飞机蒙皮检测机器人的控制和感知总体结构(英文)

介绍了一种基于DSP F2812的飞机蒙皮检测机器人的控制和感知总体结构。机器人控制系统包括移动模块:吸附模块、感知系统和无线通讯模块4个模块。从控制系统的软件和硬件对各个模块进行设计,并根据各个模块之间的联系,建立机器人的控制系统,机器人的感知系统监控机器人的运行,并通过地面主控制机远程控制机器人。通过模块间的实验和蒙皮图像检测实验验证了检测机器人设计方案的可行性和有效性。     

基于传感器信息融合的移动机器人自主爬楼梯技术研究

机器人自主爬楼梯是移动机器人完成危险环境探查、侦察、救灾等任务需要具备的基本智能行为之一.分析了楼梯的多样性和履带式机器人爬楼梯固有的不稳定性导致机器人爬楼梯工作的复杂性,描述了带前导手臂的履带式移动机器人爬楼梯的步骤,简要介绍了利用超声波、视频摄像头和激光扫描测距仪信息来感知楼梯和判断机器人与楼梯相对位置的算法,最后提出了一个基于传感器测量值可信度的信息融合方法进行楼梯参数感知和行驶方向计算的机器人自主爬楼梯的控制系统结构.     

飞机表面检查机器人系统研究

研究了飞机表面检查机器人系统结构及相关关键技术，提出了针对非对称异型飞机表面爬行机器人的总体结构和关键部件，对机器人作业过程中的吸附、运动及检测功能进行了分析，并给出了解决方案。研究了基于轨迹线性化理论的机器人路径跟踪控制算法，并对基于视觉的飞机铆钉裂纹缺陷检测与识别方法。试验结果验证了算法的有效性，为飞机蒙皮铆钉缺陷的自动在线检测提供了可行的途径。     

移动机器人环境视觉小波稀疏压缩传感和识别

视觉环境认知,尤其处理实时性,是自主移动机器人研究的重要内容之一,而减少视觉信息数据处理量是提高视觉感知处理性能的重要举措之一。基于小波稀疏和灰度共生矩阵,对两种典型环境图像的压缩传感信息进行了纹理特征提取与直接识别研究。结果表明:进行小波稀疏的两种压缩传感环境图像信息保留了原始图像特征,能够直接进行环境特征认知,在一定程度上说明了压缩传感信息直接特征识别的可行性。     

基于Netvlad神经网络的变磁力吸附爬壁机器人控制系统设计

变磁力吸附爬壁机器人是一种具有快速、灵活移动方式的爬行机器人,但其吸附力难以控制,越障稳定性较差,难以保证机器人的平稳爬行;为实现爬壁机器人在大型建筑结构外表面的自主避障,提升机器人与运动平面之间的吸附紧密性,设计基于Netvlad神经网络的变磁力吸附爬壁机器人控制系统;按照PCB控制要求,连接外置SRAM设备与传感器模块,借助驱动I/O口电路提供的电力驱动作用,控制气动阀门的闭合情况,完成变磁力吸附爬壁机器人控制系统硬件结构设计;建立Netvlad神经网络体系,通过划分控制指令程序任务的方式,确定移植参数取值范围,实现对控制协议的移植处理,联合相关硬件应用结构,完成基于Netvlad神经网络的变磁力吸附爬壁机器人控制系统设计;实验结果表明,在所设计系统作用下,障碍物所在位置与爬壁机器人所在位置之间的实测距离未大于30cm,能够有效实现自主避障,保证机器人与运动平面之间的紧密吸附。     

Development of Wall Climbing Robot System by Using Impeller Type Adhesion Mechanism

In this paper, we present a wall climbing robot system, called “LARVA”, developed for visual inspection of structures with flat surfaces. The robot has two differential driving wheels with a suspension and an adhesion mechanism. The adhesion mechanism is composed of an impeller and two–layered suction seals. It is designed to provide sufficient adhesion force and be controlled so that the robot can move freely on various wall surfaces. The static and aerodynamic modeling of the adhesion mechanism is given and the analysis of the adhesion mechanism, air leakage, and inner flow are carried out to be useful for the design as well as the control. Finally, the performances of the robot are experimentally verified on several kinds of walls and its feasibility is validated.     

