基于多传感器信息融合的移动机器人导航系统

介绍了多传感器信息融合的基本原理,给出了基于多传感器信息融合的移动机器人导航系统结构。建立了移动机器人数学模型,运用基于扩展卡尔曼滤波的信息融合方法实现了移动机器人导航算法。通过实验验证了基于多传感器信息融合的移动机器人导航系统和导航算法的有效性。     

一种基于人机交互的移动机器人反应式导航方法

针对未知环境中移动机器人的自主导航问题,提出了一种基于人机交互的反应式导航方法。在采用模糊逻辑实现机器人基本智能行为的基础上,利用基于优先级和有限状态机的混合行为协调方法建立"环境刺激-反应"机制,提高机器人的局部自主能力。提出将"人刺激-反应"机制引入机器人系统,提高机器人系统对环境的理解与决策能力。在不同环境模型中利用提出的方法对移向指定目标的机器人自主导航进行了仿真,仿真结果验证了该方法的有效性。     

A 3-level autonomous mobile robot navigation system designed by using reasoning/search approaches

This paper describes how soft computing methodologies such as fuzzy logic, genetic algorithms and the Dempster–Shafer theory of evidence can be applied in a mobile robot navigation system. The navigation system that is considered has three navigation subsystems. The lower-level subsystem deals with the control of linear and angular volocities using a multivariable PI controller described with a full matrix. The position control of the mobile robot is at a medium level and is nonlinear. The nonlinear control design is implemented by a backstepping algorithm whose parameters are adjusted by a genetic algorithm. We propose a new extension of the controller mentioned, in order to rapidly decrease the control torques needed to achieve the desired position and orientation of the mobile robot. The high-level subsystem uses fuzzy logic and the Dempster–Shafer evidence theory to design a fusion of sensor data, map building, and path planning tasks. The fuzzy/evidence navigation based on the building of a local map, represented as an occupancy grid, with the time update is proven to be suitable for real-time applications. The path planning algorithm is based on a modified potential field method. In this algorithm, the fuzzy rules for selecting the relevant obstacles for robot motion are introduced. Also, suitable steps are taken to pull the robot out of the local minima. Particular attention is paid to detection of the robot’s trapped state and its avoidance. One of the main issues in this paper is to reduce the complexity of planning algorithms and minimize the cost of the search. The performance of the proposed system is investigated using a dynamic model of a mobile robot. Simulation results show a good quality of position tracking capabilities and obstacle avoidance behavior of the mobile robot.     

Path planning of wheeled mobile robot with simultaneous free space locating capability

This paper presents a hybrid path planning algorithm for the design of autonomous vehicles such as mobile robots. The hybrid planner is based on Potential Field method and Voronoi Diagram approach and is represented with the ability of concurrent navigation and map building. The system controller (Look-ahead Control) with the Potential Field method guarantees the robot generate a smooth and safe path to an expected position. The Voronoi Diagram approach is adopted for the purpose of helping the mobile robot to avoid being trapped by concave environment while exploring a route to a target. This approach allows the mobile robot to accomplish an autonomous navigation task with only an essential exploration between a start and goal position. Based on the existing topological map the mobile robot is able to construct sub-goals between predefined start and goal, and follows a smooth and safe trajectory in a flexible manner when stationary and moving obstacles co-exist.     

A Localization Algorithm for Autonomous Mobile Robots via a Fuzzy Tuned Extended Kalman Filter

The capability to acquire the position and orientation of an autonomous mobile robot is an important element for achieving specific tasks requiring autonomous exploration of the workplace. In this paper, we present a localization method that is based on a fuzzy tuned extended Kalman filter (FT-EKF) without a priori knowledge of the state noise model. The proposed algorithm is employed in a mobile robot equipped with 16 Polaroid sonar sensors and tested in a structured indoor environment. The state noise model is estimated and adapted by a fuzzy rule-based scheme. The proposed algorithm is compared with other EKF localization methods through simulations and experiments. The simulation and experimental studies demonstrate the improved performance of the proposed FT-EKF localization method over those using the conventional EKF algorithm.     

