Autonomous navigation for high altitude satellites

The determination of the orbit of high altitude satellites with an accurate horizontal charge coupled device (CCD) sensor is considered using the extended Kalman filter. The measurement nonlinearity is removed by using a coordinate transform, and the corresponding steady state error is less than the steady state error in the Cartesian coordinate system. The performance of both of the navigational filters is evaluated for a reference geosynchronous orbit as a function of measurement error. The reduction of measurement uncertainty decreased steady state errors in position and velocity.     

Autonomous mobile robot navigation and learning

Research focused on the development and experimental validation of intelligent control techniques for autonomous mobile robots able to plan and perform a variety of assigned tasks in unstructured environments is presented. In particular, an autonomous mobile robot, HERMIES-IIB intelligence experiment series, is described. It is a self-powered, wheel-driven platform containing an onboard 16-node Ncube hypercube parallel processor interfaced to effectors and sensors through a VME-based system containing a Motorola 68020 processor, a phased sonar array, dual manipulator arms, and multiple cameras. Research on navigation and learning is examined     

Autonomous over-the-horizon navigation using LIDAR data

In this paper we present the approach for autonomous planetary exploration developed at the Canadian Space Agency. The goal of this work is to enable autonomous navigation to remote locations, well beyond the sensing horizon of the rover, with minimal interaction with a human operator. We employ LIDAR range sensors due to their accuracy, long range and robustness in the harsh lighting conditions of space. Irregular Triangular Meshes (ITMs) are used for representing the environment, providing an accurate, yet compact, spatial representation. In this paper a novel path-planning technique through the ITM is introduced, which guides the rover through flat terrain and safely away from obstacles. Experiments performed in CSA’s Mars emulation terrain, validating our approach, are also presented.     

Autonomous navigation system of near-Earth spacecraft

An autonomous navigation system for near-Earth spacecraft is described; this system allows determination of the satellite orbit and prediction of its motion parameters. Radio navigation measurements of GLONASS and GPS satellite systems are used for this purpose. The autonomous navigation system is designated for operation on near-Earth orbits which do not go beyond the navigation areas of GLONASS and/or GPS and on orbits with large eccentricity whose apocenter is at a distance of 50–70 thousand km from the Earth’s surface. The developed methods and algorithms for orbit determination are based on the application of laws of motion dynamics of a spacecraft directly at processing primary phase measurements of the carrier frequency and code pseudo-range using an extended measurement base. Algorithms for determination of motion parameters of the spacecraft and results of simulation and operation of a model system are presented. The possibility of creation of an onboard autonomous navigation system with precision and reliability higher than those of the ground measuring complex is demonstrated.     

Autonomous navigation and obstacle avoidance using navigation laws with time-varying deviation functions

In this paper we introduce a new family of navigation functions for robot navigation and obstacle avoidance. The method can be used for both path finding and real-time path planning. Each navigation function is composed of three parts: a proportionality term, a deviation function and a deviation constant. Deviation functions are time-varying functions satisfying certain conditions. These functions and parameters are updated in real-time to avoid collision with obstacles. Our strategy uses polar kinematics equations to model the navigation problem in terms of the range and direction between the robot and the goal. The obstacles are mapped to polar planes, and represented by the range and the direction from the robot or the final goal in polar coordinates. This representation gives a certain weight to the obstacles based on their relative position from the robot and facilitates the design of the navigation law. There exists an infinite number of navigation functions obtained by changing the proportionality constant, the deviation constant or the deviation function. This offers an infinite number of possibilities for the robot's path. Our navigation strategy is illustrated using an extensive simulation where different navigation parameters are used.     

Autonomous mobile robots navigation using RBF neural compensator

This paper presents an approach to adaptive trajectory tracking of mobile robots which combines a feedback linearization based on a nominal model and a RBF-NN adaptive dynamic compensation. For a robot with uncertain dynamic parameters, two controllers are implemented separately: a kinematics controller and an inverse dynamics controller. The uncertainty in the nominal dynamics model is compensated by a neural adaptive feedback controller. The resulting adaptive controller is efficient and robust in the sense that it succeeds to achieve a good tracking performance with a small computational effort. The analysis of the RBF-NN approximation error on the control errors is included. Finally, the performance of the control system is verified through experiments.     

