Underwater confined space mapping by resource‐constrained autonomous vehicle

For marine industrial inspection, archaeology, and geological formation study, the ability to map unknown underwater enclosed and confined spaces is desirable and well suited for robotic vehicles. To date, there are few solutions thoroughly tested in the field designed to perform this specific task, none of which operate autonomously. With a small, low‐cost biomimetic platform known as the U‐CAT, we developed a mapping‐mission software architecture in which the vehicle executes three key sensor‐based reactive stages: entering, exploring, and exiting. Encapsulated in the exploring stage are several state‐defined navigation strategies, called patterns, which were designed and initially tested in simulation. The results of simulation work informed the selection of two patterns that were executed in field trials at a submerged building in Rummu Quarry Lake, Estonia, as part of several full mapping missions. Over the course of these trials, the vehicle was capable of observing the majority (78–97%) of 49.9 explorable square meters within 7 minutes. Based on these results, we demonstrate the capability of a low‐cost and resource‐constrained vehicle to perform confined space mapping under sensor uncertainty. Further, the observations made by the vehicle are shown to be suitable for a target site reconstruction and analysis in postprocessing, which is the intended outcome of this type of mission in practical applications.     

Multisensor online 3D view planning for autonomous underwater exploration

This study presents a novel octree‐based three‐dimensional (3D) exploration and coverage method for autonomous underwater vehicles (AUVs). Robotic exploration can be defined as the task of obtaining a full map of an unknown environment with a robotic system, achieving full coverage of the area of interest with data from a particular sensor or set of sensors. While most robotic exploration algorithms consider only occupancy data, typically acquired by a range sensor, our approach also takes into account optical coverage, so the environment is discovered with occupancy and optical data of all discovered surfaces in a single exploration mission. In the context of underwater robotics, this capability is of particular interest, since it allows one to obtain better data while reducing operational costs and time. This study expands our previous study in 3D underwater exploration, which was demonstrated through simulation, presenting improvements in the view planning (VP) algorithm and field validation. Our proposal combines VP with frontier‐based (FB) methods, and remains light on computations even for 3D environments thanks to the use of the octree data structure. Finally, this study also presents extensive field evaluation and validation using the Girona 500 AUV. In this regard, the algorithm has been tested in different scenarios, such as a harbor structure, a breakwater structure, and an underwater boulder.     

Design and testing of the JPL‐Nautilus Gripper for deep‐ocean geological sampling

We present the design and experimental results for the JPL‐Nautilus Gripper, a 16‐finger highly underactuated microspine gripper for use in the deep ocean. The gripper can grasp objects from 10 to 30 cm in size and anchor to flat and curved rocky surfaces (i.e., cliff faces and seamounts). Laboratory results demonstrated an anchoring capability of greater than 450 N on rough rocks in both shear and normal loading directions. Deployment on the Hercules ROV (remotely operated vehicle) aboard the E/V Nautilus on three deep‐ocean dives verified performance at depths up to to 2,100 m with approximately 100 N loads applied through the ROV's thrusters, including moment loads. The gripper also serves as a development unit for future robotic tools that will include a coring drill in the center of the gripper, as previously demonstrated in non‐ocean environments with microspine grippers. Such a tool will facilitate the collection of geologic samples from the deep ocean using more agile and cost‐effective systems.     

Mapping the underside of an iceberg with a modified underwater glider

Icebergs pose many challenges to offshore operations in the Arctic Ocean and sub‐arctic regions. They could damage underwater infrastructure such as pipelines, and disrupt marine transportation. The below‐water shape of an iceberg is a key factor for iceberg management in the North Atlantic Ocean because it affects the iceberg towing plans and iceberg drift patterns. In recent years, unmanned platforms have been proposed as potential candidates for underwater iceberg mapping. Compared to a conventional ship‐based iceberg survey, using unmanned platforms is more efficient and safer. In this paper, we present research using a hybrid underwater glider to measure the underwater shape of an iceberg. The vehicle is equipped with a mechanical scanning sonar for range sensing and iceberg mapping, and a guidance system is designed to use the sonar measurements for guiding the vehicle to circumnavigate an iceberg at the desired standoff distance. Several field experiments have been conducted on an iceberg to evaluate the system performance. With repeated observations, the underside of the target iceberg was successfully reconstructed, and iceberg shape comparisons are presented.     

