复杂环境下群机器人自组织协同多目标围捕

针对动态多目标围捕,提出了一种复杂环境下协同自组织多目标围捕方法.首先设计了多目标在复杂环境下的运动模型,然后通过对生物群体围捕行为的研究,构建了多目标简化虚拟受力模型.基于此受力模型和提出的动态多目标自组织任务分配算法,提出了群机器人协同自组织动态多目标围捕算法,这两个算法只需多目标和个体两最近邻位置信息以及个体面向多目标中心方向的两最近邻任务信息,计算简单高效,易于实现.接着获得了系统稳定时参数的设置范围.由仿真可知,所提的方法具有较好的灵活性、可扩展性和鲁棒性.最后给出了所提方法相较于其它方法的优势.     

未知动态环境下非完整移动群机器人围捕

针对未知动态障碍物环境下非完整移动群机器人围捕,提出了一种基于简化虚拟受力模型的自组织方法.首先给出了个体机器人的运动方程,然后给出了未知动态环境下目标和动态障碍物的运动模型.通过对复杂环境下围捕行为的分解,抽象出简化虚拟受力模型,基于此受力模型,设计了个体运动控制方法,接着证明了系统的稳定性并给出了参数设置范围.不同情况下的仿真结果表明,本文给出的围捕方法可以使群机器人在未知动态障碍物环境下保持较好的围捕队形,并具有良好的避障性能和灵活性.最后分析了本文与基于松散偏好规则的围捕方法相比的优势.     

A simple approach to the control of locomotion in self-reconfigurable robots

In this paper we present role-based control which is a general bottom-up approach to the control of locomotion in self-reconfigurable robots. We use role-based control to implement a caterpillar, a sidewinder, and a rolling track gait in the CONRO self-reconfigurable robot consisting of eight modules. Based on our experiments and discussion we conclude that control systems based on role-based control are minimal, robust to communication errors, and robust to reconfiguration.     

Multiagent control of self-reconfigurable robots

We demonstrate how multiagent systems provide useful control techniques for modular self-reconfigurable (metamorphic) robots. Such robots consist of many modules that can move relative to each other, thereby changing the overall shape of the robot to suit different tasks. Multiagent control is particularly well-suited for tasks involving uncertain and changing environments. We illustrate this approach through simulation experiments of Proteo, a metamorphic robot system currently under development.     

Self-Repairing Algorithm of Lattice-Type Self-Reconfigurable Modular Robots

Self-reconfigurable modular robots consist of many identical modules. By changing the connections among modules, the configuration of the robot can be transformed into other configurations. For the self-reconfigurable modular robot, one of its main functions is its self-repairing ability. First, the module of the lattice-type self-reconfigurable robot is presented. It is composed of a central cube and six rotary arms. On each rotary arm the docking mechanism is designed to show the self-repairing ability. Second, the basic motion of the self-reconfigurable robot is described to change the positions of the module. The state matrix and the location matrix are proposed to describe the connection states. Third, a self-repairing algorithm based on the positions of the faulty modules is presented. The algorithm applies the Breadth-First-Search method and the Depth-First-Search method to find a locomotion path by which the faulty module is ejected and replaced by a spare module. At last, a simulation on the fourth-order lattice-type self-reconfigurable robot consisting of 729 modules shows the feasibility and effectiveness of this self-repairing algorithm in three dimensions.     

自重构机器人的自组织变形

本文深入研究了自重构机器人实现自组织变形的基本方法．首先根据自重构机器人 系统结构的基本特征提出一种描述模型，可以对各类模块化自重构机器人的拓扑结构进行统 一描述．然后提出一种建立在全离散的局部智能基础上的自重构机器人的自组织变形策略， 通过建立适当的模块运动规则和规则进化使机器人由局部自主运动产生全局系统自组织的结 果．     

自重构机器人系统中任务规划策略研究

在自重构多机器人系统中，机器人控制需要解决的关键问题是系统的任务规划和多机器人的协调控制。其中，任务规划是高层的组织与决策机制问题，指如何组织多个机器人共同完成任务，实现机器人系统资源的优化配置。本文通过建立一种多机器人系统的模型，结合集中规划的决策方法，实现自重构机器人系统任务规划的优化问题。仿真结果表明，该方法是可行的。     

