海流环境下多AUV多目标生物启发任务分配与路径规划算法

针对多障碍物海流环境下多自治水下机器人(AUV)目标任务分配与路径规划问题, 本文在栅格地图构建的 基础上给出了一种基于生物启发神经网络(BINN)模型的新型自主任务分配与路径规划算法, 并考虑海流对路径规 划的影响. 首先建立BINN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; 接着, 比较每个目标物在BINN地图中所有AUV的活性值, 并选取活性值最大的AUV作为它的获 胜AUV, 实现多AUV任务分配; 最后, 考虑常值海流影响, 根据矢量合成算法确定AUV实际的航行方向, 实现AUV路 径规划与安全避障. 海流环境下仿真实验结果表明了生物启发模型在多AUV水下任务分配与路径规划中的有效性.     

基于运动微分约束的无人车辆纵横向协同规划算法的研究

为了满足在动态环境中快速行驶的要求,现有无人车辆普遍采用在传统规划系统的两层结构（路径规划-路径跟踪）之间增加局部规划的方法,通过在路径跟踪的同时进行避障来减少耗时的全局路径重规划. 本文针对这种三层结构规划系统存在的问题,提出基于运动微分约束的纵横向协同规划算法,在真实环境中实现速度不超过40km/h的无人驾驶. 根据车辆的实时运动状态,用高阶多项式模型在预瞄距离内对可行驶曲线进行建模,不仅使行驶过程中的转向平稳,而且在较高速时仍具有良好的路径跟踪能力. 由横向规划提供横向安全性的同时,在动力学约束的速度容许空间中进行纵向规划,实现平顺的加速与制动,并保证了纵向安全性和侧向稳定性. 该算法根据实时的局部环境自动决定纵横向期望运动参数,不需要人为设定行驶模式或调整参数. 采用该算法的无人驾驶平台在2011年和2012年智能车未来挑战赛的真实交通环境中,用统一的程序框架顺利完成全程的无人驾驶.     

Embedded visual behaviors for navigation

The implementation of a set of visually based behaviors for navigation is presented. The approach, which has been inspired by insect's behaviors, is aimed at building a “library” of embedded visually guided behaviors coping with the most common situations encountered during navigation in an indoor environment. Following this approach, the main goal is no longer how to characterize the environment, but how to embed in each behavior the perceptual processes necessary to understand the aspects of the environment required to generate a purposeful motor output.

The approach relies on the purposive definition of the task to be solved by each of the behaviors and it is based on the possibility of computing visual information during the action. All the implemented behaviors share the same input process (partial information of the image flow field) and the same control variables (heading direction and velocity) to demonstrate both the generality of the approach as well as its efficient use of the computational resources. The controlled mobile base is supposed to move on a flat surface but virtually no calibration is required of the intrinsic and extrinsic parameters of the two cameras and no attempt is made at building a 2D or 3D map of the environment: the only output of the perceptual processes is a motor command.

The first behavior, the centering reflex allows a robot to be easily controlled to navigate along corridors or following walls of a given scene structure. The second behavior extends the system capabilities to the detection of obstacles lying on the pavement in front of the mobile robot. Finally docking behaviors to control the robot to a given position in the environment, with controlled speed and orientation, are presented.

Besides the long-term goal of building a completely autonomous system, these behaviors can have very short-term applications in the area of semi-autonomous systems by taking care of the continuous, tedious control required during routine navigation.     

基于模糊神经网络的六足机器人自主导航闭环控制系统设计

针对未知环境中六足机器人的自主导航问题,设计了一种基于模糊神经网络的自主导航闭环控制算法,并依据该算法设计了六足机器人的导航控制系统．算法融合了模糊控制的逻辑推理能力与神经网络的学习训练能力,并引入闭环控制方法对算法进行优化．所设计的控制系统由信息输入、模糊神经网络、指令执行以及信息反馈4个模块组成．环境及位置信息的感知由GPS（全球定位系统）传感器、电子罗盘传感器和超声波传感器共同完成．采用C语言重建模糊神经网络控制算法,并应用于该系统．通过仿真实验,从理论上论证了基于模糊神经网络的闭环控制算法性能优于开环控制算法,闭环控制算法能够减小六足机器人在遇到障碍物时所绕行的距离,行进速度提高了6.14%,行进时间缩短了8.74%．在此基础上,开展了实物试验．试验结果表明,该控制系统能够实现六足机器人自主导航避障控制功能,相对于开环控制系统,能有效地缩短行进路径,行进速度提高了5.66%,行进时间缩短了7.25%,验证了闭环控制系统的可行性和实用性．     