Nonlinear model predictive control for hydrobatics: Experiments with an underactuated AUV

Hydrobatic autonomous underwater vehicles (AUVs) can be efficient in range and speed, as well as agile in maneuvering. They can be beneficial in scenarios such as obstacle avoidance, inspections, docking, and under-ice operations. However, such AUVs are underactuated systems—this means exploiting the system dynamics is key to achieving elegant hydrobatic maneuvers with minimum controls. This paper explores the use of model predictive control (MPC) techniques to control underactuated AUVs in hydrobatic maneuvers and presents new simulation and experimental results with the small and hydrobatic SAM AUV. Simulations are performed using nonlinear model predictive control (NMPC) on the full AUV system to provide optimal control policies for several hydrobatic maneuvers in Matlab/Simulink. For implementation on AUV hardware in robot operating system, a linear time varying MPC (LTV-MPC) is derived from the nonlinear model to enable real-time control. In simulations, NMPC and LTV-MPC shows promising results to offer much more efficient control strategies than what can be obtained with PID and linear quadratic regulator based controllers in terms of rise-time, overshoot, steady-state error, and robustness. The LTV-MPC shows satisfactory real-time performance in experimental validation. The paper further also demonstrates experimentally that LTV-MPC can be run real-time on the AUV in performing hydrobatic maneouvers.     

Design,implementation and validation of a stability model for articulated autonomous robotic systems

The use of robots in agriculture and forestry is rapidly growing thanks to the progress in sensors, controllers and mechatronics devices. Especially in hilly and mountainous terrains, the development of (semi-)autonomous systems that could travel safely on uneven terrain and perform many operations is an open field of investigation. One of the most promising mobile robot architectures is the articulated 4-wheeled system that shows an optimal steering capacity, and the possibility to adapt to uneven terrains thanks to a central passive degree of freedom. In this paper, the kinematic and (quasi-)static model for evaluating the phase I instability presented in Baker and Guzzomi(2013) has been firstly extended to allow to threat a generic articulated robotic system and to forecast the instability conditions. Then, the model and the stability conditions have been implemented in a Matlab™ simulator and validated by means of an experimental emulator. Finally, a first prototype for a mechatronic anti-overturning device is discussed.     

一种基于情感智能的机器人自主趋光行为研究

针对移动机器人的自主趋光行为问题,提出了一种基于情感智能的内发动机仿生学习机制。该学习机制以生物体感觉运动系统的学习机制为基础,包括评价环节、行为选择环节和取向环节,采用模糊神经网络构建情感模型,情感模型的输出作为评价环节的内部奖赏信号。该学习机制能够使机器人在未知环境下通过自主的学习和训练逐渐形成、发展和完善趋光行为技能,通过情感智能的作用可以增加试探成功次数和减小学习步数,仿真实验证明了该方法的有效性。     

Experimental study on a learning control system with bound estimation for underwater robots

Underwater robotic vehicles (URVs) have become an important tool for numerous underwater tasks due to their greater speed, endurance, depth capability, and a higher factor of safety than human divers. However, most vehicle control system designs are based on simplified vehicle models and often result in poor vehicle performance due to the nonlinear and time-varying vehicle dynamics having parameter uncertainties. Conventional proportional-integral-derivative (PID) type controllers cannot provide good performance without fine-tuning the controller gains and may fail for sudden changes in the vehicle dynamics and its environment. Conventional adaptive control systems based on parameter adaptation techniques also fail in the presence of unmodeled dynamics.This paper describes a new vehicle control system using the bound estimation techniques, capable of learning, and adapting to changes in the vehicle dynamics and parameters. The control system was extensively wet-tested on the Omni-Directional Intelligent Navigator (ODIN)-a six degree-of-freedom, experimental underwater vehicle developed at the Autonomous Systems Laboratory, and its performance was compared with the performance of a conventional linear control system. The results showed the controller's ability to provide good performance in the presence of unpredictable changes in the vehicle dynamics and its environment, and it's capabilities of learning and adapting.     

一种仿生水下机器人的设计与动力学分析

设计了一种基于波动长鳍推进的仿生水下机器人, 两侧长鳍对称安装于机器人本体两侧. 两侧长鳍分别由十个舵机驱动, 并按照余弦函数波动. 设计了实时控制器, 通过调整鳍条的振动频率和幅值达到控制长鳍运动的目的. 加速度信息和角速度信息由一个惯性测量单元采集. 为获取机器人游动性能与振动频率以及振动幅值之间的关系, 本文给出了长鳍波动运动的运动学分析和动力学分析. 本文通过将长鳍分割成若干小单元并单独计算作用于每个小单元上的作用力, 再计算所有小单元作用力在一个波动周期内的合力的方法, 获得了整个长鳍产生的平均推力. 通过前进游动和旋转游动实验, 验证了机构设计、运动学分析和动力学分析的有效性, 最后讨论了游动性能与波动参数之间的关系.     

