基于进化免疫系统理论的多机器人协作和机器人目标探索研究
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摘要
在多机器人系统中建立协作机制是非常重要的,这些机制可以使一组机器人自治地产生协作行为。研究人员之所以对机器人协作问题产生兴趣主要是基于以下原因:某项任务可能太复杂以至于单个机器人无法完成;与使用单个大而复杂的机器人相比,使用若干个结构简单的机器人将具有更好的柔性和容错性能,而且成本更低.
智能控制代表着更为广义的控制概念,它涉及到机器/机器人与环境的交互作用。智能控制系统特别能够胜任如下任务:在分层式控制体系的不同层级上进行行为规划;依靠以往的经验进行学习;识别各种威胁系统正常运行的因素(如故障)并正确地做出反应;等等。现有的规划系统、专家系统、模糊系统、神经网络、独特型免疫网络、机器学习以及故障诊断等都是与智能控制紧密相关的一些重要研究领域。
随着工业制造领域机器人数量的不断增加,科技人员经常面临这样一些问题:如何让生产车间中的多个机器人能够协调协作,如何让每个机器人都可以实现行为自治。因此,多机器人之间的协作、任务分工、通信以及导航成为机器人学、力学、传感器技术以及智能控制等相关领域需要解决的关键问题。目前,在群机器人学领域的主要挑战是如何建立更有效、更适用的控制策略去解决这些关键问题。为此,本文重点研究了可应用于机器人学的免疫进化计算方法(Evolutionary Immune Computations,EICs)。免疫进化计算是受生物免疫系统工作机理启发而建立的一种全新进化算法。本文所做的研究可以说明如何探索和研究一个生物系统,以及如何从生物系统的运行中得出可应用于多智能体(多机器人)的推论。在我们的研究中,生物免疫系统的某些机能(如B细胞和T细胞在胸腺中的协作,克隆选择和抗体增殖,免疫记忆/二次免疫应答,以及Jerne的独特型免疫网络等)被萃取出来并应用于多机器人智能体的研究中。
以上述免疫特征为基础,本文重点研究了以下五个问题:(1)多智能体系统及其特点;(2)基于独特型免疫网络学说的多机器人智能体之间的通信及协作方法,当多个机器人协作搬运物体时,这些方法可用于机器人准确探测出任何一个具有规则形状或不规则形状物体的重心;(3)基于进化计算技术的机器人性能改进方法,例如采用进化的T淋巴细胞(Evolved T-lymphocyte,ELC)来代表机器人的传感器/探测器;(4)基于免疫系统遗传算法(Immune System GeneticAlgorithm,ISGA)的机器人鲁棒控制方法;(5)利用Simulink软件实现对机器人鲁棒控制进行仿真实验的方法。
首先,我们以多机器人协作搬运物体为例,对上述问题进行了研究。通过协调协作,多个机器人可以将空间中一个具有任意形状的物体从一个位置搬运到另一个位置,搬运过程在数学上可以通过物体重心的位移轨迹来描述。其次,我们将研究工作延展到一个称之为“机器人目标探索”(Robot Goal-Discovery,ROGODIS)的工业问题。在这个问题中,要求一个机器人单独对一个有限大小的区域进行探索,找到一扇满足条件的小门并将目标物体放入其中。整个过程要求机器人避开所遇到的障碍物并成功地找到小门。以下是对这两项研究工作的详细介绍。
目前,许多可用于物体探测的多机器人控制系统都是基于集中式控制的思想设计的。虽然相对来说其实现比较容易,但由于集中式控制的计算量和通信量都比较大,从而使得这类系统的应用效果和应用规模受到制约。并且,集中式控制也不适合用于多机器人的协调协作。在多机器人协调协作中,遇到的主要挑战(问题)是系统中的信息是分布式的。依靠良好的通信机制实现高效、可靠的信息共享是多机器人协作成功的关键。为此,本文建立了基于自然免疫系统工作机理的多智能体系统体系结构,并将人工免疫系统应用到多智能体系统中作为智能体的计算智能。这种体系结构在免疫系统和智能体之间建立了一种类比关系,它将免疫系统机理应用到多智能体系统中,采用分布式方式去实现全局目标。我们建立的这种策略已被成功地应用到多机器人协调协作物体搬运问题中。在这个问题中,我们采用仿真方法建立了一组机器人,它们利用自组织方式协作探测被搬运物体的重心并完成对物体的搬运,整个过程完全采用交互式机制而没有采用任何集中式控制机制。
为了采用自然免疫系统机理在机器人之间实现交互式机制,我们在免疫系统与多机器人系统之间建立了以下对应关系:
第一个是机器人与B细胞之间的对应关系。在物体搬运问题中,一个机器人代表一个B细胞。怎样探测一个物体并确定其重心,每个机器人都会生成它自己独特的行为策略。机器人所做的工作类比于抗原(Antigen,Ag),这里抗原代表物体。
第二个是物体与抗原之间的对应关系。我们使用了一种称为“多化合价、多抗原决定基”的抗原(Multivalent and Multi-determinantAntigen,MVMD-Ag),它具有多个抗原决定基。在免疫系统中,这种抗原能被几种不同的B细胞识别。所以,相同的抗原能被几个细胞记忆识别。在本文中,每一个由机器人探查的物体都采用一种称为物体抗原(Object-antigen,OAg)的新型计算特征量来表征。
第三个是交互式机制实现途径与免疫网络之间的对应关系。免疫网络被用作机器人之间进行交互的模式,机器人之间的通信方式采用独特型免疫网络的工作机理加以实现。
第四个是关于B细胞(机器人)激励水平的计算。一个机器人(B细胞)受到的激励越多,则它采用的行为策略被认为越好。如果一个机器人的激励水平较低,那么它将因为所采取的行为策略较差而受到抑制(淘汰)。相反,如果一个机器人总是能得到良好的激励,说明它的行为策略优秀而将予以保留。为了计算B细胞(机器人)的激励水平,本文提出了一种新的计算方法,它包括如下步骤:建立激励函数,建立亲合力函数(激活阈值),克隆B细胞,B细胞成熟过程,免疫记忆(包括记忆应答和血浆应答)以及建立B细胞抑制函数。这里,B细胞的克隆机制被用来表达信息在机器人之间的传递。基于这些步骤,一个机器人通过与相邻的其它机器人及环境的交互作用而得到激励。如果一个机器人正在执行任务,那么它就会得到更多的激励。而如果这个机器人受激励程度很高,那么它就会产生克隆B细胞,其中包含该机器人所执行任务的信息。