Design of the ATRON lattice-based self-reconfigurable robot

Self-reconfigurable robots are robots that can change their shape in order to better suit their given task in their immediate environment. Related work on around fifteen such robots is presented, compared and discussed. Based on this survey, design considerations leading to a novel design for a self-reconfigurable robot, called “ATRON”, is described. The ATRON robot is a lattice-based self-reconfigurable robot with modules composed of two hemispheres joined by a single revolute joint. Mechanical design and resulting system properties are described and discussed, based on FEM analyses as well as real-world experiments. It is concluded that the ATRON design is both competent and novel. Even though the ATRON modules are minimalistic, in the sense that they have only one actuated degree of freedom, the collective of modules is capable of self-reconfiguring in three dimensions. Also, a question is raised on how to compare and evaluate designs for self-reconfigurable robots, with a focus on lattice-based systems.     

High-level motion planning for CPG-driven modular robots

Modular robots may become candidates for search and rescue operations or even for future space missions, as they can change their structure to adapt to terrain conditions and to better fulfill a given task. A core problem in such missions is the ability to visit distant places in rough terrain. Traditionally, the motion of modular robots is modeled using locomotion generators that can provide various gaits, e.g. crawling or walking. However, pure locomotion generation cannot ensure that desired places in a complex environment with obstacles will in fact be reached. These cases require several locomotion generators providing motion primitives that are switched using a planning process that takes the obstacles into account. In this paper, we present a novel motion planning method for modular robots equipped with elementary motion primitives. The utilization of primitives significantly reduces the complexity of the motion planning which enables plans to be created for robots of arbitrary shapes. The primitives used here do not need to cope with environmental changes, which can therefore be realized using simple locomotion generators that are scalable, i.e., the primitives can provide motion for robots with many modules. As the motion primitives are realized using locomotion generators, no reconfiguration is required and the proposed approach can thus be used even for modular robots without self-reconfiguration capabilities. The performance of the proposed algorithm has been experimentally verified in various environments, in physical simulations and also in hardware experiments.     

Multimode locomotion via SuperBot reconfigurable robots

One of the most challenging issues for a self-sustaining robotic system is how to use its limited resources to accomplish a large variety of tasks. The scope of such tasks could include transportation, exploration, construction, inspection, maintenance,in-situ resource utilization, and support for astronauts. This paper proposes a modular and reconfigurable solution for this challenge by allowing a robot to support multiple modes of locomotion and select the appropriate mode for the task at hand. This solution relies on robots that are made of reconfigurable modules. Each locomotion mode consists of a set of characteristics for the environment type, speed, turning-ability, energy-efficiency, and recoverability from failures. This paper demonstrates a solution using the SuperBot robot that combines advantages from M-TRAN, CONRO, ATRON, and other chain-based and lattice-based robots. At the present, a single real SuperBot module can move, turn, sidewind, maneuver, and travel on batteries up to 500 m on carpet in an office environment. In physics-based simulation, SuperBot modules can perform multimodal locomotions such as snake, caterpillar, insect, spider, rolling track, H-walker, etc. It can move at speeds of up to 1.0 m/s on flat terrain using less than 6 W per module, and climb slopes of no less 40 degrees. Harris Chi Ho Chiu is a PhD Student in Computer Science at the University of Southern California and a research assistant in Polymorphic Robotics Laboratory of Information Science Institute. He received his Master in Computer Science from the University of Southern California and his Bachelor of Engineering from the University of Hong Kong. His research interests include intelligent automated systems, modular self-reconfigurable systems, artificial intelligence, and machine learning. Michael Rubenstein is currently a PhD student at the Polymorphic Robotics Laboratory, working on the CONRO and Superbot self-reconfigurable robotic systems. He has received his bachelors in Electrical Engineering from Purdue University, and his masters in Electrical Engineering from the University of Southern California, and is currently working towards his PhD in Computer Science from the University of Southern California. His interests include modular self-reconfigurable systems, autonomous robots, self-healing systems, and self-replicating systems. Jagadesh B Venkatesh is a member of the Polymorphic Robotics Laboratory at the Information Sciences Institute. He is currently a Master’s candidate in the Product Development Engineering program at the University of Southern California. He received his MS in Computer Science with specialization in Intelligent Robotics, also at the University of Southern California in 2005. His current interest is the commercialization of robotic technologies, specifically in the consumer robotics sector.     