Development of universal vacuum gripper for wall-climbing robot

Task performed at a height, such as wall inspections are one of the dangerous tasks for humans. Thus, robotic technology for safety inspection is required. This research focuses on developing robots to climb vertical walls with flat and uneven surfaces, e.g. concrete, tile and riveted structure. To have wall-climbing capability, climbing robots use vacuum pads, claws, magnets, intermolecular force, and adhesive. However, each of these approaches has disadvantages. To achieve wall climbing on an uneven surface without scratching and staining, we have developed a novel vacuum pad named the Universal Vacuum Gripper (UVG), which is based on the Universal Gripper (UG). The UG is a robot hand using jamming transition of coffee powder inside a balloon to grip uneven material. The UVG is a vacuum pad with a deformable skirt based on the UG. If the skirt shape is deformed in accordance with the contact surface, air leaks can be avoided. Moreover, the deformed skirt can be stiffened, thereby working as a gripper. Here, we evaluate the proposed gripper, having both grasping and adhesion force. We also develop a wall-climbing robot with UVGs, and evaluate its performance on uneven surfaces under real-world conditions.     

一种用于反恐侦察的爬壁机器人系统

介绍了一种用于反恐侦察的爬壁机器人系统．该系统包括3个部分：真空吸附式两足爬壁机器人、便携式遥控器和无线视频传输模块．其中,基于DSP技术开发的控制器可以使机器人在光滑的壁面上灵活地爬行、转向和跨越．文中提出了一种实现机器人在两个不同倾斜壁面之间跨越的控制算法．实验表明,该机器人体积小、噪声低、隐蔽性好;视频模块图像清晰、传输稳定;整个系统可以满足执行反恐侦察任务的基本要求．     

Design and motion planning of an autonomous climbing robot with claws

This paper presents the design of a novel robot capable of climbing on vertical and rough surfaces, such as stucco walls. Termed CLIBO (claw inspired robot), the robot can remain in position for a long period of time. Such a capability offers important civilian and military advantages such as surveillance, observation, search and rescue and even for entertainment and games. The robot’s kinematics and motion, is a combination between mimicking a technique commonly used in rock climbing using four limbs to climb and a method used by cats to climb on trees with their claws. It uses four legs, each with four-degrees-of-freedom (4-DOF) and specially designed claws attached to each leg that enable it to maneuver itself up the wall and to move in any direction. At the tip of each leg is a gripping device made of 12 fishing hooks and aligned in such a way that each hook can move independently on the wall’s surface. This design has the advantage of not requiring a tail-like structure that would press against the surface to balance its weight. A locomotion algorithm was developed to provide the robot with an autonomous capability for climbing along the pre-designed route. The algorithm takes into account the kinematics of the robot and the contact forces applied on the foot pads. In addition, the design provides the robot with the ability to review its gripping strength in order to achieve and maintain a high degree of reliability in its attachment to the wall. An experimental robot was built to validate the model and its motion algorithm. Experiments demonstrate the high reliability of the special gripping device and the efficiency of the motion planning algorithm.     

Study on mobile mechanism of a climbing robot for stair cleaning: a translational locomotion mechanism and turning motion

In human living environments, it is often the case that the cleaning area is three-dimensional space such as a high-rise building. An autonomous cleaning robot is proposed so as to move on all floors including stairs in a building. When a robot cleans in three-dimensional space, it needs to turn for direction in addition to climb down stairs. The proposed robot selects movement using legs or wheels depending on stairs or flat surfaces. In this paper, a mobile mechanism and a control method are described for translational locomotion. The translational mechanism is based on using two-wheel-drive type omni-directional mobile mechanism. To recognize a stair using the position-sensitive detector, the robot shifts from translational locomotion to climbing down motion or edge-following motion. It is shown that the proposed robot turns to face a stair with the accuracy of 5°.     