为了评估和改进每一个机器人的性能,我们采用了遗传算法(Genetic Algorithm,GA)进化T林巴细胞(T细胞),使其能更好地适应给定的任务。在我们的研究中,进化的T林巴细胞被用作为机器人的传感器以探测物体的重心和运动方向。每个拥有一个进化的T林巴细胞的B细胞(机器人)都能获得任务所需要的能力和性能。这些能力和性能,一方面可使B细胞(机器人)能够非常准确地探测到被搬运物体的重心;另一方面,根据作用在物体上影响物体移动的作用力,它们又可以确定机器人的运动规则。我们已经采用这种策略通过仿真实验实现了多机器人协作搬运形状规则的物体和形状不规则的物体。在本文中,我们还建立了一种适用于任何形状物体重心计算的通用方法。实验结果表明,采用这些策略和方法,机器人之间的协作更灵活、更有效、更容易实现而且搬运时间更短。这也证明了交互作用、信息传递和高端能力的获取可以使一组机器人涌现出协作行为。
导航、运动规划及自治小车/自治机器人控制都涉及到选择几何路径和机器人运动速度的问题,目的是使机器人在动态环境中避开障碍物并极小化某些代价函数,如时间或能量。速度选择错误可能会导致机器人迷失方向,或浪费时间或能量,更坏的情况甚至可能会使机器人的控制系统变得不稳定。依靠进化理论,许多困难的控制问题现在已经非常容易解决。尽管某些模型对于静态环境中的机器人导航是有用的,但将它们应用到真实的动态环境中时其鲁棒性会变差。以前期其他研究人员的工作为基础,我们对这个问题进行了进一步的研究,提出了一个在动态变化的环境中具有更好鲁棒性的策略。我们的目的是通过在不同抗体率下抗原之间的协作/竞争在B细胞网络中创造出一个更好的涌现行为(这里,抗体多时,抗原之间是协作关系;抗体少时,抗原之间则表现为相互竞争关系)。为此,我们已经建立一个新策略,它包括三个研究阶段。第一阶段使用人工免疫聚类算法(Artificial Immune Clustering Algorithm)和克隆选择原理(CloneChoice Principle)以获取一对抗原,这里,人工免疫聚类算法以适用性免疫网络理论(Adapted Immune Network Theory)为基础,用于抗原的交互-协作-竞争。每对抗原在“机器人目标探索”问题中用于代表小门的两个边框。第二阶段使用一些抗体,用它们决定移动机器人传感器的转向角。第三阶段研究基于动态环境下的运动规划和自治机器人控制问题的免疫原则。然而,当使用进化技术解决动态环境中的问题时,需要克服传统进化算法的某些固有限制,如种群的多样性保持。此外,当处理一个移动机器人系统的稳定性问题时,强收敛性可能是有疑问的,因为许多进化方法(如GA)不能有效地匹配运动控制算法。为了解决这些问题,对于上面的第三个阶段,本文提出了:(1)一种免疫系统遗传算法(Immune System Genetic Algorithm,ISGA)以获得决定一个移动机器人运动控制的最佳控制参数。所提出的这种新算法被称为基于免疫系统的遗传算法(Immune System-based GeneticAlgorithm),其所用到的主要技术有:基于人体(自然)免疫机制的基因库进化,基于人工免疫系统机制的基因库进化,肉体超变异/生物转化,以及记忆B细胞(免疫二次应答)等。这些进化技术被转换并植入到标准遗传算法中以改进其多样性保持能力。ISGA算法用于进化在机器人鲁棒运动控制器中所使用的控制参数,以使机器人的运动时间和路径最短、能耗最低。(2)实时仿真实验环境的描述方法。这些实验测试的是不同参数(如变异率、交叉率及超变异算子)对控制系统性能的影响。(3)以自然免疫系统二次应答机制为基础、用当前所获得的更快更强有力的反应去记忆过去事件的能力。针对单个机器人执行特定的目标探索任务,仿真实验已经论证了机器人能够获得成功完成任务所必需的基本探索和目标发现技能,机器人所涌现出的行为具有智能性、自适应性、柔性及自我调节能力。
为了使我们的工作更加接近于真实情况,以验证运动规划和自治机器人控制的正确性,我们假定一个机器人在一个二维平面上运动,在该平面上定义了一个全局笛卡尔坐标系统。这个机器人具有三个自由度,用一个时间向量p(t)代表它在笛卡尔坐标系统中的位置和姿态。机器人的运动受其线速度ν和角速度ω控制,它们都是时间t的函数。机器人的运动学模型由Jacobian矩阵J(θ)定义。本文所建立的数学模型(包括运动学模型、动力学模型、通信模式、控制模型等)使我们能够在Simulink环境下对一个自治式机器人进行运动控制仿真实验。尽管存在各种与环境相关的约束和干扰(如摩擦、滑动、障碍物等),仿真实验仍可在少于1分种之内被完成,这说明免疫系统遗传算法ISGA具有良好的快速性、稳定性、鲁棒性并且能够非常有效地控制机器人的运动。
本项研究的最终目标是建立多机器人学习和增强自适应能力的更有效方法,在许多领域普及协作型机器人的应用,从而推动多机器人系统在现实世界众多领域中的实际应用。
An important need in multi-robot systems is the development of mechanisms that enable robot teams to autonomously generate cooperative behaviours.Interest for cooperating robots arises when a task is inherently too complex for a single robot to accomplish;or when building and using several simple robots can be more flexible, fault-tolerant or cheaper than using a single large robot.