An Efficient Path Planning and Control Algorithm for RUAV’s in Unknown and Cluttered Environments

This paper presents an efficient planning and execution algorithm for the navigation of an autonomous rotary wing UAV (RUAV) manoeuvering in an unknown and cluttered environment. A Rapidly-exploring Random Tree (RRT) variant is used for the generation of a collision free path and linear Model Predictive Control(MPC) is applied to follow this path. The guidance errors are mapped to the states of the linear MPC structure by using the nonlinear kinematic equations. The proposed path planning algorithm considers the run time of the planning stage explicitly and generates a continuous curvature path whenever replanning occurs. Simulation results show that the RUAV with the proposed methodology successfully achieves autonomous navigation regardless of its lack of prior information about the environment.     

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.     

基于ROS的无人配送车导航系统的设计与实现

针对无人配送车在自主导航过程中存在的寻路效率低、避障能力弱、转折幅度过大等问题,该文采用搭载机器人操作系统(ROS)的Turtlebot3机器人作为无人配送车,设计并实现了高效稳定的无人配送车自主导航系统。ROS是专门用于编写机器人软件的灵活框架,对其集成的SLAM算法进行改进,以完成无人配送车在封闭园区环境中的即时定位与地图构建,同时对ROS导航功能包集成的路径规划算法进行改进,使无人配送车在已知环境地图中规划生成出适合无人配送车工作的路径和有效避开障碍物。最后在Gazebo仿真环境中对无人配送车自主导航系统进行测试与验证。仿真试验结果表明,设计实现的无人配送车导航系统能够很好地满足无人配送车在封闭园区中的自主导航功能。     

移动机器人行为参数的PSO多目标优化

根据移动机器人的导航任务,提出基于粒子群优化(PSO)算法的行为参数多目标分层优化方法。将导航方向与导航速度相关的参数按优先级进行PSO算法分层选取,使机器人在路径近似最优的基础上实现导航时间最少。仿真结果表明,该方法可以提高导航效率,实现导航决策的逐步求精,从而改善机器人在未知环境下的自主导航性能。     

Dynamic Task Assignment and Path Planning for Multi-AUV System in Variable Ocean Current Environment

An integrated multiple autonomous underwater vehicle (multi-AUV) dynamic task assignment and path planning algorithm is proposed by combing the improved self-organizing map (SOM) neural network and a novel velocity synthesis approach. Each target is to be visited by one and only one AUV, and a shortest path between a starting point and the destination is found in the presence of the variable current environment and dynamic targets. Firstly, the SOM neuron network is developed to assign a team of AUVs to achieve multiple target locations in dynamic ocean environment. The working process involves special definition of the rule to select the winner, the computation of the neighborhood function, and the method to update weights. Then, the velocity synthesis approach is applied to plan a shortest path for each AUV to visit the corresponding target in dynamic environment subject to the ocean current being variable and targets being movable. Lastly, to demonstrate the effectiveness of the proposed approach, simulation results are given in this paper.     

Control architecture for mobile robot operation and navigation

This paper presents the navigation and operation system (NOS) for a multipurpose industrial autonomous mobile robot for both indoor and outdoor environments. This architecture supports task specification in terms of an event-driven state-based machine that provides high quality mission performance in uncertain environments. All processes in the NOS have been integrated in a distributed architecture designed to consider the real-time constraints of each control level of the system. Particular task models obtained from the system requirements specifications are integrated at the highest level of the architecture so that the rest of the levels remain unchanged for a wide range of industrial applications, such as transportation and operation with onboard devices.     

