结合Winner-Take-All的足球机器人控制策略研究

在足球机器人运动过程中,足球机器人处于一个实时对抗的复杂环境中,这就需要机器人有较高的实时运动过程应对能力。需要对每个关键时刻,例如：多机器人抢球过程、单机器人控球过程等,做出合理的应对措施。许多策略的研究都只注重单机器人控球过程的路径规划,没有考虑到多机器人竞争的过程,导致足球机器人整个运动过程中的一些关键步骤的缺失,丧失了完整性,忽略了实时的对抗性。拟采用新的策略解决上述问题：第一步是将采用WTA（Winner Take All）竞争模型去有效的解决多机器人竞争问题;第二步将采用一种改进的APF（Artificial Potential Field）路径规划法来进行避障。解决了传统APF算法的弊端,提高了效率。通过仿真实验,验证了理论的正确性,也验证了所提理论的科学性和实用性,为以后在其他科学领域的实践奠定了基础。     

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。     

基于FIRA仿真足球守门员的决策设计

研究FIRA机器人足球守门员的准确性站位设计。由于守门员站位不精确,导致失败。为了提高守门员对于小球危险程度判断的准确性及其站位的精确性,提出了以球门线为公共弦长、不同半径大小的圆形区域来划分场地,提取小球、场地和守门员的综合信息,通过小球与球门线的距离、射门角度以及速度判断场地区域的危险等级,进而采取不同的防守策略。同时提出一种新的基于"角平分线—中点连线中点"站位方法,综合运用其它不同的站位法来修正守门员最佳位置,结合守门员的动作模型实现快速、准确、高效的防守。实验结果表明,证明防守策略提高了守门员防守的成功率,为足球仿真平台的设计提供了依据。     

基于自适应UKF算法的小球位置的预测

针对足球机器人比赛过程中,小球位置预测精度不高的问题,提出自适应无迹卡尔曼滤波(AUKF,adaptive unscented Kalman filter)算法。该方法利用新息的协方差匹配技术设计模糊控制器,实时调整量测噪声方差,抑制滤波器的发散;根据滤波值与真实值之间的误差来调节采样点到中心点的位置,修正采样策略,分析初始状态对UKF算法的影响,给出UKF算法初始状态的选择公式。最后通过摄像机获取球在一般滚动和连续碰撞情况下的几组图像验证AUKF算法对球的位置预测的精确性和可行性,提高小型足球机器人比赛决策的合理性。     

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.     

基于动态椭圆曲线的足球机器人射门路径规划算法

为提高机器人足球比赛中射门成功率,提出了一种基于动态椭圆曲线的射门路径规划算法。通过计算足球机器人当前位姿及期望射门角度,控制机器人按照椭圆曲线路径运动至射门目标点,实现快速有效击球射门。仿真实验及实物机器人实验验证了算法的有效性,动态椭圆曲线射门规划算法运动路径短,且能够以合理射门角度完成射门。     

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。     

Simulation of football based on PID controller and BP neural network

Wheel robot soccer speed control system using a ball object detection method and PID controller. A control system is based on the object detection system's behavior based on the robot position's orientation to the target position. PIDs are instruments, pressure, speed, and other operational factors used in control, temperature adjustment flow, and industrial control applications. The PID controller uses control loop feedback dynamics to control functional variables and is the most accurate and stable controller. The robot position is held by placing the ball vertically. When the robot's work is perpendicular to the ball, the robot moves with a certain speed controlled by the PIT controller based on the robot's distance and the ball. Standard conditions (standard ball) test results show that the robot can detect the ball material while in the vertical position, whether on the robot's right or left. In the random test that changes direction, the robot can move more dynamically as the ball's change in place.     

类人足球机器人场上目标识别算法

在比赛过程中类人足球机器人的视觉系统需要对足球、球门以及对阵双方机器人进行识别. 考虑到算法的快速性及有效性,采用基于颜色信息的算法对球及球门进行识别,通过球及球门的颜色阈值提取图片中球与球门可能的位置,再由球与球门的背景色或面积信息确定球与球门的正确位置. 对双方机器人的识别,首先提取机器人的特征,然后通过在线实时的监督学习方法训练一组级联分类器,通过训练好的分类器对双方机器人进行检测. 实验表明算法能够快速有效地识别场上目标,且算法具有较好的鲁棒性.     

Evolutionary multi-objective optimization in robot soccer system for education

As the robot soccer system becomes stabilized, it has been used as an educational platform with which various topics on mobile robotics can be taught. As one of key topics in the education of mobile robotics is computational intelligence-based navigation, this paper proposes a multiobjective population-based incremental learning (MOPBIL) algorithm to obtain the fuzzy path planner for optimal path to the ball, minimizing three objectives such as elapsed time, heading direction and posture angle errors in a robot soccer system. MOPBIL employs the probabilistic mechanism, which generates new population using probability vectors. As the probability vectors are updated by referring to nondominated solutions, population converges to Paretooptimal solution set. Simulation and experiment results show the effectiveness of the proposed MOPBIL from the viewpoint of the proximity to the Pareto-optimal set, size of the dominated space, coverage of two sets and diversity metric. By implementing each of the solutions into the educational platform, it can be educated how multi-objective optimization is realized in the real-world problem.     

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.     

