关键词： 参赛选手   惠水县   商务学院   贵州省   竞赛   青少年   教练员   开幕  

摘要： 2021年4月10日,2021年贵州省青少年机器人竞赛在惠水县贵州财经大学商务学院开幕。本次竞赛由贵州省科协、贵州省教育厅、贵州省文明办、贵州团贵州省委、贵州省妇联、贵州省科技厅共同主办,竞赛为期两天,将有来自全省9个市(州)的270支队伍1100多名参赛选手和教练员比赛。

关键词： 机器人学  

ISBN: (纸本)9787302565086

摘要： 本书介绍了机器人学的基本原理及其应用。共分为7章：第1章介绍机器人学的发展历史及相关基础知识；第2章介绍机器人的基本结构及驱动方式；第3章介绍机器人的运动学与动力学；第4章介绍机器人传感器与视觉系统；第5章阐述机器人规划与人机交互；第6章介绍机器人典型控制算法的基础知识及典型应用。第7章介绍智能机器人的典型应用案例。

关键词： 码垛机器人   D-H参数法   运动学   动力学仿真  

摘要： 目的为了实现大重量、大体积木门等板状物品的快速搬运与包装作业,设计一种适用于高速重载条件下的六自由度码垛机器人。方法针对该机器人,对其关键部件进行选型,通过D-H参数法建立机器人的运动学模型,并进行正逆运动学分析;求取机器人的工作空间,得到机器人所能达到的极限位置,为机器人的布置方案提供参考;运用ADAMS软件建立码垛机器人的动力学模型,对其在将木门进行搬运包装时危险工况进行仿真分析。结果得到码垛机器人处于最大臂展的危险工况下,大臂所受力及力矩最大,所受力及力矩最大值分别为12.9 kN,13.5 MN/mm。为进一步探究大臂的力学特性将大臂进行柔性化处理,得到大臂的力和力矩波动变化,以及最大动应力点,大臂所受力和力矩波动的最大值分别在z方向及y方向处,最大值分别为621 N,895 kN/mm。结论机器人各关节所受力和力矩变化无明显突变,所受刚性冲击较少;证明了在高速重载条件下大臂设计的合理性,为机器人结构的进一步优化及轻量化设计奠定了重要基础。

关键词： Simulation   Modeling   Analytical models  

摘要： To Perform reliably and consistently over sustained periods of time, large-scale automation critically relies on computer simulation. Simulation allows us and supervisory AI to effectively design, validate, and continuously improve complex processes, and helps practitioners to gain insight into the operation and justify future investments. While numerous successful applications of simulation in industry exist, such as circuit simulation, finite element methods, and computeraided design (CAD), state-of-the-art simulators fall short of accurately modeling physical phenomena, such as friction, impact, and deformation.

关键词： Contact force estimation   Floating-base space robots   Nonlinear observer   Collision detection and isolation  

摘要： In this paper, the contact force detection, isolation and estimation problems for an orbital robot are addressed. First, an observer based on a base-joints dynamics is reviewed and analyzed considering the issues related to a space robotic application. Then, a new scheme is derived reformulating the dynamics in terms of the motion of the centroid of the whole robot and the joints. In both observers, three momentum-based residuals are defined, which can be used to reconstruct the external contact wrench. The reconstruction through the proposed observer turns out to provide superior performance with respect to the base-joints one thanks to the decoupling of the angular and joint momentum residuals from the spacecraft linear velocity, which is an inaccurate measurement in real space scenarios. The advantages of the proposed observer are shown through both simulative and hardware validation featuring a 7 degrees of freedom (DoF) arm mounted on a 6 DoF moving base.

