Cooperative autonomous driving: intelligent vehicles sharing city roads

The paper presents the Intelligent Control System Laboratory's (ICSL) Cooperative Autonomous Mobile Robot technologies and their application to intelligent vehicles for cities. The deployed decision and control algorithms made the road-scaled vehicles capable of undertaking cooperative autonomous maneuvers. Because the focus of ICSL's research is in decision and control algorithms, it is therefore reasonable to consider replacing or upgrading the sensors used with more recent road sensory concepts as produced by other research groups. While substantial progress has been made, there are still some issues that need to be addressed such as: decision and control algorithms for navigating roundabouts, real-time integration of all data, and decision-making algorithms to enable intelligent vehicles to choose the driving maneuver as they go. With continued research, it is feasible that cooperative autonomous vehicles will coexist alongside human drivers in the not-too-distant future.     

Iaso: an autonomous fault-tolerant management system for supercomputers

With the increase of system scale, the inherent reliability of supercomputers becomes lower and lower. The cost of fault handling and task recovery increases so rapidly that the reliability issue will soon harm the usability of supercomputers. This issue is referred to as the “reliability wall”, which is regarded as a critical problem for current and future supercomputers. To address this problem, we propose an autonomous fault-tolerant system, named Iaso, in MilkyWay-2 system. Iaso introduces the concept of autonomous management in supercomputers. By autonomous management, the computer itself, rather than manpower, takes charge of the fault management work. Iaso automatically manage the whole lifecycle of faults, including fault detection, fault diagnosis, fault isolation, and task recovery. Iaso endows the autonomous features with MilkyWay-2 system, such as self-awareness, self-diagnosis, self-healing, and self-protection. With the help of Iaso, the cost of fault handling in supercomputers reduces from several hours to a few seconds. Iaso greatly improves the usability and reliability of MilkyWay-2 system.     

Implementation of an ant colony system for DNA sequence optimization

DNA computation exploits the computational power inherent in molecules for information processing. However, in order to perform the computation correctly, a set of good DNA sequences is crucial. A lot of work has been carried out on designing good DNA sequences to archive a reliable molecular computation. In this article, the ant colony system (ACS) is introduced as a new tool for DNA sequence design. In this approach, the DNA sequence design is modeled as a path-finding problem, which consists of four nodes, to enable the implementation of the ACS. The results of the proposed approach are compared with other methods such as the genetic algorithm.     

An adaptive system for autonomous driving

Software Quality Journal - Having systems that can adapt themselves in case of faults or changing environmental conditions is of growing interest for industry and especially for the automotive...     

Intelligent AGV driving toward an autonomous decentralized manufacturing system

This paper proposes two methods that give intelligence to automatically guided vehicles (AGVs). In order to drive AGVs autonomously, two types of problems need to be overcome. They are the AGV navigation problem and collision avoidance problem. The first problem has been well known since 1980s. A new method based on the feature scene recognition and acquisition is proposed. The sparse distributed memory neural network (SDM) is employed for the scene recognition and acquisition. The navigation route for the AGV is learnt by use of Q-learning depending on the recognized and acquired scenes. The second problem is described as mutual understanding of behaviors between AGVs. The method of mutual understanding is proposed by the use of Q-learning. Those two methods are combined together for driving plural AGVs autonomously to deliver raw materials between machine tools in a factory. They are incorporated into the AGVs as the machine intelligence. In experimental simulations, it is verified that the first proposed method can guide the AGV to the suitable navigation and that the second method can acquire knowledge of mutual understanding of the AGVs’ behaviors.     

Cooperative autonomous driving at the Intelligent Control Systems Laboratory

Griffith University's Intelligent Control Systems Laboratory collaborating with INRIA'S IMARA laboratory, completed what we believe is the first on-road demonstration of autonomous passenger vehicles performing cooperative passing and traversal of unsignalized intersections. (Unsignalized intersections have no traffic lights, stop signs, or yield signs to control traffic flow.) This demonstration mated ICSL's communication and collaborative decision-making subsystems with IMARA'S experimental vehicle platforms, creating autonomous vehicles capable of real-time cooperation in real-world applications. The article describes the experiment.     

正交蚁群系统求解无等待流水线调度

针对无等待流水线车间调度问题(no-wait flow-shop scheduling problem,NWFSP)的特点,为了进一步提高求解质量,提出了新的启发函数的构造方法.为降低算法陷入局部最优状态的可能性,采取了控制启发值的上下限进行控制,达到了缩小启发值之间差异的目标.另外,受统计学中的正交试验设计方法的启发,使用正交表对蚁群算法的搜索结果进行正交测试.最后,通过引入多重插入移动机制进行搜索,提高了算法求解的质量.对标准测试数据进行测试,实验结果表明,该方法获得的解要优于标准测试数据中提供的已知解,证明正交测试对解有一定的改进效果.     