腐蚀/膨胀算法在足球机器人视觉系统中的应用

在FIRA MiroSot机器人足球比赛中,视觉系统是获得比赛场上机器人与球位置信息的唯一途径。视觉系统的识别速度、精度直接影响到比赛的胜负。针对传统视觉系统在机器人足球比赛中获取各实体的位置不够准确的问题,提出了一种结合数学形态学中腐蚀/膨胀算法来处理视觉系统中的实时图像,增加足球机器人视觉系统识别的精度的设计方案。实验结果表明,该方案在没有降低比赛中识别速度的前提下,大大地提高了识别精度。     

足球机器人对彩球的鲁棒跟踪识别

在基于视觉的足球机器人系统中,对场上焦点目标——球的动态跟踪识别是系统设计的第一要务。针对半自主微型机器人足球比赛中的小球易受场上干扰、小车遮挡造成的识别丢失问题,提出基于预测与搜索窗的图像目标跟踪识别方法。通过最小二乘法预测丢失小球的可能位置,将图像目标搜索限制在局部小区域内,并利用搜索窗内的在线状态信息加以判断,实现运动目标被遮挡情况下的有效跟踪识别。实验与比赛结果统计表明,该方法实时跟踪识别效果好、鲁棒性强。     

Application of a hybrid controller to a mobile robot

This paper presents the application of a hybrid controller to the optimization of the movement of a mobile robot. Through hybrid controller processes, the optimal angle and velocity of a robot moving in a work space was determined. More effective movement resulted from these hybrid controller processes. The experimental scenarios involved a five-versus-five soccer game and a MATLAB simulation, where the proposed system dynamically assigned the robot to the target position. The hybrid controller was able to choose a better position according to the circumstances encountered. The hybrid controller that is proposed includes a support vector machine and a fuzzy logic controller. We used the method of generalized predictive control to predict the target position, and the support vector machine to determine the optimal angle and velocity required for the mobile robot to reach the goal. First, we used the generalized predictive control to predict the target position. Then, the support vector machine is used to classify the angle that must be followed by the mobile robot to reach the goal. Next, a fuzzy logic controller is designed to determine the velocity of the left and right wheels of the mobile robot. Thus generated, the velocity was optimized according to the measures obtained by the support vector machine. Finally, based on the optimal velocity of robot, the output membership function was modified. Consequently, the proposed hybrid controller allowed the robot to reach the goal quickly and effectively.     

基于强跟踪和H∞滤波计算的足球位置预测算法

机器人足球在动态的环境中运动,存在未知的外界干扰;比赛过程中,又常常与机器人等障碍物发生碰撞,从而引起运动位置和方向的突变.针对上述情况,提出一种基于强跟踪滤波( STF)和H∞滤波计算的足球位置预测算法.通过引入时变渐消因子,既能提高状态突变的跟踪能力,又能避免对干扰信号做出假设.在中型组机器人足球比赛平台上进行实验...     

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 survey on team strategies in robot soccer: team strategies and role description

This survey paper starts with a basic explanation about robot soccer and its systems, then will focus on the strategies that have been used by previous researchers. There is a time-line of described robot soccer strategies, which will show the trend of strategies and technologies. The basic algorithm for each robot, that is described here, morphs from just simple mechanical maneuvering strategies to biologically inspired strategies. These strategies are adapted from many realms. The realm of educational psychology, produced reinforcement learning and Q-learning, commerce produced concepts of market-driven economy, engineering with its potential field, AI with its petri-nets, neural network and fuzzy logic. Even insect and fish were simulated in PSO and have been adapted into robot soccer. All these strategies are surveyed in this paper. Another aspect surveyed here is the vision system trend that is shifting from global vision, to local omni-directional vision, to front-facing local vision, which shows the evolution is towards biologically inspired robot soccer agent, the human soccer player.     

Intelligent decision making in multi-agent robot soccer system through compounded artificial neural networks

This paper proposes a two-stage approach using artificial neural networks for the intelligent decision-making by the robots in a MiroSot small league. The first stage involves the use of an evolutionary algorithm for getting a rough estimate of the neural network weight matrices. The proposed approach is then generalized to the case of quick, intelligent and accurate decision-making in the case of a robot soccer system with robots utilizing the concept of compounded artificial neural networks. In the proposed approach a soccer field is divided into three zones so that the decision of the robots depends on the zone of the ball at any instant. The concept of a forward robot is also introduced in this paper to enhance the accuracy of the decision-making with the global strategy of advancing towards the goal area of the opponent for scoring a goal. Simulation results indicate that the proposed techniques are very effective in taking intelligent decision-making in a multi-agent robot soccer system in MiroSot small league as well as middle league.     

多智能体强化学习及其在足球机器人角色分配中的应用

足球机器人系统是一个典型的多智能体系统, 每个机器人球员选择动作不仅与自身的状态有关, 还要受到其他球员的影响, 因此通过强化学习来实现足球机器人决策策略需要采用组合状态和组合动作. 本文研究了基于智能体动作预测的多智能体强化学习算法, 使用朴素贝叶斯分类器来预测其他智能体的动作. 并引入策略共享机制来交换多智能体所学习的策略, 以提高多智能体强化学习的速度. 最后, 研究了所提出的方法在足球机器人动态角色分配中的应用, 实现了多机器人的分工和协作.