Cooperative formation control of autonomous underwater vehicles: An overview

Formation control is a cooperative control concept in which multiple autonomous underwater mobile robots are deployed for a group motion and/or control mission. This paper presents a brief review on various cooperative search and formation control strategies for multiple autonomous underwater vehicles (AUV) based on literature reported till date. Various cooperative and formation control schemes for collecting huge amount of data based on formation regulation control and formation tracking control are discussed. To address the challenge of detecting AUV failure in the fleet, communication issues, collision and obstacle avoidance are also taken into attention. Stability analysis of the feasible formation is also presented. This paper may be intended to serve as a convenient reference for the further research on formation control of multiple underwater mobile robots.     

UNION: underwater intelligent operation and navigation

The main goal of the UNION ESPRIT Basic Research Action is to develop methods for increasing the autonomy and intelligence of underwater remotely operated vehicles (ROVs). The project focuses mainly on the development of coordinated control and sensing strategies for combined manipulator and vehicle systems. Both fundamental theories and methods for the design of these heterogeneous systems are investigated. A complex canonical mission in the field of offshore inspection maintenance and repair tasks was chosen as an integration guideline of all the results     

基于多传感器的无人机避障方法研究及应用

随着无人机巡检作业方式应用越来越广泛,巡检过程中对障碍物检测并进行避障显得愈发关键。若无人机碰到杆塔或线路不仅会造成无人机自身的损坏,还会对居民用电造成影响,给检修带来麻烦。毫米波雷达、激光雷达、双目视觉传感器在机器人避障中有广泛应用。但是基于输电线路巡检的多旋翼无人机的实际情况,传感器器件的选型、尺寸、重量,以及障碍物信息与飞控的融合,显得尤为重要。通过对多旋翼无人机搭载毫米波雷达、双目视觉传感器、差分GPS进行了研究,采用多传感器融合方法检测障碍物,利用虚拟力场法(VFF)进行航迹重规划,并实际飞行验证。测试表明该方法对杆塔避障取得了较好的应用效果。     

基于修正导航向量场的AUV自主避障方法

针对复杂海洋环境下的自治水下机器人(Autonomous underwater vehicle, AUV)三维避障问题, 本文提出了一种高效的修正导航向量场方法.构建自由空间下的初始导航向量场, 引导AUV以最短路径向目标点航行.定义修正矩阵来量化描述障碍物对初始导航向量场的影响, 得到障碍空间下的修正导航向量场, 使得AUV向目标点航行的同时躲避静态障碍.通过结合障碍物运动速度, 分别构建相对初始导航向量场与相对修正导航向量场, 并采取有限时域推演与调整策略, 最终引导AUV安全躲避动态障碍.仿真结果表明, 本方法能较好地应用于复杂海洋环境下的AUV避障任务.     

基于毫米波雷达的智能车辆纵横向主动避障控制系统设计

智能车辆所搭载监测设备对障碍物目标的识别准确性,影响行驶车辆的纵横向避障能力。为避免车辆与障碍物发生碰撞,提升智能车辆的纵横向避障能力,设计基于毫米波雷达的智能车辆纵横向主动避障控制系统。在底层控制单元中,按需连接纵横向导航控制元件与毫米波雷达摄像头,完成智能车辆纵横向主动避障控制系统的部件结构设计。利用毫米波雷达监测所得的车辆避障图像,定义空间坐标系转换条件,通过标定雷达相机参数的方式,实现基于毫米波雷达的智能车辆避障路径规划。建立车辆纵横向运动模型,根据避障安全距离计算结果,完善具体控制流程,联合各级硬件应用结构,完成基于毫米波雷达的智能车辆纵横向主动避障控制系统的设计。实验结果表明,所设计系统可在智能车辆通过障碍物目标时,保证车体与障碍物之间的距离大于0.3m,能够避免碰撞行为发生,对于车载监测设备而言,其对于障碍物目标的准确识别能力得到了保障,能够有效提升智能车辆纵横向避障能力。     

