基于佳点集遗传算法的足球机器人策略设计

为了优化足球机器人策略的设计.文中提出了一个基于佳点集遗传算法的足球机器人动作规划算法.首先定义-个足球机器人的动作集合,根据赛场的实际情况为足球机器人分配角色与任务,然后利用佳点集遗传算法为足球机器人选择合适的动作,用该算法进行截球实验和射门实验.实验结果表明,应用新算法的仿真足球机器人动作更准确,效果更佳.     

基于退火进化算法的机器人足球策略研究

针对一般遗传算法存在的一个显著的问题：“早熟收敛”与“快速收敛”之间的矛盾,解决早熟收敛的基本思想就是保持群体中个体的多样性,而模拟退火接受准则（即Metropolis准则）可以解决这方面的问题,可以避免搜索陷入局部极值,确保找到问题的整体最优懈。因此在遗传算法中引入退火操作,定义了一个足球机器人的动作集合,根据赛场上的实际情况为足球机器人分配角色与任务,然后利用退火进化算法为足球机器人选择合适的动作,采用了一种高效的适应性评价方法,实验表明,应用退火进化算法的仿真足球机器人动作准确、效果更好。     

基于退火进化算法的机器人足球策略研究

针对一般遗传算法存在的一个显著的问题:"早熟收敛"与"快速收敛"之间的矛盾,解决早熟收敛的基本思想就是保持群体中个体的多样性,而模拟退火接受准则(即Metropolis准则)可以解决这方面的问题,可以避免搜索陷入局部极值,确保找到问题的整体最优解.因此在遗传算法中引人退火操作,定义了一个足球机器人的动作集合,根据赛场上的实际情况为足球机器人分配角色与任务,然后利用退火进化算法为足球机器人选择合适的动作,采用了一种高效的适应性评价方法,实验表明,应用退火进化算法的仿真足球机器人动作准确、效果更好.     

集控式足球机器人系统关键技术研究

足球机器人比赛是机器人研究的一个新热点,它为人工智能理论和算法的研究提供了一个实验平台,其研究的领域涵盖了人工智能、自动控制、机器人视觉、无线通信、机器学习和多智能体合作与协调等。集控式足球机器人系统通常可以划分为4个子系统,即视觉、决策、通信和车型机器人。结合研究经验,介绍了集控式足球机器人各个子系统的关键技术。     

免疫佳点集遗传算法

结合免疫机制和数论中的佳点集理论,给出了一种免疫佳点集遗传算法。该算法把数论中佳点集理论运用于遗传算法交叉操作和种群初始化的改进,提出带权欧氏距离计算抗体的相似度、浓度和适应度,引入免疫机制使群体保持多样性和快速导向高适应度模式。实验结果验证了该算法可以有效地避免早熟,改善算法的全局收敛性,提高搜索效率。     

基于数论佳点集的遗传算法初始种群均匀设计

文章利用数论中的佳点集理论和方法,给出了遗传算法初始种群生成的一种具有良好多样性的均匀分布设计.通过对遗传算法机理的研究,发现初始种群的分布状态不仅直接关系到遗传算法的全局收敛性,还影响算法的搜索效率,所以对初始种群进行科学合理设定是应用遗传算法进行寻优计算的一个重要问题.基于优化设计思想,提出应用佳点集均匀设计方法确定遗传算法的初始种群.这种方法具有简单易行、种群多样性好、更适合多维情况等特点,实验结果验证了该方法可以有效地改善算法的全局收敛性,提高搜索效率.     