Furthermore,intelligent control represents a generalization of the concept of control,which includes interactions of a machine/robot with the environment.Intelligent control systems are typically able to perform one or more of the following functions:planning actions at different levels of detail,learning from past experience,identifying changes that threaten the system behaviour,such as failures,and reacting appropriately.This identifies the areas of Planning and Expert Systems, Fuzzy Systems,Neural Networks,Idiotypic Immune Network,Machine Learning,and Failure Diagnosis,to mention but a few,as existing research areas that are related and important to Intelligent Control.
With the ever increasing number of robots in the industrial environment,scientists/technologists are often faced with issues on cooperation and coordination among different robots and their self-governance in a workspace.Therefore,cooperation,division of labor, communication and navigation of robots make heavy demands in all the key areas of robot technology,mechanics,sensors and intelligence.Today, major challenges in collective robotics are how to develop new strategies more effective and easily applicable,which could solve these demands. Because of this,this dissertation investigateS evolutionary immune computations(EICs) applied to robotics.An EIC is a novel evolutionary paradigm inspired by the biological aspects of the immune system.The research work developed serves to illustrate how a biological system can be examined and how inferences can be drawn from its operation that can be exploited in intelligent agents(robots).Some functionalities of the biological immune system(e.g.B-Cell and T-Cell cooperation in an organ named thymus,clonal selection and expansion,immune memory/secondary immune response,and Jerne's idiotypic network...) are identified for use in intelligent agent robots. Based on the above-mentioned immune proprieties,this dissertation focuses mainly on the following issues:ⅰ-Intelligent multi-agent systems (IMAS) and their characteristics;ⅱ- Communication and cooperation methods for sensing the center of gravity G_c of any payload with precision(particularly,objects with regular shapes and those with non-regular shapes),based on information criteria inspired from idiotypic immune network hypothesis;ⅲ- The method for improving the performance of robots(i.e.evolved T-lymphocyte "ELC" which represents robot sensor/detector),based on evolutionary computation techniques;ⅳ- Robust control method,based on immune system genetic algorithm(ISGA);ⅴ- The method for simulating the robust control of a robot using simulink is provided.
The aforementioned subjects are firstly employed for moving any object through space in terms of translating its center of gravity from one place to another.Secondly,we extend our work to an industrial problem called robot goal-discovery "ROGODIS".The robot goal-discovery requires a single robot to explore a limited area and discover a small gate to which the robot must put the object,avoiding any obstacles encountered.The task demands that the gate is discovered and reached successfully.The following are the detail introduction of these aspects.
Many popular multi-robot control systems available for object detection are based on centralized control and operations.While relatively easy to implement,the application and scaling of these systems have often been limited by the large computational and communications associated with their centralized control.Though,the main challenge of robots cooperation is that information is distributed.Sharing efficiently information via communication is thus crucial for robots' cooperation. Because of this,we developed natural immune system-based intelligent multi agent architecture and then we applied artificial immune system to multi agent systems for the computational intelligence of agents.The architecture draws an analogy between the immune system and intelligent agent methodologies.It applies the immune system principles to the agents to achieve a global goal in a decentralized manner.Our strategy has been applied to multi robot cooperation where we build by simulation a group of robots which behave in a self-organizing manner to detect the center of gravity of an object without any centralized control mechanism,but rather by using interaction mechanisms.
