Cooperative Tracking a Moving Target Using Multiple Fixed-wing UAVs

A cooperative tracking scheme is presented in this paper for multiple fixed-wing unmanned aerial vehicles (UAVs) to track an uncooperative, moving target. It is comprised of a target loitering algorithm and a formation flight algorithm. The loitering algorithm enables a constant speed UAV to circle around a moving target, whose speed is allowed to vary up to the UAV’s speed. The formation algorithm enables cooperative tracking using multiple UAVs by keeping them flying in a circular formation with equal inter-vehicle angular separation. Under this formation algorithm, the formation center can be controlled independently to perform target loitering, and the admissible range of the target’s speed would not be affected for given UAVs. The performance of the proposed tracking system is verified in numerical simulations.     

Parametric Planning for Multiple Cooperative Robots

A novel planning strategy, parametric planning, is proposed to negotiate the task-oriented object manipulation of multiple coordinated robots. The approach provides an advantage to improve flexibility of robotic cooperation, in which the desired trajectories in Cartesian space derived from task requirements are converted into the trajectories of robots in joint space for a fixed-coordinated multi-robot system. For this purpose, a parametric cooperative index matrix is introduced to handle the relationship of the input desired Cartesian trajectories and the position of robots. A case study of 2-dimension object-motion trajectory tracking using four robots is presented in the end. It proved that the proposed approach effectively delivers trajectory task requirements to the joint trajectories of robots.     

Cooperative Control of Multiple Nonholonomic Mobile Agents

This paper considers two cooperative control problems for nonholonomic mobile agents. In the first problem, we discuss the design of cooperative control laws such that a group of nonholonomic mobile agents cooperatively converges to some stationary point under various communication scenarios. Dynamic control laws for each agent are proposed with the aid of $sigma $-processes and results from graph theory. In the second problem, we discuss the design of cooperative control laws such that a group of mobile agents converges to and tracks a target point which moves along a desired trajectory under various communication scenarios. By introducing suitable variable transformations, cooperative control laws are proposed. Since communication delay is inevitable in cooperative control, in each of the above cooperative control problems, we analyze the effect of delayed communication on the proposed controllers. As applications of the proposed results, formation control of wheeled mobile robots is discussed. It is shown that our results can be successfully used to solve formation control problem. To show effectiveness of the proposed approach, simulation results are included.      

Distributed Cooperative Control of Multiple Nonholonomic Mobile Robots

In this paper, the distributed cooperative control problem is considered for multiple type (1,2) nonholonomic mobile robots. Firstly, a local change of coordinates and feedback is proposed to transform the original nonholonomic system to a new transformed system. Secondly, a distributed controller for the transformed system is designed by using information of the intrinsic system and its neighbors to make the state converge to the same value asymptotically. Furthermore, it shows that the same value can be confined to the origin, which means that the problem of cooperatively converging to a stationary point of a group of nonholonomic systems can be practically solved. Finally, due to the communication delays are inevitable in practice, new distributed controllers for the transformed system are also proposed making the state converge to the same value or zero asymptotically with considering communication delays. The proposed methods are then extended to the case where the nonholonomic mobile robot needs to form a prescribed formation other than agreeing on a same value. The stability of the proposed methods is proved rigorously. Simulation results confirm the effectiveness of the proposed methods.     

多个关节机器人分布式协作运动规划

由多个关节自治机器人构成的协作系统具有高度非线性和严重耦合性,为此提出了分布式协作运动规划方法(DCMP).应用大系统理论的分解协调方法,通过预估协调变量,解耦协作运动规划问题,使其分解为各个机器人个体的独立子规划,得到能够分布式迭代计算的子系统优化模型.利用协同进化算法在全局优化和约束条件处理方面的优势,DCMP解决高维组合C空间的空间—时间优化搜索问题、静态和动态约束问题,尤其是其它方法难以克服的速度约束问题.仿真实验结果证明了本文方法的有效性.     