足球机器人动作算法分析和实现

机器人的运动规划最终要靠机器人的基本动作实现。动作算法成为足球机器人的策略执行好坏的关键。通过动力学分析,考虑惯性、摩擦力、通讯延时等因素,制定相应的理论模型。进行算法分析,较好地实现了足球机器人动作的基本动作处理,并成功应用于大场地足球机器人。     

基于FLCN方法的足球机器人动作控制

考虑到足球机器人比赛中射门动作的重要性,为了使足球机器人的动作具有准确性,提出了一种基于FLCN（Fuzzy Limit-Cycle Navigation,即模糊极限环导航）方法的动作控制。通过对极限环形状的改进,使其具有椭圆特性;并针对该情况设计了相应的模糊规则,应用模糊算法确定椭圆中心点的位置来导航机器人,完成截球和射门的动作。在仿真试验与集控式足球机器人实战中进行了验证,结果证明了该方法的有效性。     

佳点集遗传算法

该文借助于遗传算法的理想浓度模型以及由此模型对遗传算法的机理的分析,给出了遗传算法的运行机理发及特点,即遗传算法是一个具有定向制导的随机搜索技术,其定向制导的原则蛎：导向以高适应度模式为祖先的“家族”方向,以此结论为基础,利用数论中的佳点休的理论和方法,对GA算法中的交叉操作进行了重新设计,给出了一个新的GA算法,称之为佳点集遗传算法,最后作者将佳点GA算法应用于求解优化问题、SAT问题、TSP问题和背包问题,并与其它求解SAT算法进行比较,通过模拟比较,可以看出新的算法不但提高了算法的速度和精度,而且避免了其它方法常有的早期收敛的现象,这说明作者对GA算法机理的理解和佳点CA算法可能为GA算法的研究开辟一条新的途径。     

利用佳点集遗传算法的白盒测试用例优先排序

在软件演化过程中,测试用例优先排序作为一种高效实用的回归测试技术,对于提高缺陷的早期检测速率和降低测试成本有重要意义。针对传统遗传算法在白盒测试用例优先排序中收敛速度慢和稳定性差的问题,采用佳点集遗传算法求解白盒测试用例优先排序问题。算法根据程序实体覆盖矩阵对个体进行编码,以程序实体覆盖平均百分比作为适应度函数,采用随机抽样选择算子和佳点集交叉算子产生新一代种群。实验选择6个典型的基准开源项目,以语句、分支和方法作为程序实体,实验结果表明佳点集遗传算法收敛速度快、稳定性好,为回归测试提供了一个有效的测试用例优先排序方法,有助于尽早发现软件缺陷,降低测试成本。     

Intelligent task planning and action selection of a mobile robot in a multi-agent system through a fuzzy neural network approach

This paper proposes an intelligent task planning and action selection mechanism for a mobile robot in a robot soccer system through a fuzzy neural network approach. The proposed fuzzy neural network system is developed through the two dimensional fuzzification of the soccer field. A five layer fuzzy neural network system is trained through error back propagation learning algorithm to impart a strategy based action selection. The action selection depends on the field configuration, and the emergence of a particular field configuration results from the game dynamics. Strategy of the robot changes when the configuration of the objects in the field changes. The proposed fuzzy neural network structure is flexible to accommodate all possible filed configurations. Simulation results indicate that the proposed approach is simple and has the capability in coordinating the multi-agent system through selection of sensible actions.     

A Fuzzy Mechanism for Action Selection of Soccer Robots

A vision-based soccer robot system is proposed, in which vision will identify the position and heading angle of each robot, and the position of the ball. With these imaging data, values for the defense factor, the competition factor, and the angle factor, are obtained. Using the three factors as the input variables of the proposed action selection mechanism, which is expressed in terms of fuzzy rules, the action that each robot should take is then chosen from the five basic actions available for the robots. In this manner, each robot may intercept, shoot, block, sweep the ball, or just stand by. After determining the action of each robot, control commands generated by the host computer are sent to the robot directly through a wireless RS-232. To show the feasibility of the proposed method, experimental results of a robot soccer game will be used for illustration.     

基于动作选择级的多机器人协作

在多机器人环境中,由于每个机器人动作选择的重叠现象,让机器人之间的协作变得很差.提出了一个方法用于确定动作选择级别.在此基础上,可以很好地控制多机器人的协作行为的获取.首先,定义了用于动作选择级优先级的8个级别,这8个级别相应的映射到8个动作子空间.然后,利用局部势场法,每个机器人的动作选择优先级被计算出来,并且因此,每个机器人获得了各自需要搜索的动作子空间.在动作子空间中,每个机器人利用加强学习方法来选择一个适当的动作.最终,把该方法用于机器人足球比赛的机器人局部协作训练中.试验的效果在仿真和实际比赛中得到了证实.     