Thus,to apply interaction mechanisms between robots at the local level,we use four main immunological metaphors.The first is B-cells, where a robot represents a B-cell and each robot has a particular strategy on how to detect an object and its center of gravity.The work to be done by the robots is analogous to antigens(Ag),which represent objects. Secondly,we use a kind of Ag called multivalent and multi-determinant antigen(MVMD-Ag) which present several epitopes.In immunology, this kind of antigen can be recognized by several different B-cells;hence, the same antigen can be recognized by several cells memories.For this dissertation,a new computational attribute called object-antigen(OAg) will be used to represent each object to be examined by robots.The third is the immune network,which allows interaction between robots(i.e. communication between robots is achieved via the idiotypic immune network).The fourth is the calculation of B-cell stimulation,where the more a robot is stimulated,and the better its strategy is to be considered (i.e.if a robot's stimulation level is considered low,then its strategy is considered too weak and it is suppressed.By revenge,if the robot is well stimulated,its strategy is considered to be good and is preserved).In order to calculate B-cell(robot) stimulation,we proposed a new computational method which includes these steps:stimulation function, affinity function(activation threshold),B-cell cloning,mature actions, immune memory(memory response and plasma response),and suppression function.Here,the B-cell cloning mechanism is used to represent messages of one robot to other robots.Based on these steps,a robot is stimulated by interacting with other neighboring robots and the work environment.If a robot is achieving the work,then it receives more stimulation.If that robot becomes well stimulated,it produces clone B-cells that contain information about the work it is doing,since it is considered to be good work.
To assess and improve the performance of each robot,we use the genetic algorithm(GA) techniques to evolve a T-lymphocyte more suited to the task.Each B-cell(robot) with an evolved lymphocyte(ELC) acquires the ability and performance for the task.The acquired performance on one hand enables B-cell(robot) to detect G_c with high precision and on the other hand determines the movement criteria based on forces which affect translations.For implementation,our strategy has been applied to both objects with only geometrical regular shape and objects with and without a geometrical regular shape.The results show that the cooperation of robots using the method of detection based on the general shape is more adaptable,more effective,easily feasible and less costly in terms of time.We therefore proved that both interaction and passing of messages and the acquisition of high ability enable a group of robots to emerge cooperative behaviour. The problem of navigation,motion planning,and autonomous vehicle or robot control consist of selecting the geometric path and robot velocities so as to avoid obstacles in a dynamic environment and to minimize some cost function such as time or energy.But,selecting the wrong velocities may cause the robot to lose its path,or waste time or energy,or even worse,become unstable.Many difficult control problems have been easily solved relying on the evolutionary approach.Although some models were useful for navigation through static environments,they were less robust when applied to real dynamic environments.Based on previous research,we thus extended this research proposing a more robust strategy to dynamically changing environments.Our intent was to create a more emergent behaviour within the network of robots through cooperation/competition of antigens compared to antibodies rate.As a result,we have developed a new strategy where three phases of study have been described.The first phase uses the artificial immune clustering algorithm based on the adapted idiotypic immune network theory(A-IIN) for antigens' interaction-cooperation-competition,and the clone choice principle(CLONALG) to obtain a pair of antigens.This couple of antigens represents two borders of the gate for robot goal-discovery (ROGODIS) problems.The second phase uses antibodies(Ab) which determine steering angles for mobile robot sensor.The third phase investigates immune principles based on the problem of motion planning and autonomous robot control in a dynamic environment.However,when using evolutionary techniques in order to cope with dynamic environments it is necessary to overcome some limitations inherent to traditional evolutionary algorithms(i.e.maintenance of diversity).In addition,when dealing with stability of a mobile robot system,strong convergence can be problematic,because many evolutionary techniques (like GA) are unable to respond effectively to motion control algorithms. To address these concerns,this dissertation proposes for the third phase quoted above:1 - an immune system genetic algorithm(ISGA) to obtain the optimal control parameters that govern locomotion control of a mobile robot.The new proposed method is referred to as immune system-based genetic algorithm and the mainly used techniques are:gene library evolution by human immune,gene library evolution by artificial immune system,somatic hypermutation/transformation,and memory B-cells(immune secondary response).These evolutionary techniques are translated and included into the standard Genetic Algorithm(GA) for promoting diversity.The ISGA focuses on evolving the control parameters used in a robust locomotion controller to obtain time optimal, shortest path,and minimum energy performance.2 - description of the environment where the experiments were carried out in real time and in simulation.These experiments test the influence of different parameters, such as mutation rate,crossover,and transformation(that represents hypermutation operator).3 - The ability to remember past situations with faster and stronger reactions obtained over time is based on the secondary response,typical of the natural immune system.For a specific implementation extended to a single robot,simulation experiment demonstrates that it is possible for a robot to acquire the essential exploration and goal-discovering skills necessary to accomplish the task successfully.The emergent behaviour is shown to be intelligent,adaptive, flexible and self-regulatory.
In order to make our work similar to real situations,verifying motion planning and autonomous robot control,we assume a mobile robot located on a 2D plane in which a global cartesian coordinate system is defined.The robot possesses three degrees of freedom in its relative positioning which are represented by a posture p(t) which is function of time t.The robot's motion is controlled by its linear velocityνand angular velocityω,which are also functions of time t.The robot's kinematics is defined by Jacobian matrix J(θ).The mathematical models which have been described in this dissertation enabled us to carry out a simulator under Simulink(Matlab).In spite of various constraints and disturbances related to environmental effects(such as friction,saturation, slipping,obstacles etc.),the simulator operation is carried out in less than one minute;what proves that the ISGA has good stability,good robustness,and can control the robot motion very effectively.