多传感器信息融合在移动机器人定位中的应用

机器人自定位是实现自主导航的关键问题之一。为了满足机器人在导航时精确定位的要求,提出一种基于多传感器信息融合的自定位算法。根据对机器人运动机构的分析和运动机构间的刚体约束,建立起机器人的运动学模型;由传感器的工作原理建立里程计和超声波传感器的观测模型;利用扩展卡尔曼滤波(EKF)算法将里程计和超声波传感器采集的数据进行融合;最后,由匹配的环境特征对机器人的位置进行修正,得到精确的位置估计。实验结果表明:该算法明显地消除了里程计的累计误差,有效地提高了定位精度。     

Path planning strategy for autonomous mobile robot navigation using Petri-GA optimisation

In this paper, a novel knowledge based genetic algorithm (GA) for path planning of multiple robots for multiple targets seeking behaviour in presence of obstacles is proposed. GA technique has been incorporated in Petri-Net model to make an integrated navigational controller. The proposed algorithm is based upon an iterative non-linear search, which utilises matches between observed geometry of the environment and a priori map of position locations, to estimate a suitable heading angle, there by correcting the position and orientation of the robots to find targets. This knowledge based GA is capable of finding an optimal or near optimal robot path in complex environments. The Petri-GA model can handle inter robot collision avoidance more effectively than the stand alone GA. The resulting navigation algorithm has been implemented on real mobile robots and tested in various environments to validate the developed control scheme.     

基于支持向量机和Q学习的移动机器人导航

基于神经网络的连续状态空间Q学习已应用在机器人导航领域。针对神经网络易陷入局部极小,提出了将支持向量机与Q学习相结合的移动机器人导航方法。首先以研制的CASIA-I移动机器人和它的工作环境为实验平台,确定出Q学习的回报函数;然后利用支持向量机对Q学习的状态——动作对的Q值进行在线估计,同时,为了提高估计速度,引入滚动时间窗机制;最后对所提方法进行了实验,实验结果表明所提方法能够使机器人无碰撞的到达目的地。     

Simultaneous localization and map building for mobile robotnavigation

Inexpensive ultrasonic sensors, incremental encoders, and grid-based probabilistic modeling are used for improved robot navigation in indoor environments. For model-building, range data from ultrasonic sensors are constantly sampled and a map is built and updated immediately while the robot is travelling through the workspace. The local world model is based on the concept of an occupancy grid. The world model extracted from the range data is based on the geometric primitive of line segments. For the extraction of these features, methods such as the Hough transform and clustering are utilized. The perceived local world model along with dead-reckoning and ultrasonic sensor data are combined using an extended Kalman filter in a localization scheme to estimate the current position and orientation of the mobile robot, which is subsequently fed to the map-building algorithm. Implementation issues and experimental results with the Nomad 150 mobile robot in a real-world indoor environment (office space) are presented     

An optimized modular neural network controller based on environment classification and selective sensor usage for mobile robot reactive navigation

A new approach to the design of a neural network (NN) based navigator is proposed in which the mobile robot travels to a pre-defined goal position safely and efficiently without any prior map of the environment. This navigator can be optimized for any user-defined objective function through the use of an evolutionary algorithm. The motivation of this research is to develop an efficient methodology for general goal-directed navigation in generic indoor environments as opposed to learning specialized primitive behaviors in a limited environment. To this end, a modular NN has been employed to achieve the necessary generalization capability across a variety of indoor environments. Herein, each NN module takes charge of navigating in a specialized local environment, which is the result of decomposing the whole path into a sequence of local paths through clustering of all the possible environments. We verify the efficacy of the proposed algorithm over a variety of both simulated and real unstructured indoor environments using our autonomous mobile robot platform.     

Real-time map building and navigation for autonomous robots inunknown environments

An algorithmic solution method is presented for the problem of autonomous robot motion in completely unknown environments. Our approach is based on the alternate execution of two fundamental processes: map building and navigation. In the former, range measures are collected through the robot exteroceptive sensors and processed in order to build a local representation of the surrounding area. This representation is then integrated in the global map so far reconstructed by filtering out insufficient or conflicting information. In the navigation phase, an A*-based planner generates a local path from the current robot position to the goal. Such a path is safe inside the explored area and provides a direction for further exploration. The robot follows the path up to the boundary of the explored area, terminating its motion if unexpected obstacles are encountered. The most peculiar aspects of our method are the use of fuzzy logic for the efficient building and modification of the environment map, and the iterative application of A*, a complete planning algorithm which takes full advantage of local information. Experimental results for a NOMAD 200 mobile robot show the real-time performance of the proposed method, both in static and moderately dynamic environments.     