基于比例导引的足球机器人截球算法研究

常用的足球机器人截球运动控制算法较少,通过类比机器人截球运动与导弹或鱼雷追踪目标的相似性,引入军事目标追踪导引算法中常用的比例导引算法,并给出了导引算法模型及其在足球机器人系统中的应用实现。仿真结果表明该算法效果明显,行之有效,并能够根据不同的应用要求灵活的调整导引系数,取得比较理想的截球控制效果。     

A multi-agent reinforcement learning approach to robot soccer

In this paper, a multi-agent reinforcement learning method based on action prediction of other agent is proposed. In a multi-agent system, action selection of the learning agent is unavoidably impacted by other agents’ actions. Therefore, joint-state and joint-action are involved in the multi-agent reinforcement learning system. A novel agent action prediction method based on the probabilistic neural network (PNN) is proposed. PNN is used to predict the actions of other agents. Furthermore, the sharing policy mechanism is used to exchange the learning policy of multiple agents, the aim of which is to speed up the learning. Finally, the application of presented method to robot soccer is studied. Through learning, robot players can master the mapping policy from the state information to the action space. Moreover, multiple robots coordination and cooperation are well realized.     

The CMUnited-98 champion small-robot team

Robotic soccer presents a large spectrum of challenging research opportunities. In this article, we present the main research and technical contributions of our champion CMUnited-98 small-robot team. The team is a multiagent robotic system with global perception, and distributed cognition and action. We introduce our new robot motion algorithm that reactively generates motion control to account for the target point, the desired robot orientation and obstacle avoidance. Our robots exhibit successful collision-free motion in the highly dynamic robotic soccer environment. At the strategic and decision-making level, we present the role-based behaviors of the CMUnited-98 robotic agents. Team collaboration is remarkably achieved through a new algorithm that allows for team agents to anticipate possible collaboration opportunities. Robots position themselves strategically in open positions that increase passing opportunities. The article terminates with a summary of the results of the RoboCup-98 games in which the CMUnited-98 small-robot team scored a total of 25 goals and suffered 6 goals in the five games that it played.     

The AGILO Robot Soccer Team—Experience-Based Learning and Probabilistic Reasoning in Autonomous Robot Control

This article describes the computational model underlying the AGILO autonomous robot soccer team, its implementation, and our experiences with it. According to our model the control system of an autonomous soccer robot consists of a probabilistic game state estimator and a situated action selection module. The game state estimator computes the robot's belief state with respect to the current game situation using a simple off-the-shelf camera system. The estimated game state comprises the positions and dynamic states of the robot itself and its teammates as well as the positions of the ball and the opponent players. Employing sophisticated probabilistic reasoning techniques and exploiting the cooperation between team mates, the robot can estimate complex game states reliably and accurately despite incomplete and inaccurate sensor information. The action selection module selects actions according to specified selection criteria as well as learned experiences. Automatic learning techniques made it possible to develop fast and skillful routines for approaching the ball, assigning roles, and performing coordinated plays. The paper discusses the computational techniques based on experimental data from the 2001 robot soccer world championship.     

足球机器人动态路径规划方法研究

针对机器人足球系统的高度实时性、不确定性,提出了一种基于统计预测的路径规划方法,该方法考虑到障碍物的速度大小和方向的不确定性,用数学统计的方法对障碍物的运动进行建模;机器人在运动过程中,根据得到的环境信息在机器视觉范围内建立预测窗口和避障窗口,在预测窗口内,机器人根据障碍物的信息建立障碍物的预测区域,在避障窗口内,机器人根据自身的位置与障碍物的预测区域,分别调用切线法或滚动窗口法进行路径规划;该方法属于局部路径规划方法,机器人在移动过程中需要不断更新环境信息来进行避障.