Design and field testing of a rover with an actively articulated suspension system in a Mars analog terrain

This study presents the electromechanical design, the control approach, and the results of a field test campaign with the hybrid wheeled‐leg rover SherpaTT. The rover ranges in the 150 kg class and features an actively articulated suspension system comprising four legs with actively driven and steered wheels at each leg’s end. Five active degrees of freedom are present in each of the legs, resulting in 20 active degrees of freedom for the complete locomotion system. The control approach is based on force measurements at each wheel mounting point and roll–pitch measurements of the rover’s main body, allowing active adaption to sloping terrain, active shifting of the center of gravity within the rover’s support polygon, active roll–pitch influencing, and body‐ground clearance control. Exteroceptive sensors such as camera or laser range finder are not required for ground adaption. A purely reactive approach is used, rendering a planning algorithm for stability control or force distribution unnecessary and thus simplifying the control efforts. The control approach was tested within a 4‐week field deployment in the desert of Utah. The results presented in this paper substantiate the feasibility of the chosen approach: The main power requirement for locomotion is from the drive system, active adaption only plays a minor role in power consumption. Active force distribution between the wheels is successful in different footprints and terrain types and is not influenced by controlling the body’s roll–pitch angle in parallel to the force control. Slope‐climbing capabilities of the system were successfully tested in slopes of up to 28° inclination, covered with loose soil and duricrust. The main contribution of this study is the experimental validation of the actively articulated suspension of SherpaTT in conjunction with a reactive control approach. Consequently, hardware and software design as well as experimentation are part of this study.     

Team NimbRo at MBZIRC 2017: Fast landing on a moving target and treasure hunting with a team of micro aerial vehicles

The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 has defined ambitious new benchmarks to advance the state‐of‐the‐art in autonomous operation of ground‐based and flying robots. This study covers our approaches to solve the two challenges that involved micro aerial vehicles (MAV). Challenge 1 required reliable target perception, fast trajectory planning, and stable control of an MAV to land on a moving vehicle. Challenge 3 demanded a team of MAVs to perform a search and transportation task, coined “Treasure Hunt,” which required mission planning and multirobot coordination as well as adaptive control to account for the additional object weight. We describe our base MAV setup and the challenge‐specific extensions, cover the camera‐based perception, explain control and trajectory‐planning in detail, and elaborate on mission planning and team coordination. We evaluated our systems in simulation as well as with real‐robot experiments during the competition in Abu Dhabi. With our system, we—as part of the larger team NimbRo—won the MBZIRC Grand Challenge and achieved a third place in both subchallenges involving flying robots.     

Research on traversability of tracked vehicle on slope with unfixed obstacles: derivation of climbing-over,tipping-over,and sliding-down conditions

When a tracked robot explores a volcanic environment, it faces difficulty in climbing over unfixed obstacles such as loose rocks on the ground. Such unfixed obstacles sometimes cause the sliding-down or tipping-over of the robot. Although such phenomena should be avoided for the success of the mission, they have not been sufficiently studied yet. Therefore, this research aims at understanding the phenomena for a tracked vehicle climbing an unfixed obstacle on a slope, and considers the conditions of climbing-over, tipping-over, and sliding-down. To simplify the problem, a model of a single track and circular cross-section obstacle is used in this research. The climbing-over and tipping-over conditions are derived from the geometric relationship, and the sliding-down condition is derived from statics. Moreover, some experiments using an actual robot are conducted to verify the validity of the conditions. The results show that the derived conditions are reasonable. Furthermore, it is revealed that unfixed obstacles typically tend to slide down more than fixed obstacles because of the number of contact points that can support a robot.     

智能交互的物体识别增量学习技术综述

智能交互系统是研究人与计算机之间进行交流与通信,使计算机能够在最大程度上完成交互者的某个指令的一个领域。其发展的目标是实现人机交互的自主性、安全性和友好性。增量学习是实现这个发展目标的一个途径。本文对智能交互系统的任务、背景和获取信息来源进行简要介绍,主要对增量学习领域的已有工作进行综述。增量学习是指一个学习系统能不断地从新样本中学习新的知识,非常类似于人类自身的学习模式。它使智能交互系统拥有自我学习,提高交互体验的能力。文中对主要的增量学习算法的基本原理和特点进行了阐述,分析各自的优点和不足,并对进一步的研究方向进行展望。     

变磁力吸附爬壁机器人的结构设计与爬越焊缝特性

为提高储油罐壁检测作业的可靠性和安全性,研究设计了一种变磁力吸附机器人本体结构并研究其爬越焊缝特性.首先基于ANSYS平台对机器人变磁力吸附单元进行仿真分析,得到了机器人本体的磁感应强度分布及磁吸附力.然后建立了爬越焊缝过程的动力学模型,应用ADAMS对其进行动力学仿真分析,得到了该过程中的磁吸附力变化曲线.通过分析验证了机构的合理性和实用性.     