关键词： Nanotechnology   Artificial intelligence   Automation   Personalised medicine   Microfluidics  

摘要： The field of nanotechnology and personalised medicine is undergoing drastic changes in the approach and efficiency of experimentation. The COVID-19 pandemic has spiralled into mass stagnation of major laboratories around the globe and led to increased investment into remote systems for nanoparticle experiments. A significant number of laboratories now operate using automated systems;however, the extension to nanoparticle preparation and artificial intelligence-dependent databases holds great translational promise. The strive to combine automation with artificial intelligence (AI) grants the ability to optimise targeted therapeutic nanoparticles for unique cell types and patients. In this perspective, the current and future trends of automated approaches to nanomedicine synthesis are discussed and compared with traditional methods.

关键词： 3D printing   Sensor   Robotics   Piezoelectric  

摘要： Three-dimensional (3D) printing has become an important fabrication method for soft robotics, due to its ability to make complex 3D structures from computer designs in simple steps and multimaterial co-deposition ability. In this article, the application of 3D printing techniques in the fabrication of four types of tactile sensors commonly used in soft robotics, including the piezoresistive tactile sensor, capacitive tactile sensor, piezoelectric tactile sensor, and triboelectric tactile sensor, will be discussed. The 3D printing mechanism, material, and structure for each type of sensor will be introduced, and the perspectives on the future potential of 3D printable tactile sensors will be discussed.

关键词： Robotics   Biomimetic   Metamaterial   Synthetic biology  

摘要： How far are we from artificial life? How far are we pushing biomimetics? In robotics, bioinspiration brings scenarios where soft robots acquire life-like abilities and better integrate themselves into our natural world. How do we make robots grow and learn, self-heal and self-feed, adapt and biodegrade? Interesting achievements in soft robotics show mechanisms for growing, by adding material to the robot body, materials for robot self-repair, and systems for energy production in the robot itself. Enormous challenges are still open though. They inspire investigations across disciplines and offer opportunities for research and for scientific and technological progress. Advances in materials, smart materials, and fabrication techniques play a key role in this direction.

关键词： autonomous navigation   collision-free   deep reinforcement learning   unmanned aerial vehicle  

摘要： Autonomous navigation and collision avoidance missions represent a significant challenge for robotics systems as they generally operate in dynamic environments that require a high level of autonomy and flexible decision-making capabilities. This challenge becomes more applicable in micro aerial vehicles (MAVs) due to their limited size and computational power. This paper presents a novel approach for enabling a micro aerial vehicle system equipped with a laser range finder to autonomously navigate among obstacles and achieve a user-specified goal location in a GPS-denied environment, without the need for mapping or path planning. The proposed system uses an actor-critic-based reinforcement learning technique to train the aerial robot in a Gazebo simulator to perform a point-goal navigation task by directly mapping the noisy MAV's state and laser scan measurements to continuous motion control. The obtained policy can perform collision-free flight in the real world while being trained entirely on a 3D simulator. Intensive simulations and real-time experiments were conducted and compared with a nonlinear model predictive control technique to show the generalization capabilities to new unseen environments, and robustness against localization noise. The obtained results demonstrate our system's effectiveness in flying safely and reaching the desired points by planning smooth forward linear velocity and heading rates.

关键词： Robotics   reinforcement learning   deep learning  

摘要： Deep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex behaviors from low-level sensor observations. Although a large portion of deep RL research has focused on applications in video games and simulated control, which does not connect with the constraints of learning in real environments, deep RL has also demonstrated promise in enabling physical robots to learn complex skills in the real world. At the same time, real-world robotics provides an appealing domain for evaluating such algorithms, as it connects directly to how humans learn: as an embodied agent in the real world. Learning to perceive and move in the real world presents numerous challenges, some of which are easier to address than others, and some of which are often not considered in RL research that focuses only on simulated domains. In this review article, we present a number of case studies involving robotic deep RL. Building off of these case studies, we discuss commonly perceived challenges in deep RL and how they have been addressed in these works. We also provide an overview of other outstanding challenges, many of which are unique to the real-world robotics setting and are not often the focus of mainstream RL research. Our goal is to provide a resource both for roboticists and machine learning researchers who are interested in furthering the progress of deep RL in the real world.