未知动态环境下非完整移动群机器人围捕

针对未知动态障碍物环境下非完整移动群机器人围捕,提出了一种基于简化虚拟受力模型的自组织方法.首先给出了个体机器人的运动方程,然后给出了未知动态环境下目标和动态障碍物的运动模型.通过对复杂环境下围捕行为的分解,抽象出简化虚拟受力模型,基于此受力模型,设计了个体运动控制方法,接着证明了系统的稳定性并给出了参数设置范围.不同情况下的仿真结果表明,本文给出的围捕方法可以使群机器人在未知动态障碍物环境下保持较好的围捕队形,并具有良好的避障性能和灵活性.最后分析了本文与基于松散偏好规则的围捕方法相比的优势.     

感知范围受限的群机器人自主围捕算法

针对在有障碍物场地中感知范围受限的群机器人协同围捕问题,本文首先给出了机器人个体、障碍物、目标的模型,并用数学形式对围捕任务进行描述,在此基础上提出了机器人个体基于简化虚拟速度和基于航向避障的自主围捕控制律.基于简化虚拟速度模型的控制律使得机器人能自主地围捕目标同时保持与同伴的距离避免互撞;基于航向的避障方法提升了个体的避障效率,避免斥力避障方法导致的死锁问题.其次本文证明了在该控制律下系统的稳定性.仿真结果表明,该算法在有效围捕目标的同时能够高效地避开障碍物,具有对复杂环境的适应性.最后本文分析了与其他方法相比该算法的优点.     

一种适合MMOG的多NPC协同围捕策略

针对MMOG中多NPC的协同围捕问题,本文提出一种基于运动学和几何学理论的NPC协同围捕策略.采用运动学方法构建个体NPC的运动模型,从而实现个体NPC在游戏中的自主运动控制,通过改变NPC的方向角和使用"正多边形队形法"实现多NPC对目标的靠近和包围.实验结果证明,该策略能使群体NPC成功的围捕目标,满足游戏的实时性、挑战性和真实感的需求.     

基于参考线的预测策略求解动态多目标优化问题

为了快速且准确地跟踪动态多目标优化问题变化的Pareto前沿与Pareto解集,在可以不依靠历史信息的前提下,提出一种基于参考线预测策略的求解动态多目标优化问题的算法(RLPS).该算法通过记录每个参考线关联的种群个体在环境变化初始时和个体自主进化一小段时间后个体位置的变化,预测最优个体所在方向,同时在该方向上均匀分布若干延伸个体,选出每个参考线关联的非支配个体作为当前环境下的引导个体,在选出的引导个体邻域内随机产生若干伴随个体增加种群多样性.通过5个标准动态测试函数对该算法测试,并与两个现有算法作对比分析,实验结果表明所提出的算法具有更快地响应环境变化的能力.     

基于简化虚拟受力模型的群机器人多目标搜索协调控制

针对未知凸和非凸障碍物以及动态障碍物环境下群机器人多目标搜索问题,提出了一种基于简化虚拟受力分析模型的循障和避碰方法(SRSMT-SVF).对复杂环境下群机器人多目标搜索行为进行了分解并抽象出简化虚拟受力分析模型.基于此受力模型,设计了个体机器人协同搜索和漫游状态下的运动控制策略,使得机器人在搜索目标的同时能够实时避碰.通过对不同群体规模系统的仿真实验表明,本文控制方法能够使个体机器人在整个搜索过程中保持良好的避碰性能,有效地减少系统与环境之间和系统内部个体之间的碰撞冲突.相比于扩展粒子群算法(EPSO),本文方法使得搜索耗时和系统能耗至少减少了13.78%、11.96%,数值仿真结果验证了本文方法的有效性.     

RTC树的构建与不确定近邻关系查询方法

空间索引结构和查询技术在空间数据库中具有重要的作用,针对已有的方法在复杂空间数据对象的近似和组织方面的局限性,提出了一种基于最小外接矩形(MBR)、梯形和圆的新的索引结构(RTC树).为了有效处理复杂空间数据对象的最近邻(NN)关系查询问题,提出了基于RTC树的最近邻查询(NNRTC)算法,NNRTC算法利用剪枝规则可减少节点遍历和距离计算.针对障碍物对数据集中最近邻的影响问题,提出了障碍物环境下的基于RTC树的最近邻查询(BNNRTC)算法,BNNRTC算法先在理想空间进行查询,再对查询结果进行判断.为了有效处理动态单纯型连续近邻链查询问题,进一步给出了基于RTC树的动态单纯型连续近邻链查询(SCNNCRTC)算法.实验结果表明,相对基于R树的查询方法,所提的方法在处理数据量较大的复杂空间对象的数据集时可提高60%~80%的效率.     