Infrared dim and small target detection based on two-stage U-skip context aggregation network with a missed-detection-and-false-alarm combination loss

Multimedia Tools and Applications - Infrared small target detection (ISTD) is a critical technique in both civil and military applications such as leak and defect inspection, cell segmentation for...     

灰色预测和混沌PSO的红外小目标检测

在分析红外图像弱小目标和背景特征的基础上,提出了一种基于灰色预测和混沌PSO的红外小目标检测方法.该方法首先采用灰色系统理论中的GM(1.1)模型对红外图像中的背景进行时域预测,并用实际图像减去预测图像得到残差图像,在抑制背景的同时增强了目标;然后提出了混沌粒子群优化(particle swarm optimizati...     

Fully autonomous micro air vehicle flight and landing on a moving target using visual–inertial estimation and model‐predictive control

The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) held in spring 2017 was a very successful competition well attended by teams from all over the world. One of the challenges (Challenge 1) required an aerial robot to detect, follow, and land on a moving target in a fully autonomous fashion. In this paper, we present the hardware components of the micro air vehicle (MAV) we built with off the self components alongside the designed algorithms that were developed for the purposes of the competition. We tackle the challenge of landing on a moving target by adopting a generic approach, rather than following one that is tailored to the MBZIRC Challenge 1 setup, enabling easy adaptation to a wider range of applications and targets, even indoors, since we do not rely on availability of global positioning system. We evaluate our system in an uncontrolled outdoor environment where our MAV successfully and consistently lands on a target moving at a speed of up to 5.0 m/s.     

Deformable fingertip with a friction reduction system based on lubricating effect for smooth operation under both dry and wet conditions

For stable robotic grasping, a surface with high friction is required; thus, a soft surface is preferable. In contrast, a slippery surface is preferable for inserting fingers into a narrow space or placing a grasped object on a table. Additionally, in an environment involving humans, such operations are performed under dry and wet conditions. Hence, this study aims at developing a soft robotic fingertip with a friction control system in which the surface friction is actively controllable under dry and wet conditions, whereas the external effects on friction, such as wetness, are minimized. The basic concept involves achieving high friction under both conditions by using a slit surface texture, while friction is reduced with a lubricating system by utilizing capillary action. The experimental validation shows that the proposed lubricating system embedded in a robotic finger surface successfully reduces friction under both conditions. The releasing and grasping operations reveal the efficacy of the proposed system in an actual situation. Additionally, the mechanism of the lubricating method is confirmed by introducing the spreading coefficient.     

Effect of handle design and target location on insertion and aim with a laparoscopic surgical tool

Two laparoscopic tools, a scissor-type grasper and an ergonomically designed grasper, were compared in terms of operation efficiency and physical workload while inserting into a simulated abdomen and aiming five cross-shaped targets. Thirty right-handed novice participants performed the tasks with five tool-grasping hand postures at two computer monitor angles that simulated reaching an organ during laparoscopic surgery. When comparing the two free style hand postures used, there was a significant improvement in operation efficiency. This demonstrated that the participants quickly became familiar with the Intuitool by finding new hand postures that will significantly help them reach the target faster and more accurately. The 45( composite function) monitor angle showed the worst accuracy and deviation, the 0( composite function) monitor angle showed the best accuracy and smallest deviation with the upper target. Thus it is recommended that the camera trocar be placed directly above the organ of interest, and the part of the organ to be reached should be displayed slightly above the center of the feedback monitor. For physical workload, the method of gripping the tools was the most important factor. The scissors-type tool caused the largest wrist flexion, in contrast both free styles hand postures with the Intuitool showed the least wrist flexion.     

一种融合多帧ICP和图优化的算法研究

当前基于迭代最近点拼接的同时定位与建图算法,存在误差积累、无法满足大范围定位精度的缺陷。为此,提出一种融合多帧迭代最近点和图优化的算法。在时域上处理点云拼接问题,将单帧迭代最近点算法推广到多帧进行最近点迭代,提取同一地点在不同时刻的数据特征,形成多个封闭循环,再运用基于最小二乘的图优化方法对点云拼接后的全网数据进行全局优化,消除累计误差,提升整体的定位精度。采用鲁巷和密歇根的数据进行测试,结果表明,该方法在一定程度上减少了匹配误差,平均误差为1.0m,最小误差为0.2m,可以满足大范围同步定位与建图的精度需求。