A nonholonomic control method for stabilizing an X4-AUV

A nonholonomic control method is considered for stabilizing all attitudes and positions (x, y, or z) of an underactuated X4 autonomous underwater vehicle (AUV) with four thrusters and six degrees of freedom (DOF), in which the positions are stabilized according to the Lyapunov stability theory. A dynamic model is first derived, and then a sequential nonlinear control strategy is implemented for the X4-AUV which is composed of translational and rotational subsystems. A controller for the translational subsystem stabilizes one position out of the x-, y-, and z-coordinates, whereas controllers for the rotational subsystems generate the desired roll, pitch, and yaw angles. Thus, the rotational controllers stabilize all the attitudes of the X4-AUV at the desired (x-, y-, or z-) position of the vehicle. Some numerical simulations are conducted to demonstrate the effectiveness of the proposed controllers.     

Autonomous collision detection and avoidance for ARAGON USV: Development and field tests

This study addresses the development of algorithms for multiple target detection and tracking in the framework of sensor fusion and its application to autonomous navigation and collision avoidance systems for the unmanned surface vehicle (USV) Aragon. To provide autonomous navigation capabilities, various perception sensors such as radar, lidar, and cameras have been mounted on the USV platform and automatic ship detection algorithms are applied to the sensor measurements. The relative position information between the USV and nearby objects is obtained to estimate the motion of the target objects in a sensor‐level tracking filter. The estimated motion information from the individual tracking filters is then combined in a central‐level fusion tracker to achieve persistent and reliable target tracking performance. For automatic ship collision avoidance, the combined track data are used as obstacle information, and appropriate collision avoidance maneuvers are designed and executed in accordance with the international regulations for preventing collisions at sea (COLREGs). In this paper, the development processes of the vehicle platform and the autonomous navigation algorithms are described, and the results of field experiments are presented and discussed.     

基于改进积分型变结构控制器的近水面机器人减摇鳍系统

海洋机器人在近水面低速航行时,由于海浪的作用将产生横摇运动.依据减摇鳍减摇原理和变结构控制理论,针对海浪波浪力作用的特点,在水下机器人横摇解耦模型的基础上,提出一种带有自适应机制的改进积分滑模控制器的设计.仿真结果表明,改进后的积分型滑模控制器在消除稳态误差的基础上,进一步提高了减摇效率.     

Intelligent mobile manipulator navigation using adaptive neuro-fuzzy systems

The work presented in this paper deals with the problem of autonomous and intelligent navigation of mobile manipulator, where the unavailability of a complete mathematical model of robot systems and uncertainties of sensor data make the used of approximate reasoning to the design of autonomous motion control very attractive.A modular fuzzy navigation method in changing and dynamic unstructured environments has been developed. For a manipulator arm, we apply the robust adaptive fuzzy reactive motion planning developed in [J.B. Mbede, X. Huang, M. Wang, Robust neuro-fuzzy sensor-based motion control among dynamic obstacles for robot manipulators, IEEE Transactions on Fuzzy Systems 11 (2) (2003) 249-261]. But for the vehicle platform, we combine the advantages of probabilistic roadmap as global planner and fuzzy reactive based on idea of elastic band. This fuzzy local planner based on a computational efficient processing scheme maintains a permanent flexible path between two nodes in network generated by a probabilistic roadmap approach. In order to consider the compatibility of stabilization, mobilization and manipulation, we add the input of system stability in vehicle fuzzy navigation so that the mobile manipulator can avoid stably unknown and/or dynamic obstacles. The purpose of an integration of robust controller and modified Elman neural network (MENN) is to deal with uncertainties, which can be translated in the output membership functions of fuzzy systems.     

基于PC104的超高速水下航行器测控程序设计与实现

超高速水下航行器自主航行试验需要雷载计算机提供时序控制、姿态自动控制等支持,在恶劣水下环境中测控系统的可靠性至关重要,它影响着实航数据的获取和航行控制研究的开展.以PC104嵌入式计算机为硬件平台,标准C语言为开发工具,针对超高速水下航行器航行控制和数据测量需求开发了测控程序.通过电平I/O进行时序及航行姿态控制,串口...     

自治水下机器人的自主启发式生物启发神经网络路径规划算法

针对复杂海流环境下自治水下机器人(autonomous underwater vehicle, AUV)的路径规划问题,本文在栅格地图的基础上给出了一种基于离散的生物启发神经网络(Glasius bio-inspired neural networks, GBNN)模型的新型自主启发式路径规划和安全避障算法,并考虑海流对路径规划的影响.首先建立GBNN模型,利用此模型表示AUV的工作环境,神经网络中的每一个神经元与栅格地图中的位置单元一一对应;其次,根据神经网络中神经元的活性输出值分布情况并结合方向信度算法实现自主规划AUV的运动路径;最后根据矢量合成算法确定AUV实际的航行方向.障碍物环境和海流环境下仿真实验结果表明了生物启发模型在AUV水下环境中路径规划的有效性.     