针对动态目标的多无人机协同组合差分进化搜索方法

针对多无人机动态目标协同搜索问题,提出一种组合差分进化无人机协同搜索航迹规划方法.建立动态目标协同搜索环境信息图模型及无人机运动模型.基于改进差分蝙蝠算法和自适应差分进化算法,设计基于种群数量自适应分配的组合框架,将差分进化算法中的变异、交叉和选择机制引入蝙蝠算法,构建组合差分进化算法的协同搜索算法,并对无人机动态目标协同搜索的航迹进行优化.针对待搜索目标轨迹随机多变且具有规避侦察特性的现实场景,建立可回访数字信息图和自适应目标搜索增益函数,从而提高无人机对动态目标的捕获能力.最后,通过仿真结果验证所提出的无人机动态目标协同搜索算法的有效性.     

复杂室内环境下的WiFi定位技术研究

在复杂的室内WLAN环境下,利用改进的MK模型和加权K最近邻法对定位性能进行了改进。首先介绍了室内传播模型及改进的MK模型;然后介绍了位置指纹定位方法及两种定位算法的基本原理,根据实测数据,分析了以上方法的定位效果以及误差来源;最后提出了MK模型和加权K最近邻法相结合的混合定位法,并对算法进行了仿真和分析。仿真结果表明,所提算法提高了算法精确度,减小了定位误差。     

基于改进鲸鱼优化算法的多无人机围捕

无人机围捕是一项具有挑战性和现实意义的任务,为使无人机可以成功有效地围捕移动目标,提出一种基于动态预测围捕点和改进鲸鱼优化算法的多无人机围捕算法。在环境未知,目标运动轨迹未知的情况下,首先利用多项式拟合预测目标运动轨迹,通过动态预测步数得到预测点,在其周围设置围捕点,然后使用双向协商法为无人机合理分配各个目标点。针对鲸鱼优化算法容易陷入局部最优的缺点,提出基于自适应权重和改变螺旋线位置更新的方法,从而提升算法的开发能力和搜索能力。最终在不同实验环境下进行多次实验仿真,实验结果表明了所提出算法的有效性。     

基于松散偏好规则的群体机器人系统自组织协作围捕

针对群体机器人协作围捕,提出了一种基于松散偏好规则的自组织方法.首 先给出了个体机器人的自由运动模型和围捕行为的数学描述.通过对围捕行为的分解,构造松散偏好 规则来使个体机器人在自组织运动过程中相互协调最终形成理想的围捕队形.在此基础上,设计了个体自组织运动控制器.最后运用Lyapunov稳定性定理证明系统的稳定性.仿真和实验结果表 明,本文给出的自组织方法对于群体机器人协作围捕是行之有效的.     

Adaptive task assignment for multiple mobile robots via swarm intelligence approach

This paper describes an adaptive task assignment method for a team of fully distributed mobile robots with initially identical functionalities in unknown task environments. A hierarchical assignment architecture is established for each individual robot. In the higher hierarchy, we employ a simple self-reinforcement learning model inspired by the behavior of social insects to differentiate the initially identical robots into “specialists” of different task types, resulting in stable and flexible division of labor; on the other hand, in dealing with the cooperation problem of the robots engaged in the same type of task, Ant System algorithm is adopted to organize low-level task assignment. To avoid using a centralized component, a “local blackboard” communication mechanism is utilized for knowledge sharing. The proposed method allows the robot team members to adapt themselves to the unknown dynamic environments, respond flexibly to the environmental perturbations and robustly to the modifications in the team arising from mechanical failure. The effectiveness of the presented method is validated in two different task domains: a cooperative concurrent foraging task and a cooperative collection task.     

A dynamic priority based path planning for cooperation of multiple mobile robots in formation forming

Robots that work in a proper formation show several advantages compared to a single complex robot, such as a reduced cost, robustness, efficiency and improved performance. Existing researches focused on the method of keeping the formation shape during the motion, but usually neglect collision constraints or assume a simplified model of obstacles. This paper investigates the path planning of forming a target robot formation in a clutter environment containing unknown obstacles. The contribution lies in proposing an efficient path planner for the multiple mobile robots to achieve their goals through the clutter environment and developing a dynamic priority strategy for cooperation of robots in forming the target formation. A multirobot system is set up to verify the proposed method of robot path planning. Simulations and experiments results demonstrate that the proposed method can successfully address the collision avoidance problem as well as the formation forming problem.     