基于蚁群算法的交叉路口多相位信号配时优化

针对城市道路交叉口的交通流特性,提出一种交叉路口多相位配时的TSP模型,采用新的优化算法——蚁群算法（ACA）来优化交叉路口多相位配时信号,并以每周期内交叉路口车辆总延误最小作为性能指标进行仿真实验。实验表明：在相同的时间和车辆到达率的情况下,采用蚁群算法优化相位和绿信比的配时方法明显优于定时配时方法,也优于定相位优化绿信比的配时方法,降低了交叉口的车辆延误,提高了通行能力;且该算法的求解速度快,稳定性好。     

一种遗传蚁群系统的研究

在遗传蚁群系统中,为减少蚂蚁构建路径的时间消耗,引入遗传操作,使得当前迭代中蚂蚁构建的路径部分来自于之前迭代获取的优秀巡回路径的遗传;同时为减少由遗传操作产生的算法停滞的影响、提高算法解的质量,对蚁群构建的路径施行2opt变异操作。通过旅行商问题测试算法性能,并与蚁群系统进行比较。实验表明,遗传蚁群系统搜索效率高,而且解的质量优于蚁群系统。     

Holon制造系统的遗传蚁群算法求解

在市场环境的不确定性、市场竞争加剧的背景下,要求企业之间的远程服务紧密联系。因此,高可靠性的远程服务动态优化协调成为现代企业发展先进智能制造系统所要解决的重要问题。为此,将Holon理论引入到企业远程服务配置中,运用遗传蚁群混合算法的运用来提高Holon制造系统的健壮性,解决跨地域企业之间远程服务的配置和调度过程中的协商问题。     

Using a style-based ant colony system for adaptive learning

Adaptive learning provides an alternative to the traditional “one size fits all” approach and has driven the development of teaching and learning towards a dynamic learning process for learning. Therefore, exploring the adaptive paths to suit learners personalized needs is an interesting issue. This paper proposes an extended approach of ant colony optimization, which is based on a recent metaheuristic method for discovering group patterns that is designed to help learners advance their on-line learning along an adaptive learning path. The investigation emphasizes the relationship of learning content to the learning style of each participant in adaptive learning. An adaptive learning rule was developed to identify how learners of different learning styles may associate those contents which have the higher probability of being useful to form an optimal learning path. A style-based ant colony system is implemented and its algorithm parameters are optimized to conform to the actual pedagogical process. A survey was also conducted to evaluate the validity and efficiency of the system in producing adaptive paths to different learners. The results reveal that both the learners and the lecturers agree that the style-based ant colony system is able to provide useful supplementary learning paths.     

蚁群算法的参数分析

蚁群算法（ACS）是一种新型的分布式模拟进化算法,它有较强的解搜索能力、很好的适应性和鲁棒性等,但如果算法中各参数选择不当,则会使算法的运行时间变长,或者陷于局部最优,达到停滞状态。恰当的参数选择,可以使蚁群算法有较好的性能,较快地收敛到全局较优解。以TSP问题为例,通过采用不同参数匹配进行优化的数值实验,分析了算法中参数α、β、ρ籽对算法性能的影响,给出了一定指导性的建议。     

An attribute-based ant colony system for adaptive learning object recommendation

Teachers usually have a personal understanding of what “good teaching” means, and as a result of their experience and educationally related domain knowledge, many of them create learning objects (LO) and put them on the web for study use. In fact, most students cannot find the most suitable LO (e.g. learning materials, learning assets, or learning packages) from webs. Consequently, many researchers have focused on developing e-learning systems with personalized learning mechanisms to assist on-line web-based learning and to adaptively provide learning paths. However, although most personalized learning mechanism systems neglect to consider the relationship between learner attributes (e.g. learning style, domain knowledge) and LO’s attributes. Thus, it is not easy for a learner to find an adaptive learning object that reflects his own attributes in relationship to learning object attributes. Therefore, in this paper, based on an ant colony optimization (ACO) algorithm, we proposed an attributes-based ant colony system (AACS) to help learners find an adaptive learning object more effectively. Our paper makes three critical contributions: (1) It presents an attribute-based search mechanism to find adaptive learning objects effectively; (2) An attributes-ant algorithm was proposed; (3) An adaptive learning rule was developed to identify how learners with different attributes may locate learning objects which have a higher probability of being useful and suitable; (4) A web-based learning portal was created for learners to find the learning objects more effectively.     

