基于改进狮群进化算法的面向空间众包平台的多工作者多任务路径规划方法

针对面向空间众包平台的多工作者多任务路径规划问题,以求解时间成本和路程成本最小的全局最优路径规划方案为目标,提出了基于改进狮群进化算法的路径规划方法.首先,结合现实问题场景,提出带有任务开始点和结束点的路径规划模型;其次,借鉴狮群进化算法的思想,改进狮群智能行为,引入驱逐行为,针对求解问题设计染色体编码方式、交叉、变异操作等,提出了面向空间众包平台的多工作者多任务路径规划的改进狮群进化算法;最后,运用改进狮群进化算法求解面向空间众包平台的多工作者多任务路径规划模型,并根据真实数据集制作问题算例进行测试.实验结果表明了算法的可用性和有效性.     

基于组件的电子地图显示软件设计

设计了基于组件的电子地图显示软件,实现了电子地图基本操作功能及路径规划功能。可以在软件中实现地图放大、缩小、漫游、测距、图层控制、鹰眼视图、全图显示、坐标显示等功能,作为电子导航显示软件,利用DOkstra算法可以在地图中求解任意两点之间最短距离,利用蚁群算法对道路进行了路径规划,在有结点约束的条件下求解一条较优路径。因蚁群算法求解路径规划问题存在求解速度慢问题,利用Cilk＋＋并行模型对蚁群算法进行了并行化。     

考虑最优路径的水面清漂船自主导航算法设计

针对当前水面清漂船自主导航算法,未考虑构建水面环境模型,导致清漂船自主导航路径规划长度较长、规划效率较低、转折角度较大的问题,提出考虑最优路径的水面清漂船自主导航算法.通过分析水面环境信息,构建水面环境模型,形成清除点路径连通网络,编码清除点连接顺序,求解清漂船作业路径规划的最小化目标函数.利用蚁群算法,计算清漂船在下...     

改进动态规划算法的移动机器人路径规划

针对静态栅格环境下的移动机器人全局路径规划问题,通过分析移动机器人到达目标的搜索方向和路径变化的动态特征,分别建立下降路径搜索动态规划模型和上升路径搜索动态规划模型,并依据整列元素路径值变化特点设计了两种模型交互使用的改进动态规划算法。仿真实验结果表明算法具有较好的路径规划效率,可以同时完成多个目标路径规划,且覆盖率越大的环境求解越快速。实验也表明改进动态规划算法同蚁群算法对比能够更快速有效地给出移动机器人较优通行路径。     

罚函数凸优化迭代算法及在无人机路径规划中的应用

针对无人机路径规划问题,建立了具有定常非线性系统、非仿射等式约束、非凸不等式约束的非凸控制问题模型,并对该模型进行了算法设计和求解。基于迭代寻优的求解思路,提出了凸优化迭代求解方法和罚函数优化策略。前者利用凹凸过程(CCCP)和泰勒公式对模型进行凸化处理,后者将经处理项作为惩罚项施加到目标函数中以解决初始点可行性限制。经证明该方法严格收敛到原问题的Karush-Kuhn-Tucker(KKT)点。仿真实验验证了罚函数凸优化迭代算法的可行性和优越性,表明该算法能够为无人机规划出一条满足条件的飞行路径。     

基于蚁群算法求解带硬时间窗的VRPSDP

建立了描述带硬时间窗的同时送取货的车辆路径问题(VRPSDPTW)的混合整数规划模型,给出了求解该模型的基于蚁群算法的改进的启发式算法。最后,通过实例计算,验证了算法的可行性和有效性,结果表明改进的蚁群算法在求解小规模问题（20个客户点）时,其性能总体优于已有的同类问题算法。     

基于方向约束的A*算法

实际机器人路径规划问题经常需要考虑路径的转弯约束以及路径起始/目标角要求,为此提出一种基于方向约束的A*算法.新算法区分同一路径点处不同方向的各条路径,通过定向扩展机制来满足路径方向约束,并采用节点合并策略和不一致队列降低算法复杂度.理论分析和典型地图集上的实验结果证明,所提算法总是能够保证给出符合转弯约束和起始/目标角约束的最短路径,且相比于现有算法,能够有效提高方向约束路径规划问题的求解能力.     