The ultimate goal of this dissertation is to develop more effective techniques for multi-robot learning and adaptation that will generalize to cooperative robot applications in many domains,thus facilitating the practical use of multi-robot teams in a wide variety of real-world applications.
智能控制代表着更为广义的控制概念,它涉及到机器/机器人与环境的交互作用。智能控制系统特别能够胜任如下任务:在分层式控制体系的不同层级上进行行为规划;依靠以往的经验进行学习;识别各种威胁系统正常运行的因素(如故障)并正确地做出反应;等等。现有的规划系统、专家系统、模糊系统、神经网络、独特型免疫网络、机器学习以及故障诊断等都是与智能控制紧密相关的一些重要研究领域。
随着工业制造领域机器人数量的不断增加,科技人员经常面临这样一些问题:如何让生产车间中的多个机器人能够协调协作,如何让每个机器人都可以实现行为自治。因此,多机器人之间的协作、任务分工、通信以及导航成为机器人学、力学、传感器技术以及智能控制等相关领域需要解决的关键问题。目前,在群机器人学领域的主要挑战是如何建立更有效、更适用的控制策略去解决这些关键问题。为此,本文重点研究了可应用于机器人学的免疫进化计算方法(Evolutionary Immune Computations,EICs)。免疫进化计算是受生物免疫系统工作机理启发而建立的一种全新进化算法。本文所做的研究可以说明如何探索和研究一个生物系统,以及如何从生物系统的运行中得出可应用于多智能体(多机器人)的推论。在我们的研究中,生物免疫系统的某些机能(如B细胞和T细胞在胸腺中的协作,克隆选择和抗体增殖,免疫记忆/二次免疫应答,以及Jerne的独特型免疫网络等)被萃取出来并应用于多机器人智能体的研究中。
以上述免疫特征为基础,本文重点研究了以下五个问题:(1)多智能体系统及其特点;(2)基于独特型免疫网络学说的多机器人智能体之间的通信及协作方法,当多个机器人协作搬运物体时,这些方法可用于机器人准确探测出任何一个具有规则形状或不规则形状物体的重心;(3)基于进化计算技术的机器人性能改进方法,例如采用进化的T淋巴细胞(Evolved T-lymphocyte,ELC)来代表机器人的传感器/探测器;(4)基于免疫系统遗传算法(Immune System GeneticAlgorithm,ISGA)的机器人鲁棒控制方法;(5)利用Simulink软件实现对机器人鲁棒控制进行仿真实验的方法。
首先,我们以多机器人协作搬运物体为例,对上述问题进行了研究。通过协调协作,多个机器人可以将空间中一个具有任意形状的物体从一个位置搬运到另一个位置,搬运过程在数学上可以通过物体重心的位移轨迹来描述。其次,我们将研究工作延展到一个称之为“机器人目标探索”(Robot Goal-Discovery,ROGODIS)的工业问题。在这个问题中,要求一个机器人单独对一个有限大小的区域进行探索,找到一扇满足条件的小门并将目标物体放入其中。整个过程要求机器人避开所遇到的障碍物并成功地找到小门。以下是对这两项研究工作的详细介绍。
目前,许多可用于物体探测的多机器人控制系统都是基于集中式控制的思想设计的。虽然相对来说其实现比较容易,但由于集中式控制的计算量和通信量都比较大,从而使得这类系统的应用效果和应用规模受到制约。并且,集中式控制也不适合用于多机器人的协调协作。在多机器人协调协作中,遇到的主要挑战(问题)是系统中的信息是分布式的。依靠良好的通信机制实现高效、可靠的信息共享是多机器人协作成功的关键。为此,本文建立了基于自然免疫系统工作机理的多智能体系统体系结构,并将人工免疫系统应用到多智能体系统中作为智能体的计算智能。这种体系结构在免疫系统和智能体之间建立了一种类比关系,它将免疫系统机理应用到多智能体系统中,采用分布式方式去实现全局目标。我们建立的这种策略已被成功地应用到多机器人协调协作物体搬运问题中。在这个问题中,我们采用仿真方法建立了一组机器人,它们利用自组织方式协作探测被搬运物体的重心并完成对物体的搬运,整个过程完全采用交互式机制而没有采用任何集中式控制机制。
为了采用自然免疫系统机理在机器人之间实现交互式机制,我们在免疫系统与多机器人系统之间建立了以下对应关系:
第一个是机器人与B细胞之间的对应关系。在物体搬运问题中,一个机器人代表一个B细胞。怎样探测一个物体并确定其重心,每个机器人都会生成它自己独特的行为策略。机器人所做的工作类比于抗原(Antigen,Ag),这里抗原代表物体。
第二个是物体与抗原之间的对应关系。我们使用了一种称为“多化合价、多抗原决定基”的抗原(Multivalent and Multi-determinantAntigen,MVMD-Ag),它具有多个抗原决定基。在免疫系统中,这种抗原能被几种不同的B细胞识别。所以,相同的抗原能被几个细胞记忆识别。在本文中,每一个由机器人探查的物体都采用一种称为物体抗原(Object-antigen,OAg)的新型计算特征量来表征。
第三个是交互式机制实现途径与免疫网络之间的对应关系。免疫网络被用作机器人之间进行交互的模式,机器人之间的通信方式采用独特型免疫网络的工作机理加以实现。
第四个是关于B细胞(机器人)激励水平的计算。一个机器人(B细胞)受到的激励越多,则它采用的行为策略被认为越好。如果一个机器人的激励水平较低,那么它将因为所采取的行为策略较差而受到抑制(淘汰)。相反,如果一个机器人总是能得到良好的激励,说明它的行为策略优秀而将予以保留。为了计算B细胞(机器人)的激励水平,本文提出了一种新的计算方法,它包括如下步骤:建立激励函数,建立亲合力函数(激活阈值),克隆B细胞,B细胞成熟过程,免疫记忆(包括记忆应答和血浆应答)以及建立B细胞抑制函数。这里,B细胞的克隆机制被用来表达信息在机器人之间的传递。基于这些步骤,一个机器人通过与相邻的其它机器人及环境的交互作用而得到激励。如果一个机器人正在执行任务,那么它就会得到更多的激励。而如果这个机器人受激励程度很高,那么它就会产生克隆B细胞,其中包含该机器人所执行任务的信息。