自主车辆安全系数估计与T形路口避碰规划

针对自主车辆T形路口协作问题,通过测量车辆速度和预碰点距离,提出安全系数估计方法,结合人工势场基本思想,实现车辆路口避碰,提高路口协作效率.首先以计算得到的安全系数为依据,评价驶入T形路口两辆自主车各自的安全程度,并利用人工势场产生的推拉作用估计协作自主车辆的期望控制力和速度.然后将估计的期望车速与反馈的实际车速形成偏差,利用增量型数字PI控制器实现自主车纵向车速的准确控制.最后以一定的策略完成两辆自主车的并线协作.仿真结果验证了基于安全系数估计的自主车辆T形路口协作避碰的可行性与有效性.     

A Wheelchair Steered through Voice Commands and Assisted by a Reactive Fuzzy-Logic Controller

This paper describes new results with a Reactive Shared-Control system that enables a semi-autonomous navigation of a wheelchair in unknown and dynamic environments. The purpose of the reactive shared controller is to assist wheelchair users providing an easier and safer navigation. It is designed as a fuzzy-logic controller and follows a behaviour-based architecture. The implemented behaviours are three: intelligent obstacle avoidance, collision detection and contour following. Intelligent obstacle avoidance blends user commands, from voice or joystick, with an obstacle avoidance behaviour. Therefore, the user and the vehicle share the control of the wheelchair. The reactive shared control was tested on the RobChair powered wheelchair prototype [6] equipped with a set of ranging sensors. Experimental results are presented demonstrating the effectiveness of the controller.     

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.     

Adaptive Behaviors in Autonomous Navigation with Collision Avoidance and Bounded Velocity of an Omnidirectional Mobile Robot

Integration of Control Theory and Genetic Programming paradigm toward development a family of controllers is addressed in this paper. These controllers are applied for autonomous navigation with collision avoidance and bounded velocity of an omnidirectional mobile robot. We introduce the concepts of natural and adaptive behaviors to relate each control objective with a desired behavior for the mobile robot. Natural behaviors lead the system to fulfill a task inherently. In this work, the motion of the mobile robot to achieve desired position, ensured by applying a Control-Theory-based controller, defines the natural behavior. The adaptive behavior, learned through Genetic-Programming, fits the robot motion in order to avoid collision with an obstacle while fulfilling velocity constraints. Hence, the behavior of the mobile robot is the addition of the natural and the adaptive behaviors. Our proposed methodology achieves the discovery of 9402 behaviors without collisions where asymptotic convergence to desired goal position is demonstrated by Lyapunov stability theory. Effectiveness of proposed framework is illustrated through a comparison between experiments and numerical simulations for a real mobile robot.     

基于相对特征的越野地形可通行性分析

自主地面车在越野环境下导航面临最困难的问题之一是对地形的理解，分析感知到的越野地形，作适合于自主车导航的可通行性分析。本文提出了越野高程地形的相对不变性概念，并利用这种性质提取出在一定尺度范围内地形具有的相对不变特征，如地形坡度、起伏度和粗糙度，最后基于模糊规则组合各特征对地形的可通行性进行评估。自主车越野导航实验表明，本文算法稳定有效，能满足自主地面车越野导航的需要。     

自治/遥控水下机器人北极冰下导航

针对北极高纬度和科学考察中长期冰站对海冰在一定范围内连续重复观测的需求,设计了面向冰下环境的自治/遥控水下机器人(北极ARV)导航定位系统.提出基于海冰运动修正的水下机器人自主导航方法,通过定时引入海冰运动信息,实时准确获取水下机器人相对于海冰的位置信息,这不仅提高了观测数据的实际应用价值,还提高了冰下作业的安全性.仿真试验和北极冰下应用证明这一导航系统具有精度高、稳定性好等优点.     

Visual Navigation for Mobile Robots: A Survey

Mobile robot vision-based navigation has been the source of countless research contributions, from the domains of both vision and control. Vision is becoming more and more common in applications such as localization, automatic map construction, autonomous navigation, path following, inspection, monitoring or risky situation detection. This survey presents those pieces of work, from the nineties until nowadays, which constitute a wide progress in visual navigation techniques for land, aerial and autonomous underwater vehicles. The paper deals with two major approaches: map-based navigation and mapless navigation. Map-based navigation has been in turn subdivided in metric map-based navigation and topological map-based navigation. Our outline to mapless navigation includes reactive techniques based on qualitative characteristics extraction, appearance-based localization, optical flow, features tracking, plane ground detection/tracking, etc... The recent concept of visual sonar has also been revised. This work is partially supported by DPI 2005-09001-C03-02 and FEDER funding.     

