Evolvable Hardware in Evolutionary Robotics

In recent decades the research on Evolutionary Robotics (ER) has developed rapidly. This direction is primarily concerned with the use of evolutionary computing techniques in the design of intelligent and adaptive controllers for robots. Meanwhile, much attention has been paid to a new set of integrated circuits named Evolvable Hardware (EHW), which is capable of reconfiguring its architectures unlimited time based on artificial evolution techniques. This paper surveys the application of evolvable hardware in evolutionary robotics. The evolvable hardware is an emerging research field concerning the development of evolvable robot controllers at the hardware level to adapt to dynamic changes in environments. The context of evolvable hardware and evolutionary robotics is reviewed, and a few representative experiments in the field of robotic hardware evolution are presented. As an alternative to conventional robotic controller designs, the potentialities and limitations of the EHW-based robotic system are discussed and summarized.     

Concurrent controller and Simulator Neural Network development for a differentially-steered robot in Evolutionary Robotics

Evolutionary Robotics (ER) strives for the automatic creation of robotic controllers and morphologies. The ER process is normally performed in simulation in order to reduce the time required and robot wear. Simulator development is a time consuming process which requires expert knowledge and must traditionally be completed before the ER process can commence. Traditional simulators have limited accuracy, can be computationally expensive and typically do not account for minor operational differences between physical robots.This research proposes the automatic creation of simulators concurrently with the normal ER process. The simulator is derived from an Artificial Neural Network (ANN) to remove the need for formulating an analytical model for the robot. The ANN simulator is improved concurrently with the ER process through real-world controller evaluations which continuously generate behavioural data. Simultaneously, the ER process is informed by the improving simulator to evolve better controllers which are periodically evaluated in the real-world. Hence, the concurrent processes provide further targeted behavioural data for simulator improvement.The concurrent and real-time creation of both controllers and ANN-based simulators is successfully demonstrated for a differentially-steered mobile robot. Various parameter settings in the proposed algorithm are investigated to determine factors pertinent to the success of the proposed approach.     

Task Modelling in Collective Robotics

Does coherent collective behaviour require an explicit mechanism of cooperation? In this paper, we demonstrate that a certain class of cooperative tasks, namely coordinated box manipulation, are possible without explicit communication or cooperation mechanisms. The approach relies on subtask decomposition and sensor preprocessing. A framework is proposed for modelling multi-robot tasks which are described as a series of steps with each step possibly consisting of substeps. Finite state automata theory is used to model steps with state transitions specified as binary sensing predicates called perceptual cues. A perceptual cue (Q), whose computation is disjoint from the operation of the automata, is processed by a 3-level finite state machine called a Q-machine. The model is based on entomological evidence that suggests local stimulus cues are used to regulate a linear series of building acts in nest construction. The approach is designed for a redundant set of homogeneous mobile robots, and described is an extension of a previous system of 5 box-pushing robots to 11 identical transport robots. Results are presented for a system of physical robots capable of moving a heavy object collectively to an arbitrarily specified goal position. The contribution is a simple task-programming paradigm for mobile multi-robot systems. It is argued that Q-machines and their perceptual cues offer a new approach to environment-specific task modelling in collective robotics.     

Evolutionary learning of a fuzzy controller for wall-following behavior in mobile robotics

The design of fuzzy controllers for the implementation of behaviors in mobile robotics is a complex and highly time-consuming task. The use of machine learning techniques such as evolutionary algorithms or artificial neural networks for the learning of these controllers allows to automate the design process. In this paper, the automated design of a fuzzy controller using genetic algorithms for the implementation of the wall-following behavior in a mobile robot is described. The algorithm is based on the iterative rule learning approach, and is characterized by three main points. First, learning has no restrictions neither in the number of membership functions, nor in their values. In the second place, the training set is composed of a set of examples uniformly distributed along the universe of discourse of the variables. This warrantees that the quality of the learned behavior does not depend on the environment, and also that the robot will be capable to face different situations. Finally, the trade off between the number of rules and the quality/accuracy of the controller can be adjusted selecting the value of a parameter. Once the knowledge base has been learned, a process for its reduction and tuning is applied, increasing the cooperation between rules and reducing its number.     

Cooperative Mobile Robotics: Antecedents and Directions

There has been increased research interest in systems composed of multiple autonomous mobile robots exhibiting cooperative behavior. Groups of mobile robots are constructed, with an aim to studying such issues as group architecture, resource conflict, origin of cooperation, learning, and geometric problems. As yet, few applications of cooperative robotics have been reported, and supporting theory is still in its formative stages. In this paper, we give a critical survey of existing works and discuss open problems in this field, emphasizing the various theoretical issues that arise in the study of cooperative robotics. We describe the intellectual heritages that have guided early research, as well as possible additions to the set of existing motivations.     