CBUG: A Quadratically Competitive Mobile Robot Navigation Algorithm

This paper is concerned with online navigation of a size $D$ mobile robot in an unknown planar environment. A formal means for assessing algorithms for online tasks is competitiveness. For the navigation task, competitiveness measures the algorithm's path length relative to the optimal offline path length. While competitiveness usually means constant relative performance, it is measured in this paper in terms of a quadratic relationship between online performance and optimal offline solution. An online navigation algorithm for a size D robot called CBUG is described. The competitiveness of CBUG is analyzed and shown to be quadratic in the length of the shortest offline path. Moreover, it is shown that, in general, quadratic competitiveness is the best achievable performance over all online navigation algorithms. Thus, up to constants, CBUG achieves optimal competitiveness. The algorithm is improved with some practical speedups, and the usefulness of its competitiveness in terms of path stability is illustrated in office-like environments.      

A Gaussian process-based RRT planner for the exploration of an unknown and cluttered environment with a UAV

A new framework which adopts a rapidly-exploring random tree (RRT) path planner with a Gaussian process (GP) occupancy map is developed for the navigation and exploration of an unknown but cluttered environment. The GP map outputs the probability of occupancy given any selected query point in the continuous space and thus makes it possible to explore the full space when used in conjunction with a continuous path planner. Furthermore, the GP map-generated path is embedded with the probability of collision along the path which lends itself to obstacle avoidance. Finally, the GP map-building algorithm is extended to include an exploration mission considering the differential constraints of a rotary unmanned aerial vehicle and the limitation arising from the environment. Using mutual information as an information-theoretic measure, an informative path which reduces the uncertainty of the environment is generated. Simulation results show that GP map combined with RRT planner can achieve the 3D navigation and exploration task successfully in unknown and complex environments.     

Evolutionary algorithm based offline/online path planner for UAV navigation

An evolutionary algorithm based framework, a combination of modified breeder genetic algorithms incorporating characteristics of classic genetic algorithms, is utilized to design an offline/online path planner for unmanned aerial vehicles (UAVs) autonomous navigation. The path planner calculates a curved path line with desired characteristics in a three-dimensional (3-D) rough terrain environment, represented using B-spline curves, with the coordinates of its control points being the evolutionary algorithm artificial chromosome genes. Given a 3-D rough environment and assuming flight envelope restrictions, two problems are solved: i) UAV navigation using an offline planner in a known environment, and, ii) UAV navigation using an online planner in a completely unknown environment. The offline planner produces a single B-Spline curve that connects the starting and target points with a predefined initial direction. The online planner, based on the offline one, is given on-board radar readings which gradually produces a smooth 3-D trajectory aiming at reaching a predetermined target in an unknown environment; the produced trajectory consists of smaller B-spline curves smoothly connected with each other. Both planners have been tested under different scenarios, and they have been proven effective in guiding an UAV to its final destination, providing near-optimal curved paths quickly and efficiently.     

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.     

QoS路由中一种有效的多受限优化路径选择算法

1 引言目前,人们正期待着宽带集成服务网来支持各种各样的满足不同QoS要求的多媒体应用。在宽带体系结构的设计中的一个关键问题就是如何提供资源来满足每次连接的需求。毫无疑问,有效的QoS路由方案的建立是这种体系结构中的一个重要组成部分。的确,QoS路由已经成为许多研究的主题。人们已经认识到一个有效的QoS路由方案的建立给我们带来了许多挑战。在算法上,QoS路由带来的一个的挑战就是需要迅速地找到一条可行路径使它满足一组限制条件同时获得较高的网络资源利用率。一般而言,QoS路由是一个复杂的问题。首先,网络电话和分布式游戏等分布式应用在延迟、延迟抖动、丢失率和带宽等方面有许多不同的QoS限制。多个限制经常使得路由问题更加复杂。例如,寻找一条具有两个独立路径限制的可行路径是NP难的。其次,将来的集成服务网很可能既要传输QoS数据流又要传输尽力而为的数据,     