为了评估和改进每一个机器人的性能,我们采用了遗传算法(Genetic Algorithm,GA)进化T林巴细胞(T细胞),使其能更好地适应给定的任务。在我们的研究中,进化的T林巴细胞被用作为机器人的传感器以探测物体的重心和运动方向。每个拥有一个进化的T林巴细胞的B细胞(机器人)都能获得任务所需要的能力和性能。这些能力和性能,一方面可使B细胞(机器人)能够非常准确地探测到被搬运物体的重心;另一方面,根据作用在物体上影响物体移动的作用力,它们又可以确定机器人的运动规则。我们已经采用这种策略通过仿真实验实现了多机器人协作搬运形状规则的物体和形状不规则的物体。在本文中,我们还建立了一种适用于任何形状物体重心计算的通用方法。实验结果表明,采用这些策略和方法,机器人之间的协作更灵活、更有效、更容易实现而且搬运时间更短。这也证明了交互作用、信息传递和高端能力的获取可以使一组机器人涌现出协作行为。
导航、运动规划及自治小车/自治机器人控制都涉及到选择几何路径和机器人运动速度的问题,目的是使机器人在动态环境中避开障碍物并极小化某些代价函数,如时间或能量。速度选择错误可能会导致机器人迷失方向,或浪费时间或能量,更坏的情况甚至可能会使机器人的控制系统变得不稳定。依靠进化理论,许多困难的控制问题现在已经非常容易解决。尽管某些模型对于静态环境中的机器人导航是有用的,但将它们应用到真实的动态环境中时其鲁棒性会变差。以前期其他研究人员的工作为基础,我们对这个问题进行了进一步的研究,提出了一个在动态变化的环境中具有更好鲁棒性的策略。我们的目的是通过在不同抗体率下抗原之间的协作/竞争在B细胞网络中创造出一个更好的涌现行为(这里,抗体多时,抗原之间是协作关系;抗体少时,抗原之间则表现为相互竞争关系)。为此,我们已经建立一个新策略,它包括三个研究阶段。第一阶段使用人工免疫聚类算法(Artificial Immune Clustering Algorithm)和克隆选择原理(CloneChoice Principle)以获取一对抗原,这里,人工免疫聚类算法以适用性免疫网络理论(Adapted Immune Network Theory)为基础,用于抗原的交互-协作-竞争。每对抗原在“机器人目标探索”问题中用于代表小门的两个边框。第二阶段使用一些抗体,用它们决定移动机器人传感器的转向角。第三阶段研究基于动态环境下的运动规划和自治机器人控制问题的免疫原则。然而,当使用进化技术解决动态环境中的问题时,需要克服传统进化算法的某些固有限制,如种群的多样性保持。此外,当处理一个移动机器人系统的稳定性问题时,强收敛性可能是有疑问的,因为许多进化方法(如GA)不能有效地匹配运动控制算法。为了解决这些问题,对于上面的第三个阶段,本文提出了:(1)一种免疫系统遗传算法(Immune System Genetic Algorithm,ISGA)以获得决定一个移动机器人运动控制的最佳控制参数。所提出的这种新算法被称为基于免疫系统的遗传算法(Immune System-based GeneticAlgorithm),其所用到的主要技术有:基于人体(自然)免疫机制的基因库进化,基于人工免疫系统机制的基因库进化,肉体超变异/生物转化,以及记忆B细胞(免疫二次应答)等。这些进化技术被转换并植入到标准遗传算法中以改进其多样性保持能力。ISGA算法用于进化在机器人鲁棒运动控制器中所使用的控制参数,以使机器人的运动时间和路径最短、能耗最低。(2)实时仿真实验环境的描述方法。这些实验测试的是不同参数(如变异率、交叉率及超变异算子)对控制系统性能的影响。(3)以自然免疫系统二次应答机制为基础、用当前所获得的更快更强有力的反应去记忆过去事件的能力。针对单个机器人执行特定的目标探索任务,仿真实验已经论证了机器人能够获得成功完成任务所必需的基本探索和目标发现技能,机器人所涌现出的行为具有智能性、自适应性、柔性及自我调节能力。
为了使我们的工作更加接近于真实情况,以验证运动规划和自治机器人控制的正确性,我们假定一个机器人在一个二维平面上运动,在该平面上定义了一个全局笛卡尔坐标系统。这个机器人具有三个自由度,用一个时间向量p(t)代表它在笛卡尔坐标系统中的位置和姿态。机器人的运动受其线速度ν和角速度ω控制,它们都是时间t的函数。机器人的运动学模型由Jacobian矩阵J(θ)定义。本文所建立的数学模型(包括运动学模型、动力学模型、通信模式、控制模型等)使我们能够在Simulink环境下对一个自治式机器人进行运动控制仿真实验。尽管存在各种与环境相关的约束和干扰(如摩擦、滑动、障碍物等),仿真实验仍可在少于1分种之内被完成,这说明免疫系统遗传算法ISGA具有良好的快速性、稳定性、鲁棒性并且能够非常有效地控制机器人的运动。
本项研究的最终目标是建立多机器人学习和增强自适应能力的更有效方法,在许多领域普及协作型机器人的应用,从而推动多机器人系统在现实世界众多领域中的实际应用。
An important need in multi-robot systems is the development of mechanisms that enable robot teams to autonomously generate cooperative behaviours.Interest for cooperating robots arises when a task is inherently too complex for a single robot to accomplish;or when building and using several simple robots can be more flexible, fault-tolerant or cheaper than using a single large robot.