Acquisition of Weld Seam Dimensional Position Information for Arc Welding Robot Based on Vision Computing

Recognition and identification of weld environment and seam dimensional position by computer vision is a key technology for developing advanced autonomous welding robot. Aiming at requirements for recognition of weld seam image characteristics, this paper first presents an improved algorithm of subpixel edge detection based on Zernike moments. Comparing with the Ghosal’s original algorithm, the improved algorithm deals with mask effect and first derivative model on edge gradient direction so that it has the strong robust to noise, self-thinning ability and higher locating precision. An algorithm based on ZMs to extract line is also proposed, the comparative results with SHT and RHT show the method has the highest calculation speed and accuracy. The stereovision technology is developed to identify dimensional position of weld seam by computing dimensional coordinates of the weld seam. According to characteristics of weld seam, view field scope model and stereovision model based on baseline are studied and a stereo matching method is presented. In order to evaluate the algorithms and models presented in this paper, a welding robot systems with single camera fixed on the weld torch end-effector has been established for the robot to identify the dimensional position of typical weld seam by one-item and two-position method. The experiment results on S-shape and saddle-shape weld seams show that the vision computing method developed in this paper can be used for acquiring weld seam dimensional position information in welding robot system. Thus the welding path is mapped before the welding operation is executed.     

油罐检测爬壁机器人结构与控制系统设计

研究智能爬壁机器人检测技术及其系统 ,提出了机器人在大型垂直罐壁移动作业的路径规划方法 ,并分析了抗倾覆机构的作用 ,设计给出了机器人总体结构和控制系统 .系统以磁吸附爬壁机器人为运动载体 ,采用非接触式无损检测技术 ,并配备多种传感器 ,具备较高的智能化水平 .现场实验表明 ,该系统自动化程度高、运动稳定可靠、定位精度高 ,大幅度提高了检测效率 .     

Sensing and gait planning of quadruped walking and climbing robot for traversing in complex environment

In this paper, a general study on improving adaptability of quadruped walking and climbing robot in complex environment is presented. First, a sensing system composed of range and gyroscope sensors in a novel arrangement is developed. By combining the sensing signals and the internal state of the robot, the surface geometry of the environment is sufficiently reconstructed in real-time. Secondly, a planning algorithm for the robot to overcome the reconstructed environment is conducted. Based on the reshaped surface, the planning algorithm not only provides the exact body trajectory and foot positions but also the adaptability of the robot in a specific environment. A method to improve the adaptability of the walking and climbing robot is also introduced. Thanks to the adherent ability of the robot, the center of gravity of the robot is allowed to move outside the support polygon to increase the reach-ability of the next swing leg. Finally, the effectiveness of the proposed approach is verified by the performances of the experiments in complex environments using a quadruped walking and climbing robot named MRWALLSPECT IV.     

Weaver: Hexapod robot for autonomous navigation on unstructured terrain

Legged robots are an efficient alternative for navigation in challenging terrain. In this paper we describe Weaver, a six‐legged robot that is designed to perform autonomous navigation in unstructured terrain. It uses stereo vision and proprioceptive sensing based terrain perception for adaptive control while using visual‐inertial odometry for autonomous waypoint‐based navigation. Terrain perception generates a minimal representation of the traversed environment in terms of roughness and step height. This reduces the complexity of the terrain model significantly, enabling the robot to feed back information about the environment into its controller. Furthermore, we combine exteroceptive and proprioceptive sensing to enhance the terrain perception capabilities, especially in situations in which the stereo camera is not able to generate an accurate representation of the environment. The adaptation approach described also exploits the unique properties of legged robots by adapting the virtual stiffness, stride frequency, and stride height. Weaver's unique leg design with five joints per leg improves locomotion on high gradient slopes, and this novel configuration is further analyzed. Using these approaches, we present an experimental evaluation of this fully self‐contained hexapod performing autonomous navigation on a multiterrain testbed and in outdoor terrain.     

Design and control of a climbing robot based on hot melt adhesion

Robust climbing in unstructured environments has been one of the long-standing challenges in robotics research. Among others, the control of large adhesion forces is still an important problem that significantly restricts the locomotion performance of climbing robots. The main contribution of this paper is to propose a novel approach to autonomous robot climbing which makes use of hot melt adhesion (HMA). The HMA material is known as an economical solution to achieve large adhesion forces, which can be varied by controlling the material temperature. For locomotion on both inclined and vertical walls, this paper investigates the basic characteristics of HMA material, and proposes a design and control of a climbing robot that uses the HMA material for attaching and detaching its body to the environment. The robot is equipped with servomotors and thermal control units to actively vary the temperature of the material, and the coordination of these components enables the robot to walk against the gravitational forces even with a relatively large body weight. A real-world platform is used to demonstrate locomotion on a vertical wall, and the experimental result shows the feasibility and overall performances of this approach.     