Differential GPS and odometry-based outdoor navigation of a mobile robot

This paper describes outdoor navigation for a mobile robot by using differential GPS (DGPS) and odometry in a campus walkway environment. The robot position is estimated by fusion of DGPS and odometry. The GPS receiver measures its position by radio waves from GPS satellites. The error of GPS measurement data increases near high buildings and trees because of multi-path and forward diffractions. Thus, it is necessary to pick up only accurate DGPS measurement data when the robot position is modified by fusing DGPS and odometry. In this paper, typical DGPS measurement data observed near high buildings and trees are reported. Then, the authors propose a novel position correction method by fusing GPS and odometry. Fusion of DGPS and odometry is realized using an extended Kalman filter framework. Moreover, outdoor navigation for a mobile robot is accomplished by using the proposed correction method.     

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。     

基于粒子滤波的欠驱动移动机器人多目标点跟踪控制

针对欠驱动移动机器人的多目标点跟踪问题,提出了一种基于粒子滤波的高精度跟踪控制方法;具体地,在考虑移动机器人采样噪声的情况下,首先利用粒子滤波对移动机器人的位置信息进行处理,得到精准可靠的移动机器人状态信息;在此基础上,根据欠驱动移动机器人的运动学模型以及目标点的分布状况,设计基于反馈控制的多目标点跟踪控制方法;相对于传统的欠驱动移动机器人目标点跟踪控制算法,改进了该控制方法中增益参数的约束条件,有效避免了移动机器人在接近目标点时产生的奇异现象,有效提高了移动机器人对目标点的跟踪精度;此外,分析了该目标点跟踪控制系统的稳定性,并通过数值仿真验证了所提方法的可行性与有效性.     

Path planning in GPS-denied environments via collective intelligence of distributed sensor networks

This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown dynamic environments. A mobile sensor network is used for localising regions of interest for path planning of an autonomous mobile robot. The underlying theory is an extension of a generalised gossip algorithm that has been recently developed in a language-measure-theoretic setting. The algorithm has been used to propagate local decisions of target detection over a mobile sensor network and thus, it generates a belief map for the detected target over the network. In this setting, an autonomous mobile robot may communicate only with a few mobile sensing nodes in its own neighbourhood and localise itself relative to the communicating nodes with bounded uncertainties. The robot makes use of the knowledge based on the belief of the mobile sensors to generate a sequence of way-points, leading to a possible goal. The estimated way-points are used by a sampling-based motion planning algorithm to generate feasible trajectories for the robot. The proposed concept has been validated by numerical simulation on a mobile sensor network test-bed and a Dubin’s car-like robot.     

Reactive navigation in dynamic environment using a multisensorpredictor

A reactive navigation system for an autonomous mobile robot in unstructured dynamic environments is presented. The motion of moving obstacles is estimated for robot motion planning and obstacle avoidance. A multisensor-based obstacle predictor is utilized to obtain obstacle-motion information. Sensory data from a CCD camera and multiple ultrasonic range finders are combined to predict obstacle positions at the next sampling instant. A neural network, which is trained off-line, provides the desired prediction on-line in real time. The predicted obstacle configuration is employed by the proposed virtual force based navigation method to prevent collision with moving obstacles. Simulation results are presented to verify the effectiveness of the proposed navigation system in an environment with multiple mobile robots or moving objects. This system was implemented and tested on an experimental mobile robot at our laboratory. Navigation results in real environment are presented and analyzed.     

基于多传感器融合的移动机器人定位

针对移动机器人定位系统中单一传感器定位精度低与环境地图的重要性问题, 提出了一种基于多传感器融合的移动机器人定位方法. 首先, 在未知环境下, 分别利用单一里程计, 扩展卡尔曼滤波(extended Kalman filter,EKF)算法融合里程计、惯性测量单元(inertial measurement unit, ...     

全局环境未知时机器人导航和避障的一种新方法

研究了全局环境未知情况下的移动机器人实时导航问题．将栅格法描述环境与基于滚动窗口的路径规划相结合，提出了一种新的移动机器人导航方法．将超声传感阵列探测到的环境信息以基于栅格的概率值进行表示，利用不确定性证据推理对其进行数据融合，得到机器人的局部环境信息；在此基础上，采用基于滚动窗口的方法进行机器人路径规划，实现机器人的实时导航．仿真与实验结果表明了该方法的有效性．     

基于改进粒子群算法的移动机器人路径规划

为了提高复杂环境下移动机器人的精准导航作用,提出了移动机器人路径规划的改进粒子群优化(PSO)算法,即利用粒子个体极值的加权平均值,同时加入惯性权重.建立了移动机器人工作环境的栅格模型,利用Matlab软件进行移动机器人路径规划仿真分析.仿真结果表明:改进后的粒子群算法容易使粒子移动到最佳位置,加强了全局寻优能力,在复杂环境中搜索路径性能优于传统算法.