Furthermore,intelligent control represents a generalization of the concept of control,which includes interactions of a machine/robot with the environment.Intelligent control systems are typically able to perform one or more of the following functions:planning actions at different levels of detail,learning from past experience,identifying changes that threaten the system behaviour,such as failures,and reacting appropriately.This identifies the areas of Planning and Expert Systems, Fuzzy Systems,Neural Networks,Idiotypic Immune Network,Machine Learning,and Failure Diagnosis,to mention but a few,as existing research areas that are related and important to Intelligent Control.
With the ever increasing number of robots in the industrial environment,scientists/technologists are often faced with issues on cooperation and coordination among different robots and their self-governance in a workspace.Therefore,cooperation,division of labor, communication and navigation of robots make heavy demands in all the key areas of robot technology,mechanics,sensors and intelligence.Today, major challenges in collective robotics are how to develop new strategies more effective and easily applicable,which could solve these demands. Because of this,this dissertation investigateS evolutionary immune computations(EICs) applied to robotics.An EIC is a novel evolutionary paradigm inspired by the biological aspects of the immune system.The research work developed serves to illustrate how a biological system can be examined and how inferences can be drawn from its operation that can be exploited in intelligent agents(robots).Some functionalities of the biological immune system(e.g.B-Cell and T-Cell cooperation in an organ named thymus,clonal selection and expansion,immune memory/secondary immune response,and Jerne's idiotypic network...) are identified for use in intelligent agent robots. Based on the above-mentioned immune proprieties,this dissertation focuses mainly on the following issues:ⅰ-Intelligent multi-agent systems (IMAS) and their characteristics;ⅱ- Communication and cooperation methods for sensing the center of gravity G_c of any payload with precision(particularly,objects with regular shapes and those with non-regular shapes),based on information criteria inspired from idiotypic immune network hypothesis;ⅲ- The method for improving the performance of robots(i.e.evolved T-lymphocyte "ELC" which represents robot sensor/detector),based on evolutionary computation techniques;ⅳ- Robust control method,based on immune system genetic algorithm(ISGA);ⅴ- The method for simulating the robust control of a robot using simulink is provided.
The aforementioned subjects are firstly employed for moving any object through space in terms of translating its center of gravity from one place to another.Secondly,we extend our work to an industrial problem called robot goal-discovery "ROGODIS".The robot goal-discovery requires a single robot to explore a limited area and discover a small gate to which the robot must put the object,avoiding any obstacles encountered.The task demands that the gate is discovered and reached successfully.The following are the detail introduction of these aspects.
Many popular multi-robot control systems available for object detection are based on centralized control and operations.While relatively easy to implement,the application and scaling of these systems have often been limited by the large computational and communications associated with their centralized control.Though,the main challenge of robots cooperation is that information is distributed.Sharing efficiently information via communication is thus crucial for robots' cooperation. Because of this,we developed natural immune system-based intelligent multi agent architecture and then we applied artificial immune system to multi agent systems for the computational intelligence of agents.The architecture draws an analogy between the immune system and intelligent agent methodologies.It applies the immune system principles to the agents to achieve a global goal in a decentralized manner.Our strategy has been applied to multi robot cooperation where we build by simulation a group of robots which behave in a self-organizing manner to detect the center of gravity of an object without any centralized control mechanism,but rather by using interaction mechanisms.