Metric embedding of view-graphs

Most recent robotic systems, capable of exploring unknown environments, use topological structures (graphs) as a spatial representation. Localization can be done by deriving an estimate of the global pose from landmark information. In this case navigation is tightly coupled to metric knowledge, and hence the derived control method is mainly pose-based. Alternative to continuous metric localization, it is also possible to base localization methods on weaker constraints, e.g. the similarity between images capturing the appearance of places or landmarks. In this case navigation can be controlled by a homing algorithm. Similarity based localization can be scaled to continuous metric localization by adding additional constraints, such as alignment of depth estimates. We present a method to scale a similarity based navigation system (the view-graph-model) to continuous metric localization. Instead of changing the landmark model, we embed the graph into the three dimensional pose space. Therefore, recalibration of the path integrator is only possible at discrete locations in the environment. The navigation behavior of the robot is controlled by a homing algorithm which combines three local navigation capabilities, obstacle avoidance, path integration, and scene based homing. This homing scheme allows automated adaptation to the environment. It is further used to compensate for path integration errors, and therefore allows to derive globally consistent pose estimates based on “weak” metric knowledge, i.e. knowledge solely derived from odometry. The system performance is tested with a robotic setup and with a simulated agent which explores a large, open, and cluttered environment. This work is part of the GNOSYS (FP6-003835-GNOSYS) project, supported by the European Commission.     

多方向无人水面艇路径规划算法

针对海洋环境下无人水面艇路径（USV）规划安全性与平滑性问题,提出一种多方向A^*路径规划算法以获得全局最优路径。首先,结合电子海图生成栅格化环境信息,并根据安全航行距离约束建立USV安全区域模型,在传统A^*算法基础上设计一种带安全距离约束的A^*启发函数来保证生成的路径节点的安全;其次,改进传统A^*算法的八方向搜索模式,提出一种多方向搜索模式来调整生成路径中的冗余点与拐点;最后,采用路径平滑算法对路径拐点进行平滑处理以获得满足实际航行要求的连续平滑路径。在仿真实验中,改进A^*算法规划的路径距离为7 043 m,相较于Dijkstra算法、传统A^*四方向搜索算法和传统A^*八方向搜索算法分别降低了9.7%、26.6%和7.9%。仿真结果表明改进后的多方向A^*搜索算法能够有效减小路径距离,更适用于USV路径规划问题。     

Efficient autonomous navigation for planetary rovers with limited resources

Rovers operating on Mars require more and more autonomous features to fulfill their challenging mission requirements. However, the inherent constraints of space systems render the implementation of complex algorithms an expensive and difficult task. In this paper, we propose an architecture for autonomous navigation. Efficient implementations of autonomous features are built on top of the ExoMars path following navigation approach to enhance the safety and traversing capabilities of the rover. These features allow the rover to detect and avoid hazards and perform significantly longer traverses planned by operators on the ground. The efficient navigation approach has been implemented and tested during field test campaigns on a planetary analogue terrain. The experiments evaluated the proposed architecture by autonomously completing several traverses of variable lengths while avoiding hazards. The approach relies only on the optical Localization Cameras stereo bench, a sensor that is found in all current rovers, and potentially allows for computationally inexpensive long‐range autonomous navigation in terrains of medium difficulty.     

Routing with multiple quality-of-services constraints: An approximation perspective

Finding a path that satisfies multiple Quality-of-Service (QoS) constraints is vital to the deployment of current emerged services. However, existing algorithms are not very efficient and effective at finding such a path. Moreover, few works focus on three or more QoS constraints. In this paper, we present an enhanced version of fully polynomial time approximation scheme (EFPTAS) for multiconstrainted path optimal (MCOP) problem. Specifically, we make four major contributions. We first allow the proposed algorithm to construct an auxiliary graph, through which the QoS parameters on each of the finding path can be guaranteed not to exceed the given constraints. Then we adopt a concept, called nonlinear definition of path constraints in EFPTAS for reducing both time and space complexity. Also, we enable EFPTAS to run iteratively to facilitate a progressive refinement of the finding result. In addition to these, we identify some “deployment” issues for proposed algorithm, the essential steps that how and when the EFPTAS takes place are presented. By analyzing the proposed algorithm theoretically, we find that the presented EFPTAS can find a (1+ε)-approximation path in the network with time complexity O(|E||V|/ε) (where |E| is the number of edges and |V| is the number of nodes), which outperforms the previous best-known algorithm for MCOP. We conduct an extensive comparison between the algorithm presented in this paper and previous best-known study experimentally, our results indicate that EFPTAS can find a path with low complexity and preferable quality.