基于离散灰狼算法的喷涂机器人路径规划方法

针对目前用于复杂结构实体喷涂的机器人路径规划方法存在的效率低、未考虑碰撞以及适用性差等问题,提出一种用于求解多层决策问题的离散灰狼算法,并把该算法用于该路径规划问题的求解。为了将连续域灰狼算法改为用于求解多层决策问题的离散灰狼算法,采用矩阵编码方法解决多层决策问题的编码问题,提出基于先验知识与随机选择的混合初始化方法提高算法求解效率和精度,运用交叉算子与两级变异算子定义离散域灰狼算法的种群更新策略。另外,运用图论将喷涂机器人路径规划问题简化为广义旅行商问题,并建立了该问题的最短路径模型和路径碰撞模型。在路径规划实验中,相较于粒子群算法、遗传算法和蚁群算法,提出的算法规划的平均路径长度分别减小了5.0%、5.5%和6.6%,碰撞次数降低为0,且路径更平滑。实验结果表明,提出的算法能够有效提高喷涂机器人的喷涂效率,以及喷涂路径的安全性和适用性。     

基于鱼群算法的智能机器人全覆盖路径规划

为保证机器人的行驶轨迹可以全方位地的覆盖地图的全部坐标点,并降低路径重复率,基于鱼群算法设计智能机器人全覆盖路径规划方法。建立智能机器人死区脱困模型,计算栅格地图模型中的目标活性值,获取整体栅格数量,描述地图中栅格状态,得到脱困时的行驶角度差。基于鱼群算法设计全路径覆盖判定方法,描述不同目标鱼个体之间的距离,在三重移动目标坐标系下,获取元素坐标向量,建立每个目标点的求解代价和,计算下一个目标点行驶的最小距离。设计机器人全覆盖路径规划算法,判断当前位置是否为死区,获取路径规划的全局最优解,实现智能机器人的全覆盖路径规划。利用Matlab仿真软件完成智能机器人全覆盖路径规划实验。结果表明,在简单环境下,该路径规划方法覆盖率为100%,重复率为5.23%,路径长度为15.36m；在复杂环境下,该路径规划方法的覆盖率为100%,重复率则为10.24%,路径长度为20.34m。由此证明,该方法具有较好地规划效果较好。     

基于生物地理学优化的多UCAV协同航迹规划

研究多无人机协同路径规划问题,为了获取从起始点到达目标点,能够避开各种阻碍的最优运动路径,提出了一种基于BBO的多UCAV协同航迹规划方法.通过对地形环境、航迹表示方式进行描述,将生物地理学优化算法运用于多UCAV协同航迹规划,对约束条件及威胁进行分析,建立了UCAV航迹规划模型及多UCAV协同航迹规划模型;对BBO算法适宜度向量编码及迁徙模型进行了分析,设计了相应的优化算子;最后,构建了多UCAV协同航迹规划的求解框架,并结合BBO算法开展了相应的仿真.仿真结果表明,改进方法较好地实现了多UCAV协同航迹规划的优化.     

Robot selection expert ‘Rose’

One of the major problems facing the robot user in the future will be his choice of the optimum robot for a particular task. What is needed is a highly automated robot selection system which will eliminate the human decision-making process. The system presented will be used when a robot is being considered to replace a human at a particular task, while the rest of the workplace remains fixed. The purpose of this paper is twofold; firstly, to demonstrate the knowledge required in making an optimum robot selection, and secondly, to provide a tutoral in designing an expert system using EXPERT. The paper will provide (1) the data base, (2) rules for transforming that data base, and (3) the control strategy that is necessary in implementing an expert system to perform the aforementioned task. The system will query the user as to the characteristics of the desired robot and the expert system will choose an optimum robot from the choices in the data base. The user will construct the environment in which the robot will be working by using 3-D modeling techniques. The user will choose from a menu and place the various objects which the robot will have to conform to. Thus, constraints such as maximum space available, can be stripped out of the 3-D drawing rather than having the expert system query the user for dimensions. One very good feature of such a system is that as new robots are developed their specifications can be added to the data base very easily.     