Cooperative Control of Multiple UAVs for Source Seeking

This paper considers the problem of unknown scalar field source seeking using multiple UAVs subject to input constraints. In this problem, each UAV can only measure the scalar field value at its current location. In order to seek the scalar field source, cooperation of multiple UAVs is carried out by adopting a leader-follower formation strategy. A least squares method is introduced to estimate the gradient of the scalar field at the leader UAV location based on the measurements of all UAVs. By using the estimated gradient, this paper proposes a guidance law for the heading of the leader UAV, and a sliding mode based heading rate controller is designed for the leader UAV to follow the desired heading angle. Furthermore, a heading rate controller is developed for each follower UAV to achieve circular formation around the leader UAV. Finally, simulation results are provided to demonstrate the effectiveness of the proposed approach.     

复合布尔函数中含某单乘积项的快速判定算法

引入了布尔函数的基本积、单乘积项等概念,通过实例剖析了复合布尔函数中是否含某一单乘积项的一般判定方法,在该基础上提出了一种快速判定途径,并给出了快速判定算法的具体描述,同时对算法进行了实验,结果表明该算法快速有效。     

多机器人协同搜索过程中的队形部署

为了完成搜索过程中的二维队形部署,先将一维队形部署算法拓展为二维,然后结合已有的极值搜索算法得到一种混合搜索算法,使多机器人在搜索过程中部署成期望的二维队形,以便完成相应的任务。最后,给出了混合搜索算法的仿真结果以验证了算法的有效性和可操作性。     

Cooperative Control of Multiple UAVs for Moving Source Seeking

Advances in multi-agent technologies and UAV technologies make it possible to take advantage of cooperation of multiple UAVs for source seeking. This paper focuses on moving source seeking using multiple UAVs with input constraints. Firstly, a least-squares method is introduced to estimate the gradient of the scalar field at the leader UAV location based on the measurements of all UAVs. Since the moving source velocity is unknown, an adaptive estimator is designed to obtain the velocity. Based on the estimated gradient and source velocity, a guidance law and a sliding mode based heading rate controller are proposed for the leader UAV to achieve level tracking. Heading rate controller for each follower UAV is also developed to achieve circular formation around the leader UAV. Furthermore, the gradient estimation error is analyzed and its influence on moving source velocity estimation and level tracking accuracy is explored as well. Finally, simulation results are provided to verify the proposed approach.     

对抗环境下多UCAV协同速度规划

在作战对抗环境下,由于目标威胁区域的密集排列,UCAV可能同时受到多个防空设施的威胁.为了有效优化控制飞行速度,利用威胁的火力范围和UCAV的攻击区确定各UCAV路径上的航路点,通过对各航路点之间的平均速度进行优化组合达到对整个飞行过程的速度控制,使飞行过程中所受总威胁值最小,飞行时间最接近预定值,同时满足UAV飞行特性约束,使用基于ε占优的多目标进化算法(MOEA)求解多目标优化速度.仿真结果表明算法能够对问题进行合理优化,获得一组高质量Pareto解,为决策者提供决策的依据.     

多条件约束的单频单历元整周模糊度确定

利用双GPS接收机构成短基线矢量进行定向的技术中针对基线长度已知的情况,将基线的长度信息线性化后融入双差载波观测方程中。分析并证明了融合长度信息后将提高整周模糊度浮点解的精度同时将降低彼此间的相关性。同时利用低成本的倾角传感器测得基线向量的俯仰角信息与基线长度信息一起作为解算的约束条件,将缩小整周模糊的搜索空间从而提高解算的成功率。最后通过实测的数据对上述方法的性能进行定量分析,结果表明该方法能够显著提高单频单历元数据整周模糊度解算的成功率。     

一种单指令多数据向量化归约方法

单指令多数据(SIMD)扩展部件旨在发掘多媒体程序和科学计算程序的数据级并行,归约操作引起的真依赖给发掘程序中的数据级并行带来了阻碍。但体系结构和指令集的差异,使得面向向量机的归约向量化方法并不适用于SIMD扩展部件。针对上述问题,提出一种面向SIMD扩展部件的归约向量代码生成方法,以及归约的识别方法,利用向量移位指令实现向量代码生成。基于SPEC2006标准测试集的测试结果表明,与未利用归约向量化技术前相比,利用该归约向量化方法后的向量化加速比提高34%,从而验证了该方法的有效性。     