EDHMoR: Evolutionary designer of heterogeneous modular robots

This paper is devoted to the problem of automatically designing feasible and manufacturable robots made up of heterogeneous modules. Specifically, the coevolution of morphology and control in robots is analyzed and a particular strategy to address this problem is contemplated. To this end, the main issues of this approach such as encoding, evaluation or transfer to reality are studied through the use of heterogeneous modular structures with distributed control. We also propose a constructive evolutionary algorithm based on tree-like representations of the morphology that can intrinsically provide for a type of generative evolutionary approach. The algorithm introduces some new elements to smooth the search space and make finding solutions much easier. The evaluation of the individuals is carried out in simulations and then transferred to real robots assembled from the modules considered. To this end, the extension of the principles proposed by classical authors in traditional evolutionary robotics to brain–body evolution regarding how simulations should be set up so that robust behaviors that can be transferred to reality are obtained is considered here. All these issues are analyzed by means of an evolutionary design system called EDHMoR (Evolutionary Designer of Heterogeneous Modular Robots) that contains all the elements involved in this process. To show practical evidences of the conclusions that have been extracted with this work, two benchmark problems in modular robotics are considered and EDHMoR is tested over them. The first one is focused on solving a linear robot motion mission and the second one on a static task of the robot that does not require displacements.     

多机器人任意队形分布式控制研究

本文针对多智能体协作完成特定任务时难以在全自主控制的前提下协作形成任意队 形和队形向量不易确定的问题，通过由各智能体自主简单的确定自己的队形向量，从理论上 扩展基于队形向量的队形控制原理以生成任意队形，改进机器人的运动方式以提高收敛速度 ，提出一种快速收敛的机器人部队任意队形分布式控制算法．为了解决智能体机器人之间的 冲突问题，提出了一个通信协调模型．仿真实验和实际机器人实验均表明了算法的可行性和 有效性．     

Modules Classification and Automatic Generation of Kinematics on Self-reconfigurable Modular Machines

Self-reconfigurable modular mechanical systems consist of a set of homogeneous units. We analyze the abstract modular structure of self-reconfigurable machines. A total of seven types of cuboid modules and two types of cubic modules have been classified. The concepts of basic group and basic cycle subgroup are proposed. The geometric relationships of each type of modules are derived with group theory. The transformation matrix T _L and other characteristic parameters can be obtained iteratively and simply. For the purpose of automatic generation of the forward kinematics, an approach has been adopted by a series of elemental matrixes multiplication with PME (product of matrix exponentials). The methods used are very general and can be applied easily to other modular robots. Examples of the kinematics for a quadruped and a morphing dual-arm reconfigurable robot are given to demonstrate the applicability and effectiveness of the proposed methods generating the kinematics.     

基于智能体的机器人模型与决策

自重构机器人是一种具有重构功能的自主机器人,机器人可以组成一个团队,团队成员可以通过舍作完成的复杂作业,可以通过重构提高团队的越障能力。为解决自重构鲤器人团队中机器人的实时控制问题,基于多智能体技术,建立了机器人的智能体模型与决策机制。实验表明,该方法既能够满足机器人实时性的要求,又能够实现多机器人协调、合作。     

A distributed and morphology-independent strategy for adaptive locomotion in self-reconfigurable modular robots