Self-calibration of environmental camera for mobile robot navigation

An environmental camera is a camera embedded in a working environment to provide vision guidance to a mobile robot. In the setup of such robot systems, the relative position and orientation between the mobile robot and the environmental camera are parameters that must unavoidably be calibrated. Traditionally, because the configuration of the robot system is task-driven, these kinds of external parameters of the camera are measured separately and should be measured each time a task is to be performed. In this paper, a method is proposed for the robot system in which calibration of the environmental camera is rendered by the robot system itself on the spot after a system is set up. Specific kinds of motion patterns of the mobile robot, which are called test motions, have been explored for calibration. The calibration approach is based upon executing certain selected test motions on the mobile robot and then using the camera to observe the robot. According to a comparison of odometry and sensing data, the external parameters of the camera can be calibrated. Furthermore, an evaluation index (virtual sensing error) has been developed for the selection and optimization of test motions to obtain good calibration performance. All the test motion patterns are computed offline in advance and saved in a database, which greatly shorten the calibration time. Simulations and experiments verified the effectiveness of the proposed method.     

ROBOT_S: An interactive robot simulation language

While it may not be practical to realize a tentative robot design as an actual robot, there is no question of the practicality of a simulation, ROBOT_S is a program in which the foundation for a comprehensive simulation environment is laid. A graphical robot is created to which physical attributes may be assigned, and whose movement may be dictated by a user-installed dynamic model and control law. A simple robot command language has been developed, by which the manipulator may be commanded to move, during which simulation data of state variables is collected and graphed.     

Recognition and movement in an artificial environment with a bipedal robot

However well we control a walking bipedal robot, the images obtained by a camera are tilted to the left or right, and have small irregularities. This complicates the recognition of an environment by using a camera in a walking robot when the robot cannot move smoothly. The reason for using a bipedal robot is to make the robot as similar as possible to a human in body shape and behavior in order to make collaboration easier. This is difficult to attain with other types of robot such as wheel-driven robots (Sato et al. AROB2008; Fujiwara et al. WMSCI2009). In an artificial environment which mainly consists of vertical and horizontal lines, the tilt angle of camera images must be corrected by using the Hough transformation, which detects lines which are nearly vertical (Okutomi et al. 2004; Forsyth and Ponce 2007). As a result, the robot can successfully recognize the environment with stereo vision using images obtained by correcting the tilted ones.     

Distance-Optimal Navigation in an Unknown Environment Without Sensing Distances

This paper considers what can be accomplished using a mobile robot that has limited sensing. For navigation and mapping, the robot has only one sensor, which tracks the directions of depth discontinuities. There are no coordinates, and the robot is given a motion primitive that allows it to move toward discontinuities. The robot is incapable of performing localization or measuring any distances or angles. Nevertheless, when dropped into an unknown planar environment, the robot builds a data structure, called the gap navigation tree, which enables it to navigate optimally in terms of Euclidean distance traveled. In a sense, the robot is able to learn the critical information contained in the classical shortest-path roadmap, although surprisingly it is unable to extract metric information. We prove these results for the case of a point robot placed into a simply connected, piecewise-analytic planar environment. The case of multiply connected environments is also addressed, in which it is shown that further sensing assumptions are needed. Due to the limited sensor given to the robot, globally optimal navigation is impossible; however, our approach achieves locally optimal (within a homotopy class) navigation, which is the best that is theoretically possible under this robot model.     

Behavioral task processing for cognitive robots using artificial emotions

This paper presents an artificial emotional-cognitive system-based autonomous robot control architecture for a four-wheel driven and four-wheel steered mobile robot. Discrete stochastic state-space mathematical model is considered for behavioral and emotional transition processes of the autonomous mobile robot in the dynamic realistic environment. The term of cognitive mechanism system which is composed from rule base and reinforcement self-learning algorithm explain all of the deliberative events such as learning, reasoning and memory (rule spaces) of the autonomous mobile robot. The artificial cognitive model of autonomous robot control architecture has a dynamic associative memory including behavioral transition rules which are able to be learned for achieving multi-objective robot tasks. Motivation module of architecture has been considered as behavioral gain effect generator for achieving multi-objective robot tasks. According to emotional and behavioral state transition probabilities, artificial emotions determine sequences of behaviors for long-term action planning. Also reinforcement self-learning and reasoning ability of artificial cognitive model and motivational gain effects of proposed architecture can be observed on the executing behavioral sequences during simulation. The posture and speed of the robot and the configurations, speeds and torques of the wheels and all deliberative and cognitive events can be observed from the simulation plant and virtual reality viewer. This study constitutes basis for the multi-goal robot tasks and artificial emotions and cognitive mechanism-based behavior generation experiments on a real mobile robot.     

