Active sonar-based bottom-following for unmanned underwater vehicles

The problem of high-precision bottom-following in the proximity of the seabed for open-frame unmanned underwater vehicles (UUVs) is addressed in this paper. The suggested approach consists of the integration of a guidance and control system with an active multi-hypothesis extended Kalman filter, able to estimate the motion of the vehicle with respect to the bottom profile. The guidance module is based on the definition of a suitable Lyapunov function associated with the bottom-following task, while the motion controller is a conventional autopilot, performing autoheading, autodepth, and autospeed. The motion of the vehicle is estimated from range and bearing measurements supplied by a high-frequency pencil-beam profiling sonar. Moreover, a general-purpose sensor-based guidance and control system for advanced UUVs, able to manage active sensing-based guidance and motion estimation modules, is presented. An application of the proposed architecture to execute high-precision bottom-following using Romeo, a prototype UUV, developed by the Robotics Dept. of the Istituto Automazione Navale, is described. Experimental results of tests, conducted in a high-diving pool with the vehicle equipped with a sonar profiler, are presented.     

基于STM32全海深ARV监控系统设计

针对全海深无人潜水器海洋参数监测的任务需求,结合“彩虹鱼”号全海深无人潜水器监控系统研制的实践经验,对全海深无人潜水器监控系统进行了相关研究,创新性地提出了一种以STM32为核心的全海深ARV监控系统的设计方案.基于模块化的设计思路实现了对ARV电源系统、运动控制系统和作业观导装置的有效控制,联合所研制的上位机可以对ARV水下作业状态及各传感器的数据进行实时控制与采集.大量单项试验、系统联调、水池试验及海试实验结果表明,监控系统方案设计合理、稳定可靠,且取得了满意的海试结果.     

Modeling and Identification of an Open-frame Underwater Vehicle: The Yaw Motion Dynamics

A semi-autonomous unmanned underwater vehicle (UUV), named LAURS, is being developed at the Laboratory of Sensors and Actuators at the University of Sao Paulo. The vehicle has been designed to provide inspection and intervention capabilities in specific missions of deep water oil fields. In this work, a method of modeling and identification of yaw motion dynamic system model of an open-frame underwater vehicle is presented. Using an on-board low cost magnetic compass sensor the method is based on the utilization of an uncoupled 1-DOF (degree of freedom) dynamic system equation and the application of the integral method which is the classical least squares algorithm applied to the integral form of the dynamic system equations. Experimental trials with the actual vehicle have been performed in a test tank and diving pool. During these experiments, thrusters responsible for yaw motion are driven by sinusoidal voltage signal profiles. An assessment of the feasibility of the method reveals that estimated dynamic system models are more reliable when considering slow and small sinusoidal voltage signal profiles, i.e. with larger periods and with relatively small amplitude and offset.     

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.     

欠驱动UUV的空间直线路径跟踪控制

针对欠驱动无人水下航行器(UUV)设计了基于视距导航法的路径跟踪控制方法。采用视距导航法建立欠驱动UUV的路径跟踪误差模型将位置误差控制转换为航速控制、艏向角控制和纵倾角控制,使得路径跟踪问题的输出空间从6个自由度降为3个自由度。对简化的3个自由度设计控制器,并证明了控制器的稳定性。最后以空间平行于x轴的直线为参考路径进行了路径跟踪控制仿真,验证了该方法的有效性。     

基于PF和UKF的水下目标运动估计方法研究

针对无人水下航行器（UUV）目标跟踪控制需求,分别提出了水下目标的粒子滤波（PF）和无迹卡尔曼滤波（UKF）运动估计方法,建立了目标运动参考坐标系,给出了坐标系之间转换基本方法;设计了建立了目标的典型运动模型和非线性随机运动模型,利用前视声呐实测实验数据,完成水下目标运动估计。通过与扩展卡尔曼滤波器（EKF）的目标运动估计对比仿真实验,验证了PF和UKF两种目标运动估计方法的有效性。     