软件工程专业多元协同实训体系的建设研究

目前,我国工科教育模式中存在一些重点问题[1],如强调个人学术能力而忽视团队合作精神、重视理论知识的讲授而忽视创新创造能力的培养等,软件工程专业实践教学迫切需要在这些方面进行改进,以培养学生更好的实践能力和团队合作能力。在TOPCARES-CDIO(构思、设计、实现和运作)工程教学理念和思想的指导下,软件工程专业建立了课下与课上一体化、理论学期与实践学期一体化的项目主导、多元协同的实训教学体系。实践证明,通过该模式培养的专业人才,就业竞争力和个人职业发展能力显著增强,建设成果也得到社会、企业及同类型兄弟院校的广泛认可。     

一种多飞行器协同航迹规划的新方法

在综合考虑飞行器编队的飞行代价和作战效果的基础上,提出了一种多飞行器协同航迹规划的新思路,将飞行器编队的协同航迹规划与任务分配结合起来考虑,研究基于任务分配的多飞行器协同航迹规划方法.对传统的飞行器航迹规划模型加以改进,重新建立了结合任务分配的飞行器编队协同航迹规划模型.运用遗传算法对构建的航迹规划模型进行仿真验证,结果表明,基于任务分配的飞行器编队协同航迹规划方法,不仅能保证各飞行器选择合理的协同航迹,也使得作战任务可以获得最佳作战效果,有效的提高编队作战的效费比.     

Multiple UAV Formations for Cooperative Source Seeking and Contour Mapping of a Radiative Signal Field

In this paper, four scenarios are presented for cooperative source seeking and contour mapping of a radiative signal field by multiple UAV formations. A source seeking strategy is adopted with saturation, and then it is modified to achieve contour mapping of the signal field with the moving source situation considered. A formation controller used for consensus problem is simplified and applied in the scenarios to stabilize the multiple UAV formation flight during source detection. The contour mapping strategy and the formation control algorithm are combined to guarantee stable source seeking and contour mapping in both circular flight path and square flight path via multiple UAV formations.     

基于多元反馈的英语线上合作学习模型设计

传统模型只注重学生英语成绩的提高,忽略了学生自信心、交集能力、学习兴趣等的发展,为此设计了一种基于多元反馈的英语线上合作学习模型.经验证,实验班和对照班自信心、语言技能、学习兴趣以及人际关系有非常显著性差异,说明设计合作学习模型有助于学生综合素质的发展.     

A Motion Planning Framework with Connectivity Management for Multiple Cooperative Robots

This paper proposes a decentralized motion planning algorithm for multiple cooperative robots subject to constraints imposed by sensors and the communication network. It consists of decentralized receding horizon planners that reside on each vehicle to navigate to individual target positions. A routing algorithm which modify the network topology based on the position of the robots and the limited range of transmitters and receivers, enables to reduce the communication link failures. A comparative study between the proposed algorithm and other existing algorithms is provided in order to show the advantages especially in terms of traveling time and communication link failure.     

群机器人多目标搜索中的合作协同和竞争协同

群机器人进行多目标搜索时,通过任务分工形成多个子群,各子群分别针对一个意向目标协同搜索,故围绕子群协同中存在的合作关系和竞争关系提出控制策略.为进行合作协同,建立子群发言人的动态遴选机制,不同子群的发言人进行通信,交换所属各子群的最优信息,引导本子群的搜索行为;为进行竞争协同,引入承包机制,按子群的优势地位高低决定是承包还是放弃对某些目标的搜索.两类协同控制的仿真结果表明,合作协同扩大了机器人的感知范围,竞争协同降低了空间冲突机率.二者综合作用,明显提高了群机器人的搜索效率.