Visual cooperation based on LOS for self-organization of swarm robots

In this study, an attempt has been made to incorporate visual cooperation among decentralized swarm robots for self-organization. Self-organization based on color recognition is presented to overcome the constraints faced in conventional self-organization based on centralized control, in which an external ceiling camera or beacon systems are used. In the proposed scheme, a singular association rule is introduced: a swarm robot considers line-of-sight (LOS) visual information only about its reference robot or a moving target. In particular, this paper presents the following set of cases pertaining to self-organization of swarm robots: 1) a case in which a robot loses a moving target from its LOS, 2) a case in which a robot loses a reference robot from its LOS, 3) a case in which a robot is lost, and 4) a case in which all robots lose the target from their LOS. Results of a simulation and an experiment on prey pursuit show that the proposed self-organization method can be effectively used for multiple mobile robots, despite the use of a simple association rule.     

Cooperative localization for disconnected sensor networks and a mobile robot in friendly environments

Localization for a disconnected sensor network is highly unlikely to be achieved by its own sensor nodes, since accessibility of the information between any pair of sensor nodes cannot be guaranteed. In this paper, a mobile robot (or a mobile sensor node) is introduced to establish correlations among sparsely distributed sensor nodes which are disconnected, even isolated. The robot and the sensor network operate in a friendly manner, in which they can cooperate to perceive each other for achieving more accurate localization, rather than trying to avoid being detected by each other. The mobility of the robot allows for the stationary and internally disconnected sensor nodes to be dynamically connected and correlated. On one hand, the robot performs simultaneous localization and mapping (SLAM) based on the constrained local submap filter (CLSF). The robot creates a local submap composed of the sensor nodes present in its immediate vicinity. The locations of these nodes and the pose (position and orientation angle) of the robot are estimated within the local submap. On the other hand, the sensor nodes in the submap estimate the pose of the robot. A parallax-based robot pose estimation and tracking (PROPET) algorithm, which uses the relationship between two successive measurements of the robot's range and bearing, is proposed to continuously track the robot's pose with each sensor node. Then, tracking results of the robot's pose from different sensor nodes are fused by the Kalman filter (KF). The multi-node fusion result are further integrated with the robot's SLAM result within the local submap to achieve more accurate localization for the robot and the sensor nodes. Finally, the submap is projected and fused into the global map by the CLSF to generate localization results represented in the global frame of reference. Simulation and experimental results are presented to show the performances of the proposed method for robot-sensor network cooperative localization. Especially, if the robot (or the mobile sensor node) has the same sensing ability as the stationary sensor nodes, the localization accuracy can be significantly enhanced using the proposed method.     

A real-time data acquisition and processing framework for large-scale robot skin

Large-scale tactile sensing applications in Robotics have become the focus of extensive research activities in the past few years, specifically for humanoid platforms. Research products include a variety of fundamentally different robot skin systems. Differences rely in technological (e.g., sensory modes and networking), system-level (e.g., modularity and scalability) and representation (e.g., data structures, coherency and access efficiency) aspects. However, differences within the same robot platform may be present as well. Different robot body parts (e.g., fingertips, forearms and a torso) may be endowed with robot skin that is tailored to meet specific design goals, which leads to local peculiarities as far as technological, system-level and representation solutions are concerned. This variety leads to the issue of designing a software framework able to: (i) provide a unified interface to access information originating from heterogeneous robot skin systems; (ii) assure portability among different robot skin solutions. In this article, a real-time framework designed to address both these issues is discussed. The presented framework, which is referred to as Skinware, is able to acquire large-scale tactile data from heterogeneous networks in real-time and to provide tactile information using abstract data structures for high-level robot behaviours. As a result, tactile-based robot behaviours can be implemented independently of the actual robot skin hardware and body-part-specific features. An extensive validation campaign has been carried out to investigate Skinware’s capabilities with respect to real-time requirements, data coherency and data consistency when large-scale tactile information is needed.