UUV水声探测功能与电磁非触发引信融合测试系统设计

针对UUV(Unmanned Underwater Vehicle,无人潜航器)的水声探测功能与电磁非触发引信,传统的分立式测试方法分别采用两套独立的硬件电路,结构复杂、操作烦琐.设计了一种针对UUV的水声探测功能与电磁非触发引信的融合测试系统,底层基于嵌入式测试单元,结合主控软件中的产品数据库和目标仿真器,通过共用部分硬件电路的方式实现测试功能的融合,能够在保证测试性能与可靠性的基础上有效降低系统复杂度,同时提高测试效率.此外,通过基于LSTM(Long Short-Term Memory,长短期记忆)网络的RUL(Remaining Useful Life,剩余寿命)估计技术,能够对嵌入式测试单元的健康状况进行实时评估,提升系统可维护性.     

Optic flow regulation: the key to aircraft automatic guidance

We have developed a visually based autopilot which is able to make an air vehicle automatically take off, cruise and land, while reacting appropriately to wind disturbances (head wind and tail wind). This autopilot consists of a visual control system that adjusts the thrust so as to keep the downward optic flow (OF) at a constant value. This autopilot is therefore based on an optic flow regulation loop. It makes use of a sensor, which is known as an elementary motion detector (EMD). The functional structure of this EMD was inspired by that of the housefly, which was previously investigated at our Laboratory by performing electrophysiological recordings while applying optical microstimuli to single photoreceptor cells of the insect's compound eye.

We built a proof-of-concept, tethered rotorcraft that circles indoors over an environment composed of contrasting features randomly arranged on the floor. The autopilot, which we have called OCTAVE (Optic flow based Control sysTem for Aerial VEhicles), enables this miniature (100 g) rotorcraft to carry out complex tasks such as ground avoidance and terrain following, to control risky maneuvers such as automatic take off and automatic landing, and to respond appropriately to wind disturbances. A single visuomotor control loop suffices to perform all these reputedly demanding tasks. As the electronic processing system required is extremely light-weight (only a few grams), it can be mounted on-board micro-air vehicles (MAVs) as well as larger unmanned air vehicles (UAVs) or even submarines and autonomous underwater vehicles (AUVs). But the OCTAVE autopilot could also provide guidance and/or warning signals to prevent the pilots of manned aircraft from colliding with shallow terrain, for example.     

面向北京2022年冬奥会水下火炬传递的新型机器人

面向北京2022年冬奥会水下火炬传递应用需求,设计水下变结构机器人和两栖机器人,并提出水下火炬传递控制方法。克服机械臂（含火炬）运动和野外流场的复合扰动是火炬传递控制的关键问题,尤其是手持燃烧火炬的机械臂对浮游模式水下机器人的影响更加显著。针对以上问题,本文提出一种基于自适应控制策略的水下火炬传递控制方法,在线辨识静力学和流场水动力参数,预先补偿控制扰动,实现水下机器人的高精度姿态控制、位置控制。最终,在北京2022年冬奥会上成功完成奥运史上首次机器人间水下火炬接力。     

基于SVM算法的碟形水下机器人姿态预测方法研究

碟形水下机器人的运动控制过程是非常复杂的,涉及到很多影响因素,并且是一个非线性控制过程,其姿态控制过程的系统模型的辨识对于实现机器人精确的控制和不同水文环境的自适应预测控制有着重要意义。因支持向量机(SVM)算法经过严格的数学推导,且在非线性等方面的良好表现,提出了将SVM算法用于碟形水下机器人模型的辨识,并设计了一组基于SVM的多输入多输出系统辨识器,可针对控制量进行姿态变化预测。通过在水池中测试的实验数据进行辨识和预测。实验验证预测的均方差不超过0.004,实验结果验证了该算法对碟形潜水器的姿态运动控制系统的辨识与预测有着良好的效果。     