In this paper, we present a distributed reinforcement learning strategy for morphology-independent life-long gait learning for modular robots. All modules run identical controllers that locally and independently optimize their action selection based on the robot’s velocity as a global, shared reward signal. We evaluate the strategy experimentally mainly on simulated, but also on physical, modular robots. We find that the strategy: (i) for six of seven configurations (3–12 modules) converge in 96% of the trials to the best known action-based gaits within 15 min, on average, (ii) can be transferred to physical robots with a comparable performance, (iii) can be applied to learn simple gait control tables for both M-TRAN and ATRON robots, (iv) enables an 8-module robot to adapt to faults and changes in its morphology, and (v) can learn gaits for up to 60 module robots but a divergence effect becomes substantial from 20–30 modules. These experiments demonstrate the advantages of a distributed learning strategy for modular robots, such as simplicity in implementation, low resource requirements, morphology independence, reconfigurability, and fault tolerance.     

Design and dock analysis for the interactive module of a lattice-based self-reconfigurable robot

In this paper, a novel, lattice-based self-reconfigurable modular robot is presented. Each module is composed of a cubic part and six rotary sides. There are two holes and two extension pegs on each side. Rotary motion is generated by a motor with a reducer by using cone-shaped gears, clutches and so on. Its quick disconnect/connect mechanism is analyzed. A face-face incidence matrix (FFIM) is proposed to describe the relationship between modules in detail. The states of docking and constraint between modules are analyzed with the geometric method and the contact force of docking is described. Lastly, a self-reconfigurable robot consisting of five similar modules designed to pass the groove in simulation with the proposed motion rules and its FFIM is presented. The results verify that the above analysis is effective.     

A membrane computing framework for self-reconfigurable robots

Self-reconfigurable robots are built by modules which can move in relationship to each other, which allows the robot to change its physical form. Finding a sequence of module moves that reconfigures the robot from the initial configuration to the goal configuration is a hard task and many control algorithms have been proposed. In this paper, we present a novel method which combines a cluster-flow locomotion based on cellular automata together with a decentralized local representation of the spatial geometry based on membrane computing ideas. This new approach has been tested with computer simulations and real-world experiments performed with modular self-reconfigurable robots and represents a new point of view with respect other control methods found in the literature.

     

Dynamic control of coordinated redundant robots with torque optimization

The problem of controlling a system of coordinated redundant robots with torque optimization based on joint redundancy is addressed. Local and global optimal control laws, both minimizing joint torque loading, are developed. A general method of load distribution among the coordinated robots is also proposed. The control problem is to regulate the motion of the object held by the coordinated robots and the internal force generated as a result of constraints on the object. The errors in the object motion and internal force converge asymptotically to zero under the proposed optimal control laws, when exact knowledge of the dynamic models is assumed. Furthermore, the robustness of the proposed method to model uncertainty is also analyzed. The motion and internal force errors are uniformly ultimately bounded under the proposed optimal controllers, when uncertainty in the dynamic models is assumed to exist.     

Dynamic and stable reconfiguration of self-reconfigurable planar parallel robots

This paper discusses dynamic and stable reconfigurations of self-reconfigurable planar parallel robots that can be done by coupling and decoupling of two underactuated robots on a horizontal plane. The limbs of the parallel robots are 2R open kinematic chains with their second joints unactuated. Two types of self-reconfigurable parallel robots are considered. One is formed by two limbs, and the other is by a limb and an underactuated robot consisting of two limbs and a platform. Uncertainty singularities enable them to self-reconfigure without additional actuators at their coupling mechanism. In this paper, we propose dynamic contact motion control to move them from an initial contact configuration to an uncertainty singular configuration while maintaining their contact. This paper also considers dynamic stability at their uncertainty singularities as equilibrium and shows that there exist geometrically stable configurations without feedback control, which are useful for decoupling. Experiments with real robots are carried out to verify the effectiveness of the dynamic contact motion control and stability analysis.     

Concept of self-reconfigurable modular robotic system

We present a concept of novel self-reconfigurable robotic system made of homogeneous autonomous robotic modules. Each robotic module has only two DOF, however a group of this module is able to change its connective configuration by changing their local connections. A cluster of the modules, thus can metamorphose into arbitrary configuration according to the surrounding environment or desired specification. Not only this ability of structural metamorphosis, the combined modules have functionality of robotic system which is capable of generating complicated motion.