基于多波束前视声呐的水下障碍物检测及避障算法

近年来,人们开始不断地开发海洋资源和空间,如在海底铺设大量的天然气管道以便于运输,因此,利用自主式水下航行器去探测海底天然气管道是否泄漏的技术,就具有重大的战略意义。基于自主式水下航行器搭载的多波束前视声呐采集的数据,进行声呐图像中的障碍物检测,提出了一种基于类间方差及小区域抑制的障碍物检测算法。然后,利用声呐图像的障碍物检测结果,设计了基于障碍物轮廓的避障算法,来估计合理的避障角度,传送给水下航行器的主控来控制航行器避开障碍物。     

Development of an Unmanned Surface Vehicle System for the 2014 Maritime RobotX Challenge

This paper addresses the development of an unmanned surface vehicle (USV) system by Team Angry‐Nerds from KAIST for the inaugural Maritime RobotX Challenge competition, which was held on October 20‐26, 2014, in Marina Bay, Singapore. The USV hardware was developed on a catamaran platform by integrating various system components, including propulsion, sensors, computer, power, and emergency systems. The competition comprised five mission tasks: 1) navigation and control, 2) underwater search and report, 3) automatic docking, 4) buoy search and observation, and 5) obstacle detection and avoidance. Onboard intelligence was a key factor for all of the mission tasks which needed to be performed autonomously with no human intervention. Software algorithms for vehicle autonomy were developed, and executable computer codes were implemented and integrated with the developed USV hardware system. This paper describes the development process of the USV system and its application to the competition mission tasks.     

A 3D reactive collision avoidance algorithm for underactuated underwater vehicles

Avoiding collisions is an essential goal of the control system of autonomous vehicles. This paper presents a reactive algorithm for avoiding obstacles in a three‐dimensional space, and shows how the algorithm can be applied to an underactuated underwater vehicle. The algorithm is based on maintaining a constant avoidance angle to the obstacle, which ensures that a guaranteed minimum separation distance is achieved. The algorithm can thus be implemented without knowledge of the obstacle shape. The avoidance angle is designed to compensate for obstacle movement, and the flexibility of operating in 3D can be utilized to implement traffic rules or operational constraints. We exemplify this by incorporating safety constraints on the vehicle pitch and by making the vehicle seek to move behind the obstacle, while also minimizing the required control effort. The underactuation of the vehicle induces a sway and heave movement while turning. To avoid uncontrolled gliding into the obstacle, we account for this movement using a Flow frame controller, which controls the direction of the vehicle's velocity rather than just the pitch and yaw. We derive conditions under which it is ensured that the resulting maneuver is safe, and these results are verified trough simulations and through full‐scale experiments on the Hugin HUS autonomous underwater vehicle. The latter demonstrates the performance of the proposed algorithm when applied to a case with unmodeled disturbances and sensor noise, and shows how the modular nature of the collision avoidance algorithm allows it to be applied on top of a commercial control system.     

面向斜拉桥索塔巡检的旋翼UAV避障航迹规划

针对斜拉桥索塔巡检的旋翼UAV避障航迹规划问题,提出了一种面向斜拉桥索塔巡检的旋翼UAV避障航迹规划方法。该方法以巡检过程中旋翼UAV的能量消耗为航迹优劣评价指标,利用基于信息熵理论改进后的遗传算法获取能量消耗最少航迹,并提出双圆弧避障策略对航迹上存在斜拉索障碍的局部区域进行航迹重新规划,使之能有效地避让斜拉索障碍,保障旋翼UAV的飞行安全。以咸阳渭城桥索塔的外观巡检为例进行仿真验证,仿真结果表明,所提方法规划的航迹有效地降低了巡检旋翼UAV的能量消耗,确保了巡检旋翼UAV的飞行安全,能够较好地适用于斜拉桥索塔外观巡检。     

Robust underwater obstacle detection and collision avoidance

A robust obstacle detection and avoidance system is essential for long term autonomy of autonomous underwater vehicles (AUVs). Forward looking sonars are usually used to detect and localize obstacles. However, high amounts of background noise and clutter present in underwater environments makes it difficult to detect obstacles reliably. Moreover, lack of GPS signals in underwater environments leads to poor localization of the AUV. This translates to uncertainty in the position of the obstacle relative to a global frame of reference. We propose an obstacle detection and avoidance algorithm for AUVs which differs from existing techniques in two aspects. First, we use a local occupancy grid that is attached to the body frame of the AUV, and not to the global frame in order to localize the obstacle accurately with respect to the AUV alone. Second, our technique adopts a probabilistic framework which makes use of probabilities of detection and false alarm to deal with the high amounts of noise and clutter present in the sonar data. This local probabilistic occupancy grid is used to extract potential obstacles which are then sent to the command and control (C2) system of the AUV. The C2 system checks for possible collision and carries out an evasive maneuver accordingly. Experiments are carried out to show the viability of the proposed algorithm.     