指定域多无人机协同目标跟踪研究

由于战场环境日益复杂、对抗性日益增强、任务日益多样和单机能力受限特性,多无人机协同执行作战任务已经成为无人机系统应用的重要发展趋势;针对协同作战中的系统复杂性、时间敏感性和通信计复杂性等特点进行分析,结合无人机运动性能及自主协同控制能力提出指定域多无人机协同目标跟踪的研究模型;首先建立面向战术任务的多无人机协同目标跟踪模型,其次在指定域范围内对多无人机进行面向持续跟踪模型的优化,最后选取一定数量的无人机在指定域内进行仿真,实验结果表明：面向持续的指定域多无人机协同目标跟踪模型是有效的,且具有较好的目标状态估计性能。     

Robot navigation in cluttered 3-D environments using preference-based fuzzy behaviors.

Autonomous navigation systems for mobile robots have been successfully deployed for a wide range of planar ground-based tasks. However, very few counterparts of previous planar navigation systems were developed for 3-D motion, which is needed for both unmanned aerial and underwater vehicles. A novel fuzzy behavioral scheme for navigating an unmanned helicopter in cluttered 3-D spaces is developed. The 3-D navigation problem is decomposed into several identical 2-D navigation subproblems, each of which is solved by using preference-based fuzzy behaviors. Due to the shortcomings of vector summation during the fusion of the 2-D subproblems, instead of directly outputting steering subdirections by their own defuzzification processes, the intermediate preferences of the subproblems are fused to create a 3-D solution region, representing degrees of preference for the robot movement. A new defuzzification algorithm that steers the robot by finding the centroid of a 3-D convex region of maximum volume in the 3-D solution region is developed. A fuzzy speed-control system is also developed to ensure efficient and safe navigation. Substantial simulations have been carried out to demonstrate that the proposed algorithm can smoothly and effectively guide an unmanned helicopter through unknown and cluttered urban and forest environments.     

Research on disturbance rejection motion control method of USV for UUV recovery

The recovery of unmanned underwater vehicle (UUV) by unmanned surface vehicle (USV) has the characteristics of autonomy, safety, and efficiency. Taking the recovery of UUV by USV as the engineering background, this paper studies the guidance and anti-interference motion control of USV in the recovery process. Aiming at the problem of dynamic guidance when recovering UUV, the USV guidance strategy for UUV recovery is studied. Fuzzy guidance is introduced as the dynamic terminal guidance method, and a layered guidance strategy combining classical guidance and fuzzy guidance is proposed. On the basis of the theory of compact form dynamic linearization-based model-free adaptive control (CFDL-MFAC), the motion control of USV in the process of recovering UUV under the influence of model perturbation, external interference, and other uncertainties is studied. Theoretical analysis and experimental results show that there is a contradiction in the matching of dynamic change speed between the USV heading control subsystem and CFDL-MFAC. By introducing the difference item into the standard control criterion to weaken the integral effect in the heading control subsystem of USV, a difference-type compact format model-free adaptive control method (DCFDL-MFAC) is proposed, and the stability of DCFDL-MFAC method is proved theoretically. The effectiveness and practicability of the proposed method are verified by simulation tests and field tests of “Dolphin IB” small USV.     

Ensemble and Fuzzy Kalman Filter for position estimation of an autonomous underwater vehicle based on dynamical system of AUV motion

An underwater vehicle is useful in the monitoring of the unstructured and dangerous underwater conditions. One of the unmanned underwater vehicle is AUV. AUV is a robotic device that is driven through the water by a propulsion system, controlled and piloted by an onboard computer, and maneuverable in three dimensions. This research explains about position estimation of AUV based on the Ensemble Kalman Filter (EnKF) and the Fuzzy Kalman Filter (FKF). EnKF is used as the estimation method of AUV’s position that maneuvering in 6 DOF (Degrees of Freedom) with the specified trajectory. The estimation results are simulated with Matlab. The simulations show the AUV position estimation based on the EnKF with some of the different ensembles and the comparison results of the position estimation between the EnKF and the FKF. The final result of these study shows that Ensemble Kalman Filter is better to estimate the trajectory of the dynamical equation of AUV motion with the error estimation of EnKF is 92% smaller in the x-position dan y-position, 6.5% smaller in the z-position, 93% smaller in the angle dan the computation of time is 50% faster than the estimation results of FKF.     