Adaptive control of redundant multiple robots in cooperative motion

A redundant robot has more degrees of freedom than what is needed to uniquely position the robot end-effector. In practical applications the extra degrees of freedom increase the orientation and reach of the robot. Also the load carrying capacity of a single robot can be increased by cooperative manipulation of the load by two or more robots. In this paper, we develop an adaptive control scheme for kinematically redundant multiple robots in cooperative motion.In a usual robotic task, only the end-effector position trajectory is specified. The joint position trajectory will therefore be unknown for a redundant multi-robot system and it must be selected from a self-motion manifold for a specified end-effector or load motion. In this paper, it is shown that the adaptive control of cooperative multiple redundant robots can be addressed as a reference velocity tracking problem in the joint space. A stable adaptive velocity control law is derived. This controller ensures the bounded estimation of the unknown dynamic parameters of the robots and the load, the exponential convergence to zero of the velocity tracking errors, and the boundedness of the internal forces. The individual robot joint motions are shown to be stable by decomposing the joint coordinates into two variables, one which is homeomorphic to the load coordinates, the other to the coordinates of the self-motion manifold. The dynamics on the self-motion manifold are directly shown to be related to the concept of zero-dynamics. It is shown that if the reference joint trajectory is selected to optimize a certain type of objective functions, then stable dynamics on the self-motion manifold result. The overall stability of the joint positions is established from the stability of two cascaded dynamic systems involving the two decomposed coordinates.     

未知环境中多移动机器人协作围捕的研究

为了实现多移动机器人在未知环境中的围捕,本文将任务建模为排队、随机搜索、包围、 捕捉和预测五种状态.提出了排队、搜索、包抄、捕捉、预测和方向优化策略,结合状态转换条件保 证了任务的顺利实现.同时,赋予被捕捉对象(下用Invader表示)一种安全运动策略,增加了围捕 的难度.仿真结果表明了所提方法的可行性.     

Towards an Efficient Self-organizing Reconfiguration Method for Self-reconfigurable Robots

This paper deals with the essential technique for self-organizing metamorphosis of self-reconfigurable robots. A general model describing self-reconfigurable robots is proposed; the model is capable of expressing the topological structure of typical 2-D and 3-D self-reconfigurable robots. Afterwards, a self-organizing metamorphic strategy of self-reconfigurable robots, based on full-discrete local intelligence, is proposed. The modules concurrently process the local interactive information. A reasonable motion sequence for modules can be obtained by means of determining and evolving the rules of modules motion. Consequently, the global self-organizing behavior will be obtained, and the complexity of the algorithm is reduced. Simulation samples are provided to illustrate this approach and demonstrate the effectiveness of the proposed strategy.     

Development of A Behavior‐Based Cooperative Search Strategy for Distributed Autonomous Mobile Robots Using Zigbee Wireless Sensor Network

To achieve efficient and objective search tasks in an unknown environment, a cooperative search strategy for distributed autonomous mobile robots is developed using a behavior‐based control framework with individual and group behaviors. The sensing information of each mobile robot activates the individual behaviors to facilitate autonomous search tasks to avoid obstacles. An 802.15.4 ZigBee wireless sensor network then activates the group behaviors that enable cooperative search among the mobile robots. An unknown environment is dynamically divided into several sub‐areas according to the locations and sensing data of the autonomous mobile robots. The group behaviors then enable the distributed autonomous mobile robots to scatter and move in the search environment. The developed cooperative search strategy successfully reduces the search time within the test environments by 22.67% (simulation results) and 31.15% (experimental results).     

非完整移动机器人目标环绕动态反馈线性化控制

本文针对多个非完整移动机器人对静止或运动目标的环绕追踪问题进行研究.每个机器人仅通过自身和其相邻的机器人的位置与方向信息以及所追踪的目标的位置信息来协调其运动.首先,提出了一种基于动态反馈线性化方法的分布式控制策略,并引入一个控制机器人之间相对角间距的非线性函数,控制机器人间的相对角间距.使多个机器人能够以期望的与目标之间的相对距离、环绕速度和机器人之间的相对角间距对目标进行追踪.然后,利用Lyapunov工具对控制算法进行了渐近稳定性和收敛性分析.最后构建了多移动机器人实验平台,进行了数值仿真和实验验证,仿真和实验的运行结果表明了所提出算法的有效性.     

Dyna-Q-based vector direction for path planning problem of autonomous mobile robots in unknown environments

Reinforcement learning (RL) is a popular method for solving the path planning problem of autonomous mobile robots in unknown environments. However, the primary difficulty faced by learning robots using the RL method is that they learn too slowly in obstacle-dense environments. To more efficiently solve the path planning problem of autonomous mobile robots in such environments, this paper presents a novel approach in which the robot’s learning process is divided into two phases. The first one is to accelerate the learning process for obtaining an optimal policy by developing the well-known Dyna-Q algorithm that trains the robot in learning actions for avoiding obstacles when following the vector direction. In this phase, the robot’s position is represented as a uniform grid. At each time step, the robot performs an action to move to one of its eight adjacent cells, so the path obtained from the optimal policy may be longer than the true shortest path. The second one is to train the robot in learning a collision-free smooth path for decreasing the number of the heading changes of the robot. The simulation results show that the proposed approach is efficient for the path planning problem of autonomous mobile robots in unknown environments with dense obstacles.