关键词： 连续体机器人   动力学模型   传动摩擦   凯恩方法  

摘要： 针对连续体机器人因自身的柔顺性及自由度的冗余性所带来的运动建模复杂和控制精度较低等问题,提出了一种考虑非线性摩擦的绳驱动连续体机器人动力学建模方法。基于凯恩方法构建了包含惯性力、弹性力、重力及驱动力作用的动力学模型,并根据改进的Capstan方程对绳-轮传动系统的力传递特性进行分析,提出了一种基于非线性摩擦补偿的前馈控制策略,最后通过对单节连续体机器人进行仿真和运动实验来验证所建模型的正确性。结果表明,所建立的运动模型和控制策略能够较为准确地描述连续体机器人的变形特性。

关键词： 球形机器人   动力学模型   拉格朗日方程   仿真  

摘要： 针对用于电缆通道环境监测的双轮驱动球形机器人,基于拉格朗日方程实现其动力学建模。通过驱动车轮的转角、球形机器人两自由度位置坐标、驱动车体的姿态角直观的计算出系统的动能和势能;考虑球形机器人位置坐标、驱动车体姿态角、球形机器人运动方向角和左右驱动车轮的转角的约束关系,选择左右驱动车轮的转角、球形机器人的移动距离、驱动车体的姿态角作为广义坐标;考虑驱动车轮和球壳、球壳与地面的摩擦得到系统的广义力和耗散能量,建立了能够描述双轮驱动球形机器人在二维水平面运动、驱动车体姿态的多体动力学。最后,在Matlab/Simulink环境中开展了模型的仿真分析,仿真结果表明所建模型的有效性。

关键词： Mobile robotics   Visual information   Artificial intelligence   Mapping   Localization   Navigation   SLAM   Exploration  

摘要： Nowadays, the field of mobile robotics has experienced an important evolution and these robots are more commonly proposed to solve different tasks autonomously. The use of visual sensors has played an important role in mobile robotics tasks during the past few years due to the advances in computer vision hardware and algorithms. It is worth remarking the use of AI tools to solve a variety of problems in mobile robotics based on the use of images either as the only source of information or combining them with other sensors such as laser or GPS. The improvement of the autonomy of mobile robots has attracted the attention of the scientific community. A considerable amount of works have been proposed over the past few years, leading to an extensive variety of approaches. Building a robust model of the environment (mapping), estimating the position within the model (localization) and controlling the movement of the robot from one place to another (navigation) are important abilities that any mobile robot must have. Considering this, this review focuses on analyzing these problems;how researchers have addressed them by means of AI tools and visual information;and how these approaches have evolved in recent years. This topic is currently open and a large number of works can be found in the related literature. Therefore, it can be of interest making an analysis of the current state of the topic. From this review, we can conclude that AI has provided robust solutions to some specific tasks in mobile robotics, such as information retrieval from scenes, mapping, localization and exploration. However, it is worth continuing to develop this line of research to find more integral solutions to the navigation problem so that mobile robots can increase their autonomy in large, complex and heterogeneous environments.

关键词： 双分裂   四轮机器人   分层递推   动力学建模   模型仿真  

摘要： 电力机器人服役于超高压强电磁特种作业环境下,机器人动力学模型是机器人机械结构设计、电气系统设计及控制器设计的关键,且动力学模型的精确度和复杂度直接影响机器人控制性能和机器人的运行速度。针对高压双分裂输电线路四轮移动带电作业机器人常规动力学建模方法计算量大、复杂、不利于机器人机构和控制器设计的问题,提出了一种基于分层递推结构的机器人动力学建模方法。通过分解作业过程中机械臂的运动,利用拉格朗日动力学建模方法对机器人的机械臂基本动作进行了动力学建模,得到了机械臂基本动作的统一动力学模型,通过递推得出了四轮移动机器人的完整动力学模型,最后,通过机器人作业现场获取的关节电机实时数据,在MATLAB软件中进行仿真实验,结果验证了本文机器人动力学模型的正确性,为机器人物理样机开发、控制器和关节驱动机构的设计与选型提供了理论依据。

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： Walking rehabilitation   Inverse dynamics   Power assist  

摘要： This paper describes a gait training algorithm based on the inverse dynamics of DDgo Pro, an end-effector-type walking rehabilitation robot. As a major feature, a gait training algorithm with high responsiveness and flexibility was achieved by analyzing the inverse dynamics of the five-link mechanism with a foothold attached to the end of the mechanism and compensating for the required torque for the link motion and the patient's weight during gait training. To this end, inverse dynamics was solved using Newton Euler's method, and three training modes were proposed. First, passive mode training performs three tasks: motion compensation of the five-link mechanism for the given constant walking speed through the inverse dynamics, patient's weight compensation, and tracking of the gait trajectory of the foothold using PD control to practice the normal walking pattern and restore muscle strength for standing. Second, Active-assisted mode training was implemented for the patient to receive muscle assistance from an electric motor in proportion to the patient's walking intention in addition to the reduced compensations of the motion of the five-link mechanism and patient's weight. Third, active mode training was implemented so that the patient can perform gait training while feeling their own weight with the time-varying motion compensation of the five-link mechanism. Finally, the performance of each gait training mode was experimentally verified, and the muscle activity was measured using EMG sensors to compare the muscle assistance performances of the training modes. Furthermore, the correction of the gait motion of the affected leg according to the motor assistance in active-assisted mode was also verified.

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.

关键词： image-based sensor systems   object detection systems   safety of autonomous agriculture machines   test stand   humanoid test target  

摘要： As an essential part for the development of autonomous agricultural robotics, the functional safety of autonomous agricultural machines is largely based on the functionality and robustness of non-contact sensor systems for human protection. This article presents a new step in the development of autonomous agricultural machine with a concept and the realization of a novel test method using a dynamic test stand on an agricultural farm in outdoor areas. With this test method, commercially available sensor systems are tested in a long-term test around the clock for 365 days a year and 24 h a day on a dynamic test stand in continuous outdoor use. A test over a longer period of time is needed to test as much as possible all occurring environmental conditions. This test is determined by the naturally occurring environmental conditions. This fact corresponds to the reality of unpredictable/determinable environmental conditions in the field and makes the test method and test stand so unique. The focus of the developed test methods is on creating own real environment detection areas (REDAs) for each sensor system, which can be used to compare and evaluate the autonomous human detection of the sensor systems for the functional safety of autonomous agricultural robots with a humanoid test target. Sensor manufacturers from industry and the automotive sector provide their sensor systems to have their sensors tested in cooperation with the TuV.

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.