基于图与势场法的多车道编队控制

多车协同驾驶能显著提高交通安全和效率,是未来5G网联自动驾驶技术的重要应用场景之一.传统上,多车协同驾驶的主要形式为单一车道上的无人车队列,其队列稳定性受队列长度、通信距离及延迟的限制.本文提出一种无人车编队方法,将单车道队列扩展为多车道护航编队.针对不同场景下的需求设计多车道编队调整策略,结合基于图的分布式控制,完成任意预定义的编队结构;同时,利用势场法对行车环境建立势场模型,实现无人车的避障轨迹规划,提高编队的避障能力;最后,结合纵横向控制器,实现无人车多车道护航编队控制.仿真实验表明,本文提出的无人车多车道护航编队方法,能适应不同交通场景,如道路变化、障碍车运动等,完成自动变换编队结构,实现安全、高效通行.     

A coordinated control of an underwater vehicle and robotic manipulator

Remotely operated underwater robotic vehicles (URVs) have been used for various tasks: inspection, recovery, construction, etc. With the increased utilization of remotely operated vehicles in subsea applications, the development of autonomous vehicles becomes highly desirable to enhance operator efficiency. However, engineering problems associated with the high density, nonuniform and unstructured seawater environment, and the nonlinear response of the vehicle make a high degree of autonomy difficult to achieve. The vehicles are usually equipped with mechanical manipulators that are utilized during the working mode. The accurate performance of the vehicle during the working mode can be achieved by controlling the vehicle and manipulator at the same time and compensating the end-effector error due to the vehicle motion. This article describes an adaptive control strategy for the coordinated control of an underwater vehicle and its robotic manipulator. The effectiveness of the control system is investigated by case study. The results show that the presented control system can provide the high performance of the vehicle and manipulator in the presence of unpredictable changes in the dynamics of the vehicle and its environment.     

An evaluation of stereo and laser‐based range sensing for rotorcraft unmanned aerial vehicle obstacle avoidance

We present an evaluation of stereo vision and laser‐based range sensing for rotorcraft unmanned aerial vehicle (RUAV) obstacle avoidance. Our focus is on sensors that are suitable for mini‐RUAV class vehicles in terms of weight and power consumption. The study is limited to the avoidance of large static obstacles such as trees. We compare two commercially available devices that are representative of the state of the art in two‐dimensional scanning laser and stereo‐based sensing. Stereo is evaluated with three different focal length lenses to assess the tradeoff between range resolution and field of view (FOV). The devices are evaluated in the context of obstacle avoidance through extensive flight trials with an RUAV. We discuss the merits and limitations of each sensor type, including sensing range, FOV, accuracy, and susceptibility to lighting conditions. We show that the stereo device fitted with 8‐mm lenses has a better sensing range and vertical FOV than the laser device; however, it relies on careful calibration and is affected by high‐contrast outdoor lighting conditions. The laser has a wider horizontal FOV and is more reliable at detecting obstacles that are within a 20‐m range. Overall the laser produced superior obstacle avoidance performance, with a success rate of 84% compared to 42% for 8‐mm stereo. © 2012 Wiley Periodicals, Inc.     

A Scalable Adaptive Approach to Multi-Vehicle Formation Control with Obstacle Avoidance

This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multi-vehicle systems (MVSs) in complex obstacle-laden environments. The MVS under consideration consists of a leader vehicle with an unknown control input and a group of follower vehicles, connected via a directed interaction topology, subject to simultaneous unknown heterogeneous nonlinearities and external disturbances. The central aim is to achieve effective and collision-free formation tracking control for the nonlinear and uncertain MVS with obstacles encountered in formation maneuvering, while not demanding global information of the interaction topology. Toward this goal, a radial basis function neural network is used to model the unknown nonlinearity of vehicle dynamics in each vehicle and repulsive potentials are employed for obstacle avoidance. Furthermore, a scalable distributed adaptive formation tracking control protocol with a built-in obstacle avoidance mechanism is developed. It is proved that, with the proposed protocol, the resulting formation tracking errors are uniformly ultimately bounded and obstacle collision avoidance is guaranteed. Comprehensive simulation results are elaborated to substantiate the effectiveness and the promising collision avoidance performance of the proposed scalable adaptive formation control approach.      