Biologically inspired decision-making in the robot platform MAMoRo

An important step in coming near to building machines with artificial intelligence is by studying and understanding how the human brain works, then applying this knowledge to build machines that “think” using the same concept. MAMoRo (modular autonomous mobile robot) is a general-purpose robot platform targeted at teaching and research in the academia. It consists of three modules: power and motion module, control module, and intelligence module. The decision unit of MAMoRo is distributed into two modules: the control module, which is equipped with a low-cost microcontroller, and handles low-level hardware functions, and the intelligence module, which is equipped with a field-programmable gate array (FPGA) and handles high-level functions. This model of distribution was inspired by the anatomy of the human brain and brings with it many advantages. To prove the concept, MAMoRo was tested with a practical application. This work was presented in part at the First European Workshop on Artificial Life and Robotics, Vienna, Austria, July 12–13, 2007     

金融生态四元主体的演化博弈与仿真研究

针对自主创新的融资困境及生态体系的缺位与失调,为解决上述问题,提出优化方案。运用演化博弈理论和遗传基因算法,对金融生态的四元主体之间的自适应自学习进行理论分析,利用Repast仿真建模平台对金融生态主体在创新策略上的演变过程进行仿真分析。实验结果表明自主创新内在的风险特性与错位的融资机制使之陷入低水平锁定。最后指出在金融生态四元主体的良性循环与互惠互动过程中稳步提升金融生态促进自主创新的功能具有战略意义,应以各主体的创新策略的演化递进来持续推进企业自主创新的可持续性发展。通过仿真工具提供了解决方案的依据。     

Reconfigurable swarm robots produce self-assembling and self-repairing organisms

Reconfigurable robots are set to become a vital factor in the theoretical development and practical utilization of robotics. The core problem in this scientific area is steady information transfer between a swarm and its organisms and vice versa. To this end, we present a basic theoretical framework that stipulates the interoperation between the two modes. We evaluate our proposed framework by constructing 100 mobile microrobots of three different types that initiate the processes of self-reconfigurability and self-repair. The autonomous decision to self-aggregate to an organism mainly derives from the necessity to overcome existing obstructive environmental conditions, e.g. ramps or clefts. The methodological dichotomy that we have chosen to evaluate our concept was to pursue in parallel an approach based on embodied distributed cognition and an evolutionary path mainly based on artificial genomes and reproduction. In this paper, we evaluate these two different approaches in two distinct grand challenges and present the main results.     

Evolutionary control of bipedal locomotion in a high degree-of-freedom humanoid robot: first steps

This article describes a methodology, together with an associated series of experiments employing this methodology, for the evolution of walking behavior in a simulated humanoid robot with up to 20 degrees of freedom. The robots evolved in this study learn to walk smoothly in an upright or near-upright position and demonstrate a variety of different locomotive behaviors, including “skating,” “limping,” and walking in a manner curiously reminiscent of a mildly or heavily intoxicated person. A previous study demonstrated the possible potential utility of this approach while evolving controllers based on simulated humanoid robots with a restricted range of movements. Although walking behaviors were developed, these were slow and relied on the robot walking in an excessively stooped position, similar to the gait of an infirm elderly person. This article extends the previous work to a robot with many degrees of freedom, up to 20 in total (arms, elbows, legs, hips, knees, etc.), and demonstrates the automatic evolution of fully upright bipedal locomotion in a humanoid robot using an accurate physics simulator. This work was presented in part at the 11th International Symposium on Artificial Life and Robotics, Oita, Japan, January 23–25, 2006     

Simulation and control of distributed robot search teams

This article describes the simulation of distributed autonomous robots for search and rescue operations. The simulation system is utilized to perform experiments with various control strategies for the robot team and team organizations, evaluating the comparative performance of the strategies and organizations. The objective of the robot team is to, once deployed in an environment (floor-plan) with multiple rooms, cover as many rooms as possible. The simulated robots are capable of navigation through the environment, and can communicate using simple messages. The simulator maintains the world, provides each robot with sensory information, and carries out the actions of the robots. The simulator keeps track of the rooms visited by robots and the elapsed time, in order to evaluate the performance of the robot teams. The robot teams are composed of homogenous robots, i.e., identical control strategies are used to generate the behavior of each robot in the team. The ability to deploy autonomous robots, as opposed to humans, in hazardous search and rescue missions could provide immeasurable benefits.     