Autonomous robot navigation in outdoor cluttered pedestrian walkways

This paper describes an implementation of a mobile robot system for autonomous navigation in outdoor concurred walkways. The task was to navigate through nonmodified pedestrian paths with people and bicycles passing by. The robot has multiple redundant sensors, which include wheel encoders, an inertial measurement unit, a differential global positioning system, and four laser scanner sensors. All the computation was done on a single laptop computer. A previously constructed map containing waypoints and landmarks for position correction is given to the robot. The robot system's perception, road extraction, and motion planning are detailed. The system was used and tested in a 1‐km autonomous robot navigation challenge held in the City of Tsukuba, Japan, named “Tsukuba Challenge 2007.” The proposed approach proved to be robust for outdoor navigation in cluttered and crowded walkways, first on campus paths and then running the challenge course multiple times between trials and the challenge final. The paper reports experimental results and overall performance of the system. Finally the lessons learned are discussed. The main contribution of this work is the report of a system integration approach for autonomous outdoor navigation and its evaluation. © 2009 Wiley Periodicals, Inc.     

Autonomous navigation system using Event Driven-Fuzzy Cognitive Maps

This study developed an autonomous navigation system using Fuzzy Cognitive Maps (FCM). Fuzzy Cognitive Map is a tool that can model qualitative knowledge in a structured way through concepts and causal relationships. Its mathematical representation is based on graph theory. A new variant of FCM, named Event Driven-Fuzzy Cognitive Maps (ED-FCM), is proposed to model decision tasks and/or make inferences in autonomous navigation. The FCM??s arcs are updated from the occurrence of special events as dynamic obstacle detection. As a result, the developed model is able to represent the robot??s dynamic behavior in presence of environment changes. This model skill is achieved by adapting the FCM relationships among concepts. A reinforcement learning algorithm is also used to finely adjust the robot behavior. Some simulation results are discussed highlighting the ability of the autonomous robot to navigate among obstacles (navigation at unknown environment). A fuzzy based navigation system is used as a reference to evaluate the proposed autonomous navigation system performance.     

Autonomous mobile robot proprioceptive navigation and predictive trajectory tracking

For an autonomous mobile robot a novel predictive control algorithm is presented, that enables both trajectory tracking and point stabilization. This algorithm is employed in conjunction with a proprioceptive navigation algorithm using either inertial or odometric data for posture estimation according to detected slip conditions. Based on the latter, slip control is made possible. Experimental results demonstrate the performance of the system.     

Marsokhod: Autonomous navigation tests on a Mars-like terrain

This paper describes the Russian rover Marsokhod, designed by Babakin Center for Mars exploration and the navigation sub-system based on stereovision developed by the French Space Agency C.N.E.S. to provide the rover with autonomous motion ability, improving thus its exploration range on the surface of Mars. Tests of the complete vehicle, including autonomous locomotion, has been recently fulfilled on a Mars-like area build in C.N.E.S. for this purpose by a joined Russian-French team; the main results and conclusions of these test are related.     

Autonomous navigation of an automated guided vehicle in industrial environments

The research presented in this paper approaches the issue of navigation using an automated guided vehicle (AGV) in industrial environments. The work describes the navigation system of a flexible AGV intended for operation in partially structured warehouses and with frequent changes in the floor plant layout. This is achieved by incorporating a high degree of on-board autonomy and by decreasing the amount of manual work required by the operator when establishing the a priori knowledge of the environment. The AGV's autonomy consists of the set of automatic tasks, such as planner, perception, path planning and path tracking, that the industrial vehicle must perform to accomplish the task required by the operator. The integration of these techniques has been tested in a real AGV working on an industrial warehouse environment.     

基于天文/GPS的HEO卫星自主导航方法

为了实现大椭圆轨道(HEO)卫星高精度自主导航,提出一种将直接敏感地平天文导航与全球定位系统(GPS)相结合的组合导航方法.首先,分析卫星轨道??2运动模型及其所受空间摄动,建立卫星轨道动力学模型;然后,分析单一使用天文导航和GPS的优缺点,根据HEO卫星对GPS的可见性,提出在远地点只采用天文导航,而在近地点采用以天文导航为主、适时引入GPS信号进行位速测量辅助修正的方法.通过计算机仿真和结果分析表明了所提出的设计方法导航精度比单一天文导航提高72.4%～85.6%.     

Autonomous navigation for mobile service robots in urban pedestrian environments

This paper presents a fully autonomous navigation solution for urban, pedestrian environments. The task at hand, undertaken within the context of the European project URUS, was to enable two urban service robots, based on Segway RMP200 platforms and using planar lasers as primary sensors, to navigate around a known, large (10,000 m²), pedestrian‐only environment with poor global positioning system coverage. Special consideration is given to the nature of our robots, highly mobile but two‐wheeled, self‐balancing, and inherently unstable. Our approach allows us to tackle locations with large variations in height, featuring ramps and staircases, thanks to a three‐dimensional, map‐based particle filter for localization and to surface traversability inference for low‐level navigation. This solution was tested in two different urban settings, the experimental zone devised for the project, a university campus, and a very crowded public avenue, both located in the city of Barcelona, Spain. Our results total more than 6 km of autonomous navigation, with a success rate on go‐to requests of nearly 99%. The paper presents our system, examines its overall performance, and discusses the lessons learned throughout development. © 2011 Wiley Periodicals, Inc.     