基于模糊神经网络的六足机器人自主导航闭环控制系统设计

针对未知环境中六足机器人的自主导航问题,设计了一种基于模糊神经网络的自主导航闭环控制算法,并依据该算法设计了六足机器人的导航控制系统．算法融合了模糊控制的逻辑推理能力与神经网络的学习训练能力,并引入闭环控制方法对算法进行优化．所设计的控制系统由信息输入、模糊神经网络、指令执行以及信息反馈4个模块组成．环境及位置信息的感知由GPS（全球定位系统）传感器、电子罗盘传感器和超声波传感器共同完成．采用C语言重建模糊神经网络控制算法,并应用于该系统．通过仿真实验,从理论上论证了基于模糊神经网络的闭环控制算法性能优于开环控制算法,闭环控制算法能够减小六足机器人在遇到障碍物时所绕行的距离,行进速度提高了6.14%,行进时间缩短了8.74%．在此基础上,开展了实物试验．试验结果表明,该控制系统能够实现六足机器人自主导航避障控制功能,相对于开环控制系统,能有效地缩短行进路径,行进速度提高了5.66%,行进时间缩短了7.25%,验证了闭环控制系统的可行性和实用性．     

Climbing robots for maintenance and inspections of vertical structures—A survey of design aspects and technologies

The maintenance and inspection of large vertical structures with autonomous systems is still an unsolved problem. A large number of different robots exist which are able to navigate on buildings, ship hulls or other human-made structures. But, most of these systems are limited to special situations or applications. This paper deals with different locomotion and adhesion methods for climbing robots and presents characteristics, challenges and applications for these systems. Based on a given set of requirements these principles are examined and in terms of a comprehensive state-of-the-art more than hundred climbing robots are presented. Finally, this schematics is applied to design aspects of a wall-climbing robot which should be able to inspect large concrete buildings.     

一种输电线路巡检机器人控制系统的设计与实现

介绍了一种超高压输电线路巡检机器人控制系统的设计与实现方法．根据巡检作业任务的要求，采用遥控与局部自主相结合的控制模式实现巡检机器人沿线行走及跨越障碍．设计了巡检机器人有限状态机模型，实现了机器人遥控与局部自主控制的有机结合．采用基于激光传感器定位的方法实现了巡检机器人的自主越障控制．实验结果表明，该机器人可实现沿线行走及自主跨越障碍，从而验证了控制系统设计的有效性与合理性．     

海底管道检测机器人自主缺陷定位的模糊控制研究

针对全自主管道检测机器人对超长距离海底油气管道内壁缺陷的精确定位的难题,提出了一种基于缺陷信息定位的模糊控制方法,并将其应用在海底管道检测机器人的智能控制器中．仿真实验和模拟样机的现场测试证明,该智能控制器定位准确,工作稳定可靠,满足了检测机器人在超长距离海底油气管道内全自主精确定位的技术要求．     

Modeling curiosity in a mobile robot for long-term autonomous exploration and monitoring

This paper presents a novel approach to modeling curiosity in a mobile robot, which is useful for monitoring and adaptive data collection tasks, especially in the context of long term autonomous missions where pre-programmed missions are likely to have limited utility. We use a realtime topic modeling technique to build a semantic perception model of the environment, using which, we plan a path through the locations in the world with high semantic information content. The life-long learning behavior of the proposed perception model makes it suitable for long-term exploration missions. We validate the approach using simulated exploration experiments using aerial and underwater data, and demonstrate an implementation on the Aqua underwater robot in a variety of scenarios. We find that the proposed exploration paths that are biased towards locations with high topic perplexity, produce better terrain models with high discriminative power. Moreover, we show that the proposed algorithm implemented on Aqua robot is able to do tasks such as coral reef inspection, diver following, and sea floor exploration, without any prior training or preparation.