Research on Circular Area Search algorithm of multi-robot service based on SOA cloud platform

Considering autonomous mobile robots with a variety of specific functions as a kind of service, when there are many types and quantities of services and the density of regional services is large, proposing an algorithm of Circular Area Search (CAS) because of the problem of multi-robot service scheduling in various areas. Firstly, Django is used as the web framework to build the Service-Oriented Architecture (SOA) multi-robot service cloud platform, which is the basic platform for multi-service combination. Then, the service type, the latitude and longitude and the scoring parameters of the service are selected as the service search metrics to design the CAS algorithm that based on the existing service information registered in MySQL and the Gaode Map for screening optimal service, and then providing the service applicant with the best service. Finally, the service applicant applies for the self-driving tour service as an example to perform performance simulation test on the proposed CAS algorithm. The results show that the CAS algorithm of the multi-robot service cloud platform proposed in this paper is practical compared to the global search. And compared with the Greedy Algorithm experiment, the service search time is reduced about 58% compared with the Greedy Algorithm, which verifies the efficiency of CAS algorithm.     

基于协同多目标算法的多机器人路径规划

多机器人路径规划是群体机器人协同工作的前提,其特点是在防碰撞与避障的前提下追求多方面资源的最小消耗.针对这一特点,提出协同非支配排序遗传算法,解决具有多个优化目标的多机器人路径规划问题;运用改进的多目标优化算法,克服多目标优化取权值的不足,同时考虑机器人能源与时间两大资源,以多机器人的路径总长度、总平滑度、总耗时为规划目标.同时引入合作型协同算法框架,将难以求解的多变量问题分组求解.每个机器人的路径视为子种群,子种群通过带精英策略的非支配排序遗传算法,进化并筛选出子种群的部分进入协同进化,每次迭代更新外部的精英解集,最终生成一组非支配路径解.仿真结果表明,在栅格地图环境下,本文算法可有效实现多移动机器人的多优化目标路径规划.     

A Survey of Underwater Multi-Robot Systems

As a cross-cutting field between ocean development and multi-robot system (MRS), the underwater multi-robot system (UMRS) has gained increasing attention from researchers and engineers in recent decades. In this paper, we present a comprehensive survey of cooperation issues, one of the key components of UMRS, from the perspective of the emergence of new functions. More specifically, we categorize the cooperation in terms of task-space, motion-space, measurement-space, as well as their combination. Further, we analyze the architecture of UMRS from three aspects, i.e., the performance of the individual underwater robot, the new functions of underwater robots, and the technical approaches of MRS. To conclude, we have discussed related promising directions for future research. This survey provides valuable insight into the reasonable utilization of UMRS to attain diverse underwater tasks in complex ocean application scenarios.      

基于信息素反应的多机器人协作探测的方法研究

针对单一探测机器人在复杂环境下可靠性和容错能力较低难以完成探测任务的问题,采用了多机器人协作探测的方法,利用吸引素和排斥素将引力和斥力之间的平衡点作为机器人队形控制及避障行为的约束条件,同时利用信息素之间的反应,计算出指引机器人到达目标点的目标信息素及返回初终点的返回信息素的数值,实现缩小机器人的搜索空间,形成最优路径,提高多机器人的协作探测能力。实验证明,引入信息素反应理论,减化机器人相互协作模型,同时能够完成复杂环境下的机器人探测任务,达到了预期效果。     

具有避障的机器人集群系统分布式编队控制

针对具有线性二阶积分运动学模型的机器人集群系统,基于机器人之间的相对状态信息设计了分布式编队控制器.通过变量替换,将机器人集群系统的编队问题转换为一致性控制问题,推导得到了机器人集群系统实现期望编队队形的充分性条件.利用人工势场函数的方法,设计了具有避障功能的机器人编队控制器.最后,仿真实验证明,机器人集群系统实现编队并能够避免与障碍物发生碰撞.     

Genetic Algorithm Based Combinatorial Auction Method for Multi-Robot Task Allocation

An improved genetic algorithm is proposed to solve the problem of bad real-time performance or inability to get a global optimal/better solution when applying single-item auction （SIA） method or combinatorial auction method to multi-robot task allocation. The genetic algorithm based combinatorial auction （GACA） method which combines the basic-genetic algorithm with a new concept of ringed chromosome is used to solve the winner determination problem （WDP） of combinatorial auction. The simulation experiments are conducted in OpenSim, a multi-robot simulator. The results show that GACA can get a satisfying solution in a reasonable shot time, and compared with SIA or parthenogenesis algorithm combinatorial auction （PGACA） method, it is the simplest and has higher search efficiency, also, GACA can get a global better/optimal solution and satisfy the high real-time requirement of multi-robot task allocation.     

多机器人三维仿真与离线编程系统设计

以支持二次开发的三维实体造型软件为开发平台,通用的C++为编程语言,开发了多机器人三维仿真与离线编程系统。离线规划的典型协作运动作业文件在仿真与真实系统中都完成了所规划的协作运动,由此验证了仿真系统具有多机器人三维仿真与离线编程功能。     

一种基于情绪量的多机器人冲突消解方法*

针对动态非结构化环境下多机器人之间存在的空间冲突问题,提出了一种基于情绪量的多机器人冲突消解方法。该方法可以使机器人根据情绪量自主判定对其他机器人的躲避半径,无须预先设定固定的避碰优先级或进行机器人之间的协商。仿真结果表明该方法是一种有效的多机器人冲突消解方法。     

基于博弈论的多机器人任务分配算法

为了寻找一种合理有效的多机器人任务分配算法,基于多机器人协作救火任务环境,以博弈论纳什均衡为基础,研究多机器人的任务分配问题。根据任务模型特点和纳什均衡的主要特征提出了一种基于博弈论的任务分配算法。博弈的效用函数同时考虑了距离、火势和燃烧时间等因素,机器人根据此效用函数选择行为策略,促使机器人尽快扑灭惩罚值较大的火灾而获得较大的奖励值。利用任务总收益函数值的大小评价算法的优劣性。收益函数与火势、燃烧时间和机器人扑灭火灾数有关,这切合实际救火模型。实验结果证明了该任务分配算法的有效性。     

面向电力智能巡检的多机器人系统协同路径规划算法

针对在复杂电力智能巡检任务与环境下,多机器人路径规划算法计算量大、实时性差的问题,提出了面向电力智能巡检的多机器人系统协同路径规划算法。首先结合实际情况,提出了2种不同群体规模下的多机器人系统交通规则法;接着,在所提交通规则法的基础上融入单机器人路径规划算法,提出了多机器人系统协同路径规划算法,且通过加权改进提升其运行效率;最后,通过仿真实验表明了所提2种交通规则以及协同路径规划算法在多机器人系统上应用的可行性和有效性。