matlab/Simulink中建立机器人的控制模型，并进行了Matlab-Adams联合仿真。仿真结果与动力学分析结论相吻合，验证了四足机器人动力学简化模型的合理性和有效性。

摘要： This work presents a novel way of reducing the search space for the optimization of a dynamic system, in this case of the beam of a Curved Beam Hopper (CBH). Note that this method could also be applied to other similar systems. This reduced search space has the enormous advantage, that the computational burden decreases significantly, especially since the simulation of most dynamic systems takes long. After introducing the CBH, the model thereof is explained in detail. The robot is simulated using a SimMechanics simulation. This simulation discretizes the beam structure of the CBH into different pieces. Most importantly, it relies on an ap- proximation using the Euler Bernoulli beam theory tocalculate the discretization parameters of any given beam shape. The ground interaction model used is elastic with damping. Furthermore the technique to reduce the search space is explained, it uses basic properties from dynamics and geometry to significantly reduce the search space of the optimization. The idea is that rather than just changing one parameter at a time, multiple parameters of the systems are changed using a single iteration pa- rameter. Then, the performance of the presented technique is discussed and it is shown that even with very few optimization steps, good performance is achieved. Finally an alternative approximation of the beam structure is outlined in the ap- pendix; this method could be used to improve the approximation of the stiffness of the different discretization elements.

关键词： Da Vinci Whipple's surgery   Robotic surgery   Robotic Whipple's surgery  

摘要： Whipple is one of the most demanding and complex surgeries of the abdomen. It is the most commonly performed operation for pancreatic cancer, the fourth leading cause of cancer death in the United States. For patients with benign as well as malignant pancreatic tumors, it is believed that the robotic Whipple procedure will be a major improvement over the traditional procedure. The robotic surgery involves five small incisions (one to accommodate a miniature camera), rather than a large incision and separation, not cutting of muscles.

关键词： animation   collaborative learning   educational transition   learning by design   mathematics education   problem solving   robotics  

摘要： LOGO and turtle graphics were an influential movement in primary school mathematics education in the 1980s and 1990s. Since then, technology has moved forward, both in terms of its sophistication and pedagogical potential;and learner experiences, preferences and ways of thinking have changed dramatically. Based on the authors' previous work and a literature review, this article revisits the subject of enhancing mathematics education through educational robotics kits and virtual robotic animations by proposing their simultaneous deployment at the school-university transition. The rationale for such an application is argued and an evaluation framework for these technologies is proposed. Two educational robotic kits and a virtual environment supporting robotic animations are evaluated both in terms of their feasibility of deployment and their educational effectiveness. Finally, the evaluation of learning experiences when deploying the proposed pedagogical approach is discussed.

摘要： In this dissertation, we present four application-driven robotic manipulation tasks that are solved using a combination of feature-based, machine learning, dimension- ality reduction, and optimization techniques. First, we study a previously-published image processing algorithm whose goal is to learn how to classify which pixels in an image are considered good or bad grasping points. Exploiting the ideas behind dimensionality reduction in general and principal component analysis in particular, we formulate feature selection and search space reduction hypotheses that provide approaches to reduce the algorithm's computation time by up to 98% while retain- ing its classification accuracy. Second, we incorporate the image processing tech- nique into a new method that computes valid end-effector orientations for grasping tasks, the combination of which generates a unimanual rigid object grasp planner. Specifically, a fast and accurate three-layered hierarchical supervised machine learn- ing framework is developed, where the robot is kinesthetically taught a set of valid end-effector orientations by a human-in-the-loop. Third, we solve the challenge of bi- manual regrasping, where a pick-and-place operation requires an object transfer from one manipulator to another, by casting it as an optimization problem where the ob- jective is to minimize execution time. The optimization problem is supplemented by the image processing and unimanual grasping algorithm that jointly identify two good grasping points on the object and the proper orientations for each end-effector. Fourth, we target deformable objects by solving the problem of using cooperative manipulators to perform towel folding tasks. We solve this problem with a new learn- ing algorithm that combines both imitation and reinforcement learning in such a way that human demonstrations are used to reduce the search space of the reinforcement learning algorithm, resulting in quick convergence and fast learning capabilities