情景教学法在平面设计中的应用

本文应用情景教学法,提高学生平面设计能力。     

虚拟现实与系统仿真

详细讨论了系统仿真、多媒体与虚拟现实的基本概念及它们之间的关系,指出在仿真中如何及何时应用这些技术,并提出了狭义多媒体仿真、ＶＲ仿真系统的概念,对仿真实践具有一定的参考价值。     

Review of the International Symposium on Artificial Life and Robotics (AROB)

In this article, we review the growth of AROB conferences, and assess the various contributions, which have covered digital life, the artificial brain, artificial society, and evolutionary robots, etc. We analyze the relations between artificial life, complexity, and intelligent robots, and finally give our view of the expected future of AROB. This work was presented in part at the 5th International Symposium on Artificial Life and Robotics, Oita, Japan, January 26–28, 2000     

装备作战仿真中的虚拟现实及可视化研究

该文分析了装备作战研究的现状,重点提出了装备作战仿真的定义,及其用装备作战仿真方法通过研究作战过程、作战结果,进行装备作战、装备使用及相关问题研究,本文结合实际工作经验,从虚拟战场环境、实体建模、分布式网络等方面研究分析了装备作战仿真中虚拟现实及可视化技术有待研究、发展和解决的一些关键技术问题。     

日本高校机器人学科建设经验

在机器人学科建设方面,日本高校较早建立了成熟的培养理念,并制定了合理完善的课程体系。比较分析了日本多所高校机器人专业的课程设置内容及特点,并总结了课程设置和相关应用领域的关系,可供我们在学科建设中借鉴。     

无人机虚拟现实仿真的设计与实现

讨论了在PC机上实现无人机虚拟现实仿真的系统结构、实现平台、模型和仿真数据库的组织实现方法。讨论了图形仿真各个子模块的实现方法,交解决了无人机模型的位姿自动校准问题。     

Human Interaction Dynamics for Its Use in Mobile Robotics:Impedance Control for Leader-follower Formation

A complete characterization of the behavior in human-robot interactions (HRI) includes both:the behavioral dynamics and the control laws that characterize how the behavior is regulated with the perception data. In this way, this work proposes a leader-follower coordinate control based on an impedance control that allows to establish a dynamic relation between social forces and motion error. For this, a scheme is presented to identify the impedance based on fictitious social forces, which are described by distance-based potential fields. As part of the validation procedure, we present an experimental comparison to select the better of two different fictitious force structures. The criteria are determined by two qualities:least impedance errors during the validation procedure and least parameter variance during the recursive estimation procedure. Finally, with the best fictitious force and its identified impedance, an impedance control is designed for a mobile robot Pioneer 3AT, which is programmed to follow a human in a structured scenario. According to results, and under the hypothesis that moving like humans will be acceptable by humans, it is believed that the proposed control improves the social acceptance of the robot for this kind of interaction.      

分布式虚拟现实及其在仿真中的应用

分布式虚拟现实技术是虚拟现实和计算机网络技术相结合的产物,其可以突破时空限制的显著特点,为人们分析问题、解决问题,以及协同工作提供了崭新的手段,是目前倍受国际科学界和工程界关注的前沿技术,它已非常成功地应用于军事、航天、医疗和娱乐领域,并在教育培训,工程设计与制造,电子商务等方面显示出了良好的应用前景,该文介绍了分布式虚拟现实的概念、特征、涉及的关键技术和应用领域,综述了它的产生,发展及存在的问题,最后对其进一步发展提出了几点思考。     

Improved control and simulation models of a tricycle collaborative robot

The objective of collaborative manufacturing is to create the synergism from the collaboration of manufacturing resources. Most of the studied collaborations are made among intelligent machines; however, the collaboration can be realized even between machines and human being, and a collaborative robot (Cobot) belongs to the latter. A cobot is a robot designed to assist human beings as a guide or assistor in a constrained motion. Various prototypes have been developed and the potentials of these robots have been demonstrated. The research presented in this paper focuses on the control and simulation models of a tricycle cobot with three steered wheels, with the following two contributions: (i) A concise model for the closed-loop control is developed. Existing closed-loop control has been implemented in an intuitive way, and some control parameters have to be determined by a trial-and-error method. (ii) A simulation model is proposed to validate the control algorithms. No simulation model is available and the control models of other existing systems have to be validated experimentally. The developed control and simulation models have been implemented. Graphic simulation is also developed. Case studies are provided and the simulation results are analyzed.