Fuzzy-Logic Based Navigation of Underwater Vehicles

A fuzzy logic based general purpose modular control architecture is presented for underwater vehicle autonomous navigation, control and collision avoidance. Three levels of fuzzy controllers comprising the sensor fusion module, the collision avoidance module and the motion control module are derived and implemented. No assumption is made on the specific underwater vehicle type, on the amount of a priori knowledge of the 3-D undersea environment or on static and dynamic obstacle size and velocity. The derived controllers account for vehicle position accuracy and vertical stability in the presence of ocean currents and constraints imposed by the roll motion. The main advantage of the proposed navigation control architecture is its simplicity, modularity, expandability and applicability to any type of autonomous or semi-autonomous underwater vehicles. Extensive simulation studies are performed on the NPS Phoenix vehicle whose dynamics have been modified to account for roll stability.     

Obstacle Avoidance for Unmanned Aerial Vehicles

This work is framed within the PITVANT project and aims to contribute to the development of obstacle avoidance techniques for unmanned aerial vehicles (UAVs). The paper describes the design, implementation and experimental evaluation of a potential field obstacle avoidance algorithm based on the fluid mechanics panel methods. Obstacles and the UAV goal position are modeled by harmonic functions thus avoiding the presence of local minima. Adaptations are made to apply the method to the automatic control of a fixed wing aircraft, relying only on a local map of the environment that is updated with information from sensors onboard the aircraft. Hardware-In-Loop simulations show the good performance of the proposed algorithm in the envisioned mission scenarios for the PITVANT vehicles.     

Aerial robotic contact-based inspection: planning and control

The challenge of aerial robotic contact-based inspection is the driving motivation of this paper. The problem is approached on both levels of control and path-planning by introducing algorithms and control laws that ensure optimal inspection through contact and controlled aerial robotic physical interaction. Regarding the flight and physical interaction stabilization, a hybrid model predictive control framework is proposed, based on which a typical quadrotor becomes capable of stable and active interaction, accurate trajectory tracking on environmental surfaces as well as force control. Convex optimization techniques enabled the explicit computation of such a controller which accounts for the dynamics in free-flight as well as during physical interaction, ensures the global stability of the hybrid system and provides optimal responses while respecting the physical limitations of the vehicle. Further augmentation of this scheme, allowed the incorporation of a last-resort obstacle avoidance mechanism at the control level. Relying on such a control law, a contact-based inspection planner was developed which computes the optimal route within a given set of inspection points while avoiding any obstacles or other no-fly zones on the environmental surface. Extensive experimental studies that included complex “aerial-writing” tasks, interaction with non-planar and textured surfaces, execution of multiple inspection operations and obstacle avoidance maneuvers, indicate the efficiency of the proposed methods and the potential capabilities of aerial robotic inspection through contact.     

分布式无人机队列控制与动态障碍规避设计

研究分布式无人机集群巡航任务中的协同路径跟踪问题与动态避障控制问题.利用transverse feedback linearization(TFL)方法对无人机的动力学模型进行变换,通过解耦控制实现对期望巡航路径的跟踪.在期望路径方向上,设计基于一致性协议的分布式无人机队列协同控制算法,并结合势场法设计协同巡航过程中对移动障碍物的规避控制策略.在队列达成一致性目标的同时,能够保障队列行进的安全性.基于Lyapunov分析方法和LaSalle不变原理证明闭环系统的稳定性,同时采用能量法证明队列中的无人机不会与动态障碍物发生碰撞.最后,基于搭建的无人机协同飞行实验平台,完成多架无人机的协同队列控制和移动障碍物规避实验,飞行实验结果验证了所设计协同控制算法与避障控制策略的有效性.     