关键词： Anthropomorphic robots  

摘要： This paper proposes a general method for robots to learn motions and corresponding semantic knowledge simultaneously. A modified ISOMAP algorithm is used to convert the sampled 6D vectors of joint angles into 2D trajectories, and the required movements for writing numbers are learned from this modified ISOMAP-based model. Using this algorithm, the knowledge models are established. Learned motion and knowledge models are stored in a 2D latent space. Gaussian Process (GP) method is used to model and represent these models. Practical experiments are carried out on a humanoid robot, named ISAC, to learn the semantic representations of numbers and the movements of writing numbers through imitation and to verify the effectiveness of this framework. This framework is applied into training a humanoid robot, named ISAC. At the learning stage, ISAC not only learns the dynamics of the movement required to write the numbers, but also learns the semantic meaning of the numbers which are related to the writing movements from the same data set. Given speech commands, ISAC recognizes the words and generated corresponding motion trajectories to write the numbers. This imitation learning method is implemented on a cognitive architecture to provide robust cognitive information processing.

关键词： 灭火机器人   智能优化   搜索策略   传感器  

摘要： 随着科技的发展，机器人已开始进入人们的生活中，并发挥了重大作用，在不久的将来更多的机器人将会在人类生活中出现。为了推动机器人研究的发展，出现了各种机器人比赛，灭火机器人比赛就是其中之一。本文首先对机器人技术的研究背景和意义、研究现状和存在的挑战、存在的问题等进行全面的叙述。然后针对灭火机器人的硬件条件和灭火比赛的具体功能要求，进行了智能灭火机器人优化技术的研究和实现。 在硬件方面，对灭火机器人改进主要集中在声音传感器、避障传感器、红外传感器和灰度传感器等传感器上。针对这些传感器在灭火工作中的作用原理和实际使用环境中进行分析，从而做到对各传感器的精确控制。这方面工作主要是对这些传感器在实际环境中的参数测试和优化。 在软件设计方面的工作，主要是火源搜索策略和回家策略的设计及其程序实现。火源搜索策略用来保证灭火机器人以最快的速度寻找到火源，回家策略是在机器人完成灭火后，最快的速度返回出发点。程序实现主要包括启动模块、搜索火源模块、灭火模块、回家模块。 本文主要研究灭火机器人的智能优化技术，从提高机器人出发的灵敏性，搜索火源的快速性，火源定位的准确性，以及整机运行的稳定性入手，以全局优化的策略来对机器人火源搜索策略进行优化，改进了回家策略。最后，在实现了以上策略和对各类传感器参数的细致调整后，在标准比赛场地中进行机器人灭火实验，并和其他灭火策略进行对比，验证了本文设计的优化策略的实用性和参数调整的有效性。

关键词： Evolutionary robotics   diversity   multi-objective evolutionary algorithm   neural networks  

摘要： Evolutionary robotics (ER) aims at automatically designing robots or controllers of robots without having to describe their inner workings. To reach this goal, ER researchers primarily employ phenotypes that can lead to an infinite number of robot behaviors and fitness functions that only reward the achievement of the task and not how to achieve it. These choices make ER particularly prone to premature convergence. To tackle this problem, several papers recently proposed to explicitly encourage the diversity of the robot behaviors, rather than the diversity of the genotypes as in classic evolutionary optimization. Such an approach avoids the need to compute distances between structures and the pitfalls of the noninjectivity of the phenotype/behavior relation;however, it also introduces new questions: how to compare behavior? should this comparison be task specific? and what is the best way to encourage diversity in this context? In this paper, we review the main published approaches to behavioral diversity and benchmark them in a common framework. We compare each approach on three different tasks and two different genotypes. The results show that fostering behavioral diversity substantially improves the evolutionary process in the investigated experiments, regardless of genotype or task. Among the benchmarked approaches, multi-objective methods were the most efficient and the generic, Hamming-based, behavioral distance was at least as efficient as task specific behavioral metrics.

摘要： 青少年机器人爱好者的活动青少年机器人科普活动是在电子、信息技术不断普及应用的基础上,逐步兴起的一项青少年科技教育活动.它为21世纪的青少年开创了科学上的自我学习模式,也帮助他们建立了探究科技能力的知识架构.在设计过程中,除了激发学生的学习动机、探究精神,掌握和应用电子信息技术的技巧外,还给孩子们提供更多的协作机会.这不但可以开阔他们运用科技知识的视野,也互相提高了彼此的学习效能,并共同展示着他们在科技方面的杰出才华与丰富的想象力及创造力,从而激发他们对科学的兴趣.