A continuous ant colony system framework for fuzzy data mining

The goal of data mining is to find out interesting and meaningful patterns from large databases. In some real applications, many data are quantitative and linguistic. Fuzzy data mining was thus proposed to discover fuzzy knowledge from this kind of data. In the past, two mining algorithms based on the ant colony systems were proposed to find suitable membership functions for fuzzy association rules. They transformed the problem into a multi-stage graph, with each route representing a possible set of membership functions, and then, used the any colony system to solve it. They, however, searched for solutions in a discrete solution space in which the end points of membership functions could be adjusted only in a discrete way. The paper, thus, extends the original approaches to continuous search space, and a fuzzy mining algorithm based on the continuous ant approach is proposed. The end points of the membership functions may be moved in the continuous real-number space. The encoding representation and the operators are also designed for being suitable in the continuous space, such that the actual global optimal solution is contained in the search space. Besides, the proposed approach does not have fixed edges and nodes in the search process. It can dynamically produce search edges according to the distribution functions of pheromones in the solution space. Thus, it can get a better nearly global optimal solution than the previous two ant-based fuzzy mining approaches. The experimental results show the good performance of the proposed approach as well.     

epsilon- DANTE : an ant colony oriented depth search procedure

The eepsilon-Depth ANT Explorer (eepsilon- DANTE ) algorithm applied to a multiple objective optimization problem is presented in this paper. This method is a hybridization of the ant colony optimization algorithm with a depth search procedure, putting together an oriented/limited depth search. A particular design of the pheromone set of rules is suggested for these kinds of optimization problems, which are an adaptation of the single objective case. Six versions with incremental features are presented as an evolutive path, beginning in a single colony approach, where no depth search is applied, to the final eepsilon- DANTE . Versions are compared among themselves in a set of instances of the multiple objective Traveling Salesman Problem. Finally, our best version of eepsilon- DANTE is compared with several established heuristics in the field showing some promising results.     

Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation

In the Motion Planning research field, heuristic methods have demonstrated to outperform classical approaches gaining popularity in the last 35 years. Several ideas have been proposed to overcome the complex nature of this NP-Complete problem. Ant Colony Optimization algorithms are heuristic methods that have been successfully used to deal with this kind of problems. This paper presents a novel proposal to solve the problem of path planning for mobile robots based on Simple Ant Colony Optimization Meta-Heuristic (SACO-MH). The new method was named SACOdm, where d stands for distance and m for memory. In SACOdm, the decision making process is influenced by the existing distance between the source and target nodes; moreover the ants can remember the visited nodes. The new added features give a speed up around 10 in many cases. The selection of the optimal path relies in the criterion of a Fuzzy Inference System, which is adjusted using a Simple Tuning Algorithm. The path planner application has two operating modes, one is for virtual environments, and the second one works with a real mobile robot using wireless communication. Both operating modes are global planners for plain terrain and support static and dynamic obstacle avoidance.     

一种带有竞争机制的混合蚁群算法

针对基本蚁群算法（AS）存在的不足,提出了一种同时包含竞争机制和多种寻优规则的混合蚁群算法（MCAS）。通过对TSP问题的仿真实验,表明MCAS算法选用适当的参数组合后,可以在不增加算法复杂度的前提下表现出比AS算法更佳的全局求解能力和鲁棒性。     

基于差别矩阵的蚁群系统约简方法

针对粗糙集属性约简的结果容易出现局部最优问题,本文引入差别矩阵,将相对和绝对属性约简统一为差别列表上的集合操作,提出基于蚁群系统的启发式数据约简算法,将蚁群算法的启发信息建立在差别矩阵的核度和必要度上。实验仿真表明此算法可以较好的克服普通约简算法的局部最优问题,说明此算法具有较好约简性能。     

双链量子蚁群系统及应用

针对现有量子蚁群算法构造、更新两条信息素链,但只选择一条链进行寻优操作的问题,提出了一种双链量子蚁群系统。该算法采用余弦和正弦双链蚂蚁寻优构造解空间,针对不同链上蚂蚁的特征构造了不同的路径选择策略;定义了信息素量子比特相位角的范围和量子信息素最大最小区间,给出了基于量子旋转门的量子信息素挥发与增强策略,运用了一种信息素的平滑机制以提高算法的性能;最后结合TSP算例对算法进行验证、比较与分析,仿真结果表明双链量子蚁群系统具有算法稳定、寻优能力强的特点。