基于势能场模型的RoboCup进攻策略

文中设定了一个势能场来描述比赛的情况。在这样的势能场中，球将向势能降低的方向运动。定义了3种势能场：第一种是对球场定义的；第二种是对每一个球员定义的；最后一种是前两种的结合。在实际中运用的是第一种。把这一方法提供给队员让他们用它来做出判断，他们将依靠判断势能的大小来决定球往哪踢。把这种策略与以往的基于逻辑准则的策略进行了比较，基于势能场的策略在很多情况下会做出更好的决定，在其他情况下也不会比以前的策略更差。     

移动机器人路径规划强化学习的初始化

针对现有机器人路径规划强化学习算法收敛速度慢的问题,提出了一种基于人工势能场的移动机器人强化学习初始化方法.将机器人工作环境虚拟化为一个人工势能场,利用先验知识确定场中每点的势能值,它代表最优策略可获得的最大累积回报.例如障碍物区域势能值为零,目标点的势能值为全局最大.然后定义Q初始值为当前点的立即回报加上后继点的最大折算累积回报.改进算法通过Q值初始化,使得学习过程收敛速度更快,收敛过程更稳定.最后利用机器人在栅格地图中的路径对所提出的改进算法进行验证,结果表明该方法提高了初始阶段的学习效率,改善了算法性能.     

一种基于势能函数的多智能体编队控制新方法

提出一种针对移动障碍物的编队控制方法．在二次积分模型的基础上,采用了采用了势能函数 进行控制设计,该势能函数是基于电势场的概念提出的．在智能体和障碍物之间采用基于位置并加以速度修 正的势能函数,在智能体之间采用基于位置信息的势能函数．状态反馈的策略则用于控制智能体向目标移动． 此方法减小了整个势能场的复杂度,降低了局部极小值产生的可能．文章证明了这种控制方法的稳定性,并 用仿真说明了此种策略的控制效果．     

一种Sort-first构架的基于包围球的归属判断策略

在大规模场景并行渲染系统的任务拆分过程中,当场景比较复杂,图元数量较多时,采取一种有效的归属判断策略对整个系统的效率具有重要影响.针对采用Sort-first架构的基于立即模式的包围盒的归属判断策略的固有缺点,介绍了一种基于驻留模式的包围球的归属判断策略.实验结果表明,采用这种基于"包围球"的归属判断策略相对于基于"包围盒"的归属判断策略大大提高了图元归属情况的计算速度.     

基于分层强化学习及人工势场的多Agent路径规划方法

针对路径规划算法收敛速度慢及效率低的问题,提出了一种基于分层强化学习及人工势场的多Agent路径规划算法。首先,将多Agent的运行环境虚拟为一个人工势能场,根据先验知识确定每点的势能值,它代表最优策略可获得的最大回报;其次,利用分层强化学习方法的无环境模型学习以及局部更新能力将策略更新过程限制在规模较小的局部空间或维度较低的高层空间上,提高学习算法的性能;最后,针对出租车问题在栅格环境中对所提算法进行了仿真实验。为了使算法贴近真实环境,增加算法的可移植性,在三维仿真环境中对该算法进行验证,实验结果表明该算法收敛速度快,收敛过程稳定。     

基于PER-APF算法的无人驾驶汽车换道轨迹规划

针对传统人工势场算法在解决无人驾驶汽车换道轨迹规划过程中存在的不足,提出一种基于势能重构人工势场 (Potential Energy Reconstruction- Artificial Potential Field, PER-APF) 的无人驾驶汽车换道轨迹规划算法。首先,建立了具有斥力区分的道路边界约束条件和多约束换道轨迹规划模型,通过判断障碍车辆与道路边沿的距离来保证换道过程的安全性与有效性;其次,提出了基于势能重构的改进APF算法,通过构建虚拟区域以及重构物理势能力场,有效的解决了目标不可达以及局部最优问题。仿真结果表明,所设计的PER-APF算法能够快速有效地为无人驾驶汽车规划一条安全合理的换道轨迹。     

基于神经网络的RoboCup进攻策略

机器人足球赛Robocup(Robot World Cup)是国际上规模最大且影响最为广泛的机器人足球赛事。机器人足球比赛已经成为当前人工智能研究的热点之一,作为多Agent系统的一个理想的试验平台,它涉及到了多个技术领域。文章应用神经网络技术解决Robocup仿真组比赛的进攻策略问题,对射门底层技术进行了有效优化。我们把这种策略与以往的基于逻辑准则的策略进行了比较。基于神经网络的优化射门策略在很多情况下会做出更好的决定。     

一种基于数据场的多目标引力搜索算法

提出一种基于数据场的多目标引力搜索算法(DFMOGSA). 该算法利用外部档案存储非支配解, 同时将外部档案视为目标空间的数据场, 通过计算非支配解的势能来判断每个解的密度; 密度最低的解被选为第1类引导粒子, 直接吸引粒子向低密度区域收敛, 提高解分布的均匀性; 另外, 为了确保算法收敛 在种群内, 选择较优粒子作为第2类引导粒子, 通过引力引导粒子搜索. 对比实验结果表明了DFMOGSA算法的有效性和优越性.     