Cooperative bathymetry-based localization using low-cost autonomous underwater vehicles

We present a cooperative bathymetry-based localization approach for a team of low-cost autonomous underwater vehicles (AUVs), each equipped only with a single-beam altimeter, a depth sensor and an acoustic modem. The localization of the individual AUV is achieved via fully decentralized particle filtering, with the local filter’s measurement model driven by the AUV’s altimeter measurements and ranging information obtained through inter-vehicle communication. We perform empirical analysis on the factors that affect the filter performance. Simulation studies using randomly generated trajectories as well as trajectories executed by the AUVs during field experiments successfully demonstrate the feasibility of the technique. The proposed cooperative localization technique has the potential to prolong AUV mission time, and thus open the door for long-term autonomy underwater.     

Abstracting Vehicle Shape and Kinematic Constraints from Obstacle Avoidance Methods

Most obstacle avoidance techniques do not take into account vehicle shape and kinematic constraints. They assume a punctual and omnidirectional vehicle and thus they are doomed to rely on approximations when used on real vehicles. Our main contribution is a framework to consider shape and kinematics together in an exact manner in the obstacle avoidance process, by abstracting these constraints from the avoidance method usage. Our approach can be applied to many non-holonomic vehicles with arbitrary shape. For these vehicles, the configuration space is three-dimensional, while the control space is two-dimensional. The main idea is to construct (centred on the robot at any time) the two-dimensional manifold of the configuration space that is defined by elementary circular paths. This manifold contains all the configurations that can be attained at each step of the obstacle avoidance and is thus general for all methods. Another important contribution of the paper is the exact calculus of the obstacle representation in this manifold for any robot shape (i.e. the configuration regions in collision). Finally, we propose a change of coordinates of this manifold so that the elementary paths become straight lines. Therefore, the three-dimensional obstacle avoidance problem with kinematic constraints is transformed into the simple obstacle avoidance problem for a point moving in a two-dimensional space without any kinematic restriction (the usual approximation in obstacle avoidance). Thus, existing avoidance techniques become applicable. The relevance of this proposal is to improve the domain of applicability of a wide range of obstacle avoidance methods. We validated the technique by integrating two avoidance methods in our framework and performing tests in the real robot. Javier Minguez received the physics science degree in 1996 from the Universidad Complutense de Madrid, Madrid, Spain, and the Ph.D. degree in computer science and systems engineering in 2002 from the University of Zaragoza, Zaragoza, Spain. During his student period, in 1999 he was a research visitor in the Robotics and Artificial Intelligence Group, LAASCNRS, Toulouse, France. In 2000, he visited the Robot and ComputerVision Laboratory (ISR-IST), Technical University of Lisbon, Lisbon, Portugal. In 2001, he was with the Robotics Laboratory, Stanford University, Stanford, USA. He is currently a fulltime Researcher in the Robot, Vision, and Real Time Group, in the University of Zaragoza. His research interests are obstacle avoidance, motion estimation and sensor-based motion systems for mobile robots. Luis Montano was born on September 6, 1958 in Huesca, Spain. He received the industrial engineering degree in 1981 and the PhD degree in 1987 from the University of Zaragoza, Spain. He is an Associate Professor of Systems Engineering and Automatic Control at the University of Zaragoza (Spain). He has been Head of the Computer Science and Systems Engineering Department of the University of Zaragoza. Currently he is the coordinator of the Production Technologies Research in the Aragon Institute of Engineering Research and of the Robotics, Perception and Real Time group of the University of Zaragoza. He is principal researcher in robotic projects and his major research interests are mobile robot navigation and cooperative robots. José Santos-Victor received the PhD degree in Electrical and Computer Engineering in 1995 from Instituto Superior Técnico (IST - Lisbon, Portugal), in the area of Computer Vision and Robotics. He is an Associate Professor at the Department of Electrical and Computer Engineering of IST and a researcher of the Institute of Systems and Robotics (ISR), at the Computer and Robot Vision Lab - VisLab. (http://vislab.isr.ist.utl.pt) He is the scientific responsible for the participation of IST in various European and National research projects in the areas of Computer Vision and Robotics. His research interests are in the areas of Computer and Robot Vision, particularly in the relationship between visual perception and the control of action, biologically inspired vision and robotics, cognitive vision and visual controlled (land, air and underwater) mobile robots. Prof. Santos-Victor is an IEEE member and an Associated Editor of the IEEE Transactions on Robotics.