基于卡尔曼滤波的四旋翼飞行器姿态估计和控制算法研究

四旋翼飞行器作为无人机的一种,由于其简单气动布局和复杂的动力学模型,在控制领域获得了越来越多的学术关注;本文首先分析了微机电系统惯性测量单元(MEMS IMU)传感器的误差,给出了基于自回归(autoregressive,AR)噪声模型的卡尔曼滤波算法设计;然后根据加速度计和陀螺仪长短周期测量的不同特性,进一步对姿态数据做互补融合,实验表明此算法可以实现良好的滤波效果;基于上面的姿态估计,本文又提出了一种双增益的PD控制算法对飞行器进行姿态控制;最后将姿态估计算法和控制算法应用到实验平台中,可以实现四旋翼在支架上的自主悬停等功能.     

Hybrid Observer Concept for Sensor Fusion of Sporadic Measurements for Underwater Navigation

International Journal of Control, Automation and Systems - Accurate underwater navigation systems are required for closed-loop guidance and control of unmanned underwater vehicles (UUV). This paper...     

舵桨联合操纵水下机器人运动控制研究

水下机器人空间运动具有耦合性和非线性等特点,具有良好品质的运动控制器是水下机器人完成各种作业的前提.针对某舵桨联合操纵小型自主式水下机器人运动控制问题进行了研究.对速度,深度和艏向控制系统进行了介绍,根据控制需求,建立了水下机器人动力学模型,对执行机构进行了描述,设计了水下机器人滑模控制方案,采用变速趋近项代替一般指数...     

Proximal object and hazard detection for autonomous underwater vehicle with optical fibre sensors

This paper describes short range and tactile optical fibre sensors for marine applications. The sensors are designed for obstacle avoidance on unmanned underwater vehicles (UUVs) operating in confined spaces, but have other possible applications. The fibre sensors augment the sensory abilities derived from ultrasonic and other sensors employed for marine proximity measurement. Of particular interest is proximity detection in the “near” (less than 1 m) and tactile areas. The paper describes the basic principle of operation and alternative sensor configurations. Results are given based on laboratory tests and deployment on a mini autonomous submersible in a test pool.     

Mobile manipulation: The robotic assistant

Mobile manipulation capabilities are key to many new applications of robotics in space, underwater, construction, and service environments. This paper discusses the development of robotic “assistance” capabilities to aid workers in the accomplishment of a variety of physical operations and presents various control strategies developed for vehicle-arm coordination, compliant motion tasks, and cooperative manipulation between multiple platforms. These strategies have been implemented on two holonomic mobile platforms designed and built at Stanford in collaboration with Oak Ridge National Laboratories and Nomadic Technologies.     

Memory-Based In Situ Learning for Unmanned Vehicles

The ultimate goal of our research is to provide teams of unmanned underwater vehicles (UUVs) some of the abilities of animals to adapt to their environment using their memories, without requiring exhaustive trial-and-error testing or complex modeling of the environment. We focus on UUVs because they offer the promise of making dangerous tasks such as searching for underwater hazards or surveying the ocean bottom more safe and economical for government and commercial operations. We adopt a team concept to reduce overall mission cost using several low-cost subordinate UUVs to augment the sensor capabilities of a higher-capability lead UUV. Our goal is to develop a team of robots that would have the capability to learn their roles and improve team strategies so that the team can meet its overall goals in dynamic unstructured. Our research uses a sensor-input-based metric for success combined with a training regimen based on recently collected memories - a temporal series of sensor/action relationships - in which robots with "ears" listen for a leader robot and attempt to follow, and where the ensuing formations are a result of emergent behavior