Thus,to apply interaction mechanisms between robots at the local level,we use four main immunological metaphors.The first is B-cells, where a robot represents a B-cell and each robot has a particular strategy on how to detect an object and its center of gravity.The work to be done by the robots is analogous to antigens(Ag),which represent objects. Secondly,we use a kind of Ag called multivalent and multi-determinant antigen(MVMD-Ag) which present several epitopes.In immunology, this kind of antigen can be recognized by several different B-cells;hence, the same antigen can be recognized by several cells memories.For this dissertation,a new computational attribute called object-antigen(OAg) will be used to represent each object to be examined by robots.The third is the immune network,which allows interaction between robots(i.e. communication between robots is achieved via the idiotypic immune network).The fourth is the calculation of B-cell stimulation,where the more a robot is stimulated,and the better its strategy is to be considered (i.e.if a robot's stimulation level is considered low,then its strategy is considered too weak and it is suppressed.By revenge,if the robot is well stimulated,its strategy is considered to be good and is preserved).In order to calculate B-cell(robot) stimulation,we proposed a new computational method which includes these steps:stimulation function, affinity function(activation threshold),B-cell cloning,mature actions, immune memory(memory response and plasma response),and suppression function.Here,the B-cell cloning mechanism is used to represent messages of one robot to other robots.Based on these steps,a robot is stimulated by interacting with other neighboring robots and the work environment.If a robot is achieving the work,then it receives more stimulation.If that robot becomes well stimulated,it produces clone B-cells that contain information about the work it is doing,since it is considered to be good work.
To assess and improve the performance of each robot,we use the genetic algorithm(GA) techniques to evolve a T-lymphocyte more suited to the task.Each B-cell(robot) with an evolved lymphocyte(ELC) acquires the ability and performance for the task.The acquired performance on one hand enables B-cell(robot) to detect G_c with high precision and on the other hand determines the movement criteria based on forces which affect translations.For implementation,our strategy has been applied to both objects with only geometrical regular shape and objects with and without a geometrical regular shape.The results show that the cooperation of robots using the method of detection based on the general shape is more adaptable,more effective,easily feasible and less costly in terms of time.We therefore proved that both interaction and passing of messages and the acquisition of high ability enable a group of robots to emerge cooperative behaviour. The problem of navigation,motion planning,and autonomous vehicle or robot control consist of selecting the geometric path and robot velocities so as to avoid obstacles in a dynamic environment and to minimize some cost function such as time or energy.But,selecting the wrong velocities may cause the robot to lose its path,or waste time or energy,or even worse,become unstable.Many difficult control problems have been easily solved relying on the evolutionary approach.Although some models were useful for navigation through static environments,they were less robust when applied to real dynamic environments.Based on previous research,we thus extended this research proposing a more robust strategy to dynamically changing environments.Our intent was to create a more emergent behaviour within the network of robots through cooperation/competition of antigens compared to antibodies rate.As a result,we have developed a new strategy where three phases of study have been described.The first phase uses the artificial immune clustering algorithm based on the adapted idiotypic immune network theory(A-IIN) for antigens' interaction-cooperation-competition,and the clone choice principle(CLONALG) to obtain a pair of antigens.This couple of antigens represents two borders of the gate for robot goal-discovery (ROGODIS) problems.The second phase uses antibodies(Ab) which determine steering angles for mobile robot sensor.The third phase investigates immune principles based on the problem of motion planning and autonomous robot control in a dynamic environment.However,when using evolutionary techniques in order to cope with dynamic environments it is necessary to overcome some limitations inherent to traditional evolutionary algorithms(i.e.maintenance of diversity).In addition,when dealing with stability of a mobile robot system,strong convergence can be problematic,because many evolutionary techniques (like GA) are unable to respond effectively to motion control algorithms. To address these concerns,this dissertation proposes for the third phase quoted above:1 - an immune system genetic algorithm(ISGA) to obtain the optimal control parameters that govern locomotion control of a mobile robot.The new proposed method is referred to as immune system-based genetic algorithm and the mainly used techniques are:gene library evolution by human immune,gene library evolution by artificial immune system,somatic hypermutation/transformation,and memory B-cells(immune secondary response).These evolutionary techniques are translated and included into the standard Genetic Algorithm(GA) for promoting diversity.The ISGA focuses on evolving the control parameters used in a robust locomotion controller to obtain time optimal, shortest path,and minimum energy performance.2 - description of the environment where the experiments were carried out in real time and in simulation.These experiments test the influence of different parameters, such as mutation rate,crossover,and transformation(that represents hypermutation operator).3 - The ability to remember past situations with faster and stronger reactions obtained over time is based on the secondary response,typical of the natural immune system.For a specific implementation extended to a single robot,simulation experiment demonstrates that it is possible for a robot to acquire the essential exploration and goal-discovering skills necessary to accomplish the task successfully.The emergent behaviour is shown to be intelligent,adaptive, flexible and self-regulatory.
In order to make our work similar to real situations,verifying motion planning and autonomous robot control,we assume a mobile robot located on a 2D plane in which a global cartesian coordinate system is defined.The robot possesses three degrees of freedom in its relative positioning which are represented by a posture p(t) which is function of time t.The robot's motion is controlled by its linear velocityνand angular velocityω,which are also functions of time t.The robot's kinematics is defined by Jacobian matrix J(θ).The mathematical models which have been described in this dissertation enabled us to carry out a simulator under Simulink(Matlab).In spite of various constraints and disturbances related to environmental effects(such as friction,saturation, slipping,obstacles etc.),the simulator operation is carried out in less than one minute;what proves that the ISGA has good stability,good robustness,and can control the robot motion very effectively.
The ultimate goal of this dissertation is to develop more effective techniques for multi-robot learning and adaptation that will generalize to cooperative robot applications in many domains,thus facilitating the practical use of multi-robot teams in a wide variety of real-world applications.
引文
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