基于势能冷却的内容中心网络缓存替换算法

针对目前内容中心网络CCN缓存替换策略所存在的效率低下等问题,引用物理学中“势能”的概念,并结合“自然冷却”这一自然现象,提出了一种基于势能冷却的替换算法PEC-Rep。根据被访问的次数以及时间间隔,准确判断出内容在未来一段时间内的使用价值,在进行内容替换时,将价值最小的内容删除,使得节点中的内容保持最大价值,满足用户的后续请求。仿真结果表明,PEC-Rep可以有效地提高域内缓存命中率,减轻服务器的负载,提高CCN的整体性能。     

灵活访问且模糊可搜索的EHR云服务系统

在电子健康记录系统(E-Healthcare Record Systems,EHRS)中,一些方案利用密钥策略ABE(KP-ABE)来保护隐私。由用户指定一个访问策略,密文只有与访问策略相匹配时才能被解密。现有的KP-ABE要求在生成密钥期间必须先确定访问策略,这在EHRS中是不可行的,因为有时访问策略在密钥生成后才被决定。基于KP-ABE,提出一种灵活访问且模糊可搜索的EHR云服务系统。该系统不仅实现了基于关键字容错的云端密文搜索,而且允许用户重新定义访问策略并为之生成密钥,因此一个精确的访问策略将不再是必需的。最后,证明了该方案的安全性。     

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。首先定义一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觇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。     

RoboCup比赛环境下足球机器人路径规划研究

RoboCup中型组足球机器人比赛具有高度的对抗性和实时性．比赛中机器人需要针对不同的比赛态势进行角色切换和任务选择．在这种环境下，应用传统人工势场或一般改进型人工势场的路径规划方法都无法得到令人满意的结果．将障碍物与机器人之间的相对速度矢量以及目标与机器人之间的相对速度矢量分别引入人工势场法中，对传统的势场函数进行了改进；并根据机器人的不同角色和任务，采用模糊逻辑方法对势场函数进行修正，提出一种处理多角色多任务环境的改进型人工势场法机器人路径规划方法．仿真试验和实际应用验证了此算法存足球机器人比赛系统中的可行性．     

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.     

Biomimetic control architecture for robotic cooperative tasks

This article proposes some control algorithms to be applied to the MIROSOT robot league architecture. The MIROSOT league soccer game concept is fairly simple: two teams of robots, with 3–5 robots per side, play football autonomously. The ball that the teams play with is an orange golf ball. Above the pitch is a machine vision camera running at 60 frames per second. This camera is linked to a server, which calculates the positions and velocities of each of the robots and the ball, and then determines what each robot should be doing. These instructions are then communicated to the robots over wireless links. In order to develop an efficient control strategy and architecture, the robots have to use strategies from the real human soccer game. Using the software Simi Scout, a suitable analysis of tactics can be extracted from the games. After analyzing the soccer game, a number of attributes are specified and then embedded at different levels. The specified attributes are interconnected, and the analysis of the game is processed for optimization. Using this information, the robot program is adapted and experimental tests/games are played. We comment on the results, and propose an improved control architecture based on practical results.     

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。     

A fuzzy approach to strategic games

A game is a decision-making situation with many players, each having objectives that conflict with each other. The players involved in the game usually make their decisions under conditions of risk or uncertainty. In the paper, a fuzzy approach is proposed to solve the strategic game problem in which the pure strategy set for each player is already defined. Based on the concepts of fuzzy set theory, the approach uses a multicriteria decision-making method to obtain the optimal strategy in the game, a method which shows more advantages than the classical game methods. Moreover, with this approach, some useful conclusions are reached concerning the famous “prisoner's dilemma” problem in game theory     

SimuroSot决策系统中的态势评估算法

态势评估是决策系统的重要组成部分。在仿真机器人足球比赛中,考虑到足球机器人比赛的实质就是策略的对抗,对场上球和机器人实时的态势评估是提高球队实力的一个必需且有效的办法。该文提出并分析了基于SimuroSot5vs5平台中机器人和球的态势评估的数学模型,在此基础上提出了一类实用的程序算法,其有效性已在实验及比赛中得到验证。     

Soft-computing-based emotional expression mechanism for game of computer Go

The game of Go is considered one of the most complicated games in the world. One Go game is divided into three stages: the opening, the middle, and the ending stages. Millions of people regularly play Go in countries around the world. The game is played by two players. One is White and another is Black. The players alternate placing one of their stones on an empty intersection of a square grid-patterned game board. The player with more territory wins the game. This paper proposes a soft-computing-based emotional expression mechanism and applies it to the game of computer Go to make Go beginners enjoy watching Go game and keep their tension on the game. First, the knowledge base and rule base of the proposed mechanism are defined by following the standards of the fuzzy markup language. The soft-computing mechanism for Go regional alarm level is responsible for showing the inferred regional alarm level to Go beginners. Based on the inferred board situation, the fuzzy inference mechanisms for emotional pleasure and arousal are responsible for inferring the pleasure degree and arousal degree, respectively. An emotional expression mapping mechanism maps the inferred degree of pleasure and degree of arousal into the emotional expression of the eye robot. The protocol transmission mechanism finally sends the pre-defined protocol to the eye robot via universal serial bus interface to make the eye robot express its emotional motion. From the experimental results, it shows that the eye robot can support Go beginners to have fun and retain their tension while watching or playing a game of Go.     

Ping-pong player prototype

In this paper, a low-cost robot capable of playing ping-pong against human opponent using a vision system to detect the ball is presented. In the subsequent sections, the three main subsystems of the robot, i.e., the vision system, mechanical structure, and the control systems, are described. A prototype has been designed with lightweight and resistant materials to increase the response time and accurateness of the shot. One of the important features of this system is that it uses only one camera to detect the ball, thus reducing the computational time and hardware requirements. To detect the location of the ball, the robot combines the information about the ball and about the shadow it casts on the table. The expert module control defines the game strategy. Orienting the bat in order to return the ball to the desired position on the table does this. In these experiments, the success rate in returning balls was greater than 80%.