Autonomous robot navigation using optimal control of probabilistic regular languages

This paper addresses autonomous intelligent navigation of mobile robotic platforms based on the recently reported algorithms of language-measure-theoretic optimal control. Real-time sensor data and model-based information on the robot's motion dynamics are fused to construct a probabilistic finite state automaton model that dynamically computes a time-dependent discrete-event supervisory control policy. The paper also addresses detection and avoidance of livelocks that might occur during execution of the robot navigation algorithm. Performance and robustness of autonomous intelligent navigation under the proposed algorithm have been experimentally validated on Segway RMP robotic platforms in a laboratory environment.     

多传感器数据融合的智能电动车自主导航

以四轮驱动电动车为研究对象,将智能电动车上多种传感器所采集的信息进行处理与融合,实现校园环境里的自主行驶与导航.提出了在空旷路段和沿墙导航的控制决策,着重研究了沿墙算法和入弯算法,使智能电动车在不同的环境下可以有效地完成自主导航.实验结果表明:该导航控制策略和算法具有较高的可靠性.     

基于联邦UKF算法的移动机器人自主组合导航

组合导航技术是解决地面机器人自主导航的一个有效途径,其中GPS/DR是一种典型的组合方式。常用的卡尔曼滤波主要用于处理线性问题,针对该导航系统非线性的特点,对Unscented卡尔曼滤波（UKF）与分散式滤波技术相结合的方法进行了研究,建立了用于GPS/DR导航系统的联邦UKF算法。数值仿真实验表明,联邦UKF比联邦EKF有更好的滤波精度,同时有更高的稳定性和容错性,是一种理想的GPS/DR导航非线性滤波方法。     

基于轨迹引导的移动机器人导航策略优化算法

针对在杂乱、障碍物密集的复杂环境下移动机器人使用深度强化学习进行自主导航所面临的探索困难,进而导致学习效率低下的问题,提出了一种基于轨迹引导的导航策略优化（TGNPO）算法。首先,使用模仿学习的方法为移动机器人训练一个能够同时提供专家示范行为与导航轨迹预测功能的专家策略,旨在全面指导深度强化学习训练;其次,将专家策略预测的导航轨迹与当前时刻移动机器人所感知的实时图像进行融合,并结合坐标注意力机制提取对移动机器人未来导航起引导作用的特征区域,提高导航模型的学习性能;最后,使用专家策略预测的导航轨迹对移动机器人的策略轨迹进行约束,降低导航过程中的无效探索和错误决策。通过在仿真和物理平台上部署所提算法,实验结果表明,相较于现有的先进方法,所提算法在导航的学习效率和轨迹平滑方面取得了显著的优势。这充分证明了该算法能够高效、安全地执行机器人导航任务。     

无人机多源导航信息自主管理方法研究

完全依赖卫星导航信息融合的方案自主性差、完好性难以保证;而完全利用其它自主导航的方式,精度也难以保证,针对这些问题,提出了基于混合式架构的多源导航信息自主管理方法.利用联合故障检测方法,解决了对卫星干扰的有效检测,使完好性得到保证;设计了基于D–S证据推理的信息优选策略,针对典型无人机任务场景进行了仿真分析,结果表明信息优选策略降低了多故障检测方法同时应用的虚警率,提高了导航精度.     

火星探测器转移轨道的自主导航方法

本文针对转移轨道火星探测器对自主导航的迫切需求,提出了一种基于小行星和X射线脉冲星交互观测的自主导航方法,并结合工程任务对上述量测信息的获取方式和获取条件进行了研究,仿真结果表明,与单独基于小行星或单独基于X射线脉冲星观测的自主导航系统相比,该方法不仅可获得更高的导航精度,为转移轨道中途修正提供准确的轨道信息,且工程实现简单,是一种可行的转移轨道火星探测器自主导航方案,可为我国火星探测器自主导航系统的设计提供参考.     

基于情感与环境认知的移动机器人自主导航控制

将基于情感和认知的学习与决策模型引入到基于行为的移动机器人控制体系中, 设计了一种新的自主导航控制系统. 将动力学系统方法用于基本行为设计, 并利用ART2神经网络实现对连续的环境感知状态的分类, 将分类结果作为学习与决策算法中的环境认知状态. 通过在线情感和环境认知学习, 形成合理的行为协调机制. 仿真表明, 情感和环境认知能明显地改善学习和决策过程效率, 提高基于行为的移动机器人在未知环境中的自主导航能力