基于模糊混合控制的自治水下机器人路径跟踪控制

基于模糊混合控制策略,本文提出了一种用于非线性欠驱动自治水下机器人的鲁棒路径跟踪控制方法.利用Sugeno型模糊推理系统,将PD滑模控制器与非奇异终端滑模控制器光滑连接,构造了模糊混合控制器.它能充分融合这两类控制器的优势,无论系统远离平衡点还是在其附近,都能取得快速收敛的效果.如果,借助于非时间参考量,将该混合控制器用于自治水下机器人路径跟踪控制,将有利于提高它在不确定环境中的跟踪能力.最后,通过仿真计算结果验证了该控制策略的有效性.     

基于多传感器信息融合的目标跟踪与防撞决策

为了实现汽车主动安全系统中的目标跟踪与防撞,提出了混合式汽车防撞系统信息融合结构模型,采用分级信息融合实现目标跟踪,推导出了基于跟踪残留误差和预测残留误差共同校正的融合算法,并给出了算法的实现结构.在此基础之上,利用模糊积分方法融合多种相关信息,确定了汽车应采用的安全运行模式,实现了主动安全防撞决策.大量实验表明,该算法具有很好的稳定性和准确率.     

基于模糊控制器的移动机器人路径规划仿真

目前移动机器人系统已经被广泛研究,机器人的避碰控制策略也多种多样.主要借鉴模糊控制的思想来解决移动机器人路径规划中的避碰问题,实现机器人在障碍物环境中快速、准确的找到一条无碰撞的路径,最终达到目标点.首先介绍了模糊控制的理论基础,然后对路径规划算法进行了推导,在总结经验建立模糊规则的基础上,运用模糊推理,构造出一张实践效果较好的控制响应表,仿真结果表明了该算法应用于移动机器人路径规划具有正确性、实用性和智能性等,该方法计算量小,运算速度快,提高了机器人控制的速度.     

Comparison of fuzzy implication operators by means of fuzzy relational products used for intelligent local path-planning of AUVs

This paper describes the best choice to fuzzy implication operator and α-cut that are proper to the heuristic search technique for real-time collision avoidance of autonomous underwater vehicles (AUVs). A fuzzy implication operator is applied to the computation of fuzzy triangle product that constructs a new fuzzy relation between two fuzzy relations. An α-cut transforms a fuzzy relation into a crisp relation which is represented as a matrix. Those are the theoretical basis of heuristic search technique. In this paper, we review briefly our previous work—a heuristic search technique using fuzzy relational products for the collision avoidance system of AUVs, and propose the selection of a fuzzy implication operator and α-cut which are the most suitable for the search technique. In order to verify the optimality and the efficiency of the selected fuzzy implication operator and α-cut, we simulate every case of α-cut for each fuzzy implication operator in view of the cost of path and the number of α -cut generating acceptable path to the goal. This work was supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD) (KRF-2007-521-D00433).     

Effective Turning Motion Control of Internally Actuated Autonomous Underwater Vehicles

This paper presents a novel roll mechanism and an efficient control strategy for internally actuated autonomous underwater vehicles (AUVs). The developed control algorithms are tested on Michigan Tech’s custom research glider, ROUGHIE (Research Oriented Underwater Glider for Hands-on Investigative Engineering), in a controlled environment. The ROUGHIE’s design parameters and operational constraints were driven by its requirement to be man portable, expandable, and maneuverable in shallow water. As an underwater glider, the ROUGHIE is underactuated with direct control of only depth, pitch, and roll. A switching control method is implemented on the ROUGHIE to improve its maneuverability, enabling smooth transitions between different motion patterns. This approach uses multiple feedforward-feedback controllers. Different aspects of the roll mechanism and the effectiveness of the controller on turning motion are discussed based on experimental results. The results illustrate that the ROUGHIE is capable of achieving tight turns with a radius of 2.4 meters in less than 3 meters of water, or one order of magnitude improvement on existing internally actuated platforms. The developed roll mechanism is not specific to underwater gliders and is applicable to all AUVs, especially at lower speeds and in shallower water when external rudder is less effective in maneuvering the vehicle.     

Navigation of carlike robots in an extended dynamic environment with swarm avoidance

In this paper, we propose a new solution to the motion planning and control problem for a team of carlike mobile robots traversing in an extended dynamic environment. Motivated by the emerging necessity to avoid or defend against a swarm of autonomous robots, the wide array of obstacles in this dynamic environment for the first time includes a swarm of boids governed separately by a system of ordinary differential equations. The swarm exhibits collective emergent behaviors, whereas the carlike mobile robots safely navigate to designated targets. We present a set of nonlinear continuous controllers for obstacle, collision, and swarm avoidance. The controllers provide a collision‐free trajectory within a constrained workspace cluttered with various fixed and moving obstacles while satisfying the nonholonomic and kinodynamic constraints associated with the vehicular robotic system. An advantage of the proposed method is the ease in deriving the acceleration‐based control laws from the Lyapunov‐based control scheme. The effectiveness of the control laws is demonstrated via computer simulations. The novelty of this paper lies in the simplicity of the controllers and the ease in the treatment of an extended dynamic environment, which includes swarm avoidance.