检索结果-内蒙古大学图书馆

IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Bhardwaj, Akanksha Kala, Rahul Indian Inst Informat Technol Robot & Machine Intelligence Lab Allahabad Prayagraj India

ISBN: (纸本)9781728121536

Robot mission planning deals with accomplishing a mission that requires the robot to visit a set of sites and carrying out specific tasks at each site, consisting of Boolean and temporal constraints. The Boolean constraints are like "Visit any one of the three coffee machines" and "Visit any two of the three instructors",while the temporal constraints typically ask the robot to carry some sub-missions in a sequential order and some others in parallel. Each mission can have a different number of sites and nature of constraints involved. Each mission is expressed in a generalized form using OR, AND, and THEN clauses. Further,the notion of sensors is added, wherein a resource for a mission may be unavailable and therefore the robot will have to plan an alternate (maybe longer) strategy, however the availability will only be known when the robot physically visits the mission site. The typical mechanisms to solve for such mission using formal languages and verification engines incur an exponential complexity. Therefore, this paper proposes an evolutionary algorithm to solve the problem, enabling the use of probabilistic optimality properties. However, calculating the expected path length with the prior probabilities of the sensor is also exponential complexity which causes the resulting algorithm to have an exponential complexity in terms of the number of sensors. A greedy heuristic cost function is devised that imitates human decision making, wherein the robot tries to solve the mission with the shortest length as well as maximize the probability of completion of the task. Further, each site is assumed to have an associated waiting time which is the expected time for which the robot will have to wait in the queue till the resource is available for use. Another greedy heuristic is used to align the sites in the crossover operation to approach a better solution faster. Experimental results confirm that the proposed solution performs well as compared to baseline optimistic

关键词： mission planning evolutionary robotics evolutionary computation robot motion planning sensor-based planning policysearch

来源：评论

学校读者我要写书评

暂无评论

evolutionary motion design for humanoid robots 06

Evolutionary motion design for humanoid robots

引用

8th Annual Genetic and evolutionary Computation Conference

作者： Yanase, Toshihiko Iba, Hitoshi Univ Tokyo Dept Frontier Informat Chiba 2778561 Japan

ISBN: (纸本)9781595931863

We propose a new approach to generating the motion of humanoid robots intuitively by means of Interactive evolutionary Computation (IEC). In our system, novice users are able to design effective motions through the subjective evaluation of displayed individuals, even if they do not have any technical knowledge. The motions evolved by the IEC system are not necessarily stable nor feasible in real environments. Thus, appropriate adjustments are required to revise the motions. For this purpose, we use a real-valued CA in a dynamic simulator. We empirically show the effectiveness of our approach by designing a kick motion for a humanoid robot.

关键词： humanoid robot motion design multi-objective CA evolutionary robotics interactive evolutionary computation

来源：评论

学校读者我要写书评

暂无评论

evolutionary Optimization of Self-Assembly in a Swarm of Bio-micro-robots 17

Evolutionary Optimization of Self-Assembly in a Swarm of Bio...

引用

Genetic and evolutionary Computation Conference (GECCO)

作者： Aubert-Kato, Nathanael Fosseprez, Charles Gines, Guillaume Kawamata, Ibuki Dinh, Huy Cazenille, Leo Estevez-Tores, Andre Hagiya, Masami Rondelez, Yannick Bredeche, Nicolas Tokyo Inst Technol ELSI Tokyo Japan Ochanomizu Univ Tokyo Japan CNRS ESPCI CBI UMR 8231 Paris France MNHN Paris France CNRS ESPCI UMR 7083 Gulliver Paris France Tohoku Univ Sendai Miyagi Japan Playnext Lab Inc Tokyo Japan Sorbonne Paris Cite Paris France Sorbonne Univ CNRS Paris France UPMC Univ Paris 06 Sorbonne Univ CNRS LJP F-75005 Paris France Univ Tokyo Tokyo Japan UPMC Univ Paris 06 Sorbonne Univ CNRS ISIR F-75005 Paris France

ISBN: (纸本)9781450349208

This paper deals with the programmability of a swarm of bio-microrobots in order to display self-assembling behaviors into specific shapes. We consider robots that are DNA-functionalized microbeads capable of sensing and expressing signals as well as self-assembling. We describe an in vitro experimentation with a million of micro-beads conditionally aggregating into clusters. Using a realistic simulation, we then address the question of how to automatically design the reaction networks that define the micro-robots' behavior, to self-assemble into a specific shape at a specific location. We use bioNEAT, an instantiation of the famous NEAT algorithm capable of handling chemical reaction networks, and CMA-ES to optimize the behavior of each micro-bead. As in swarm robotics, each micro-bead shares the same behavioral rules and the general outcome depends on interactions between neighbors and with the environment. Results obtained on four different target functions show that solutions optimized with evolutionary algorithms display efficient self-assembling behaviors, improving over pure hand-designed networks provided by an expert after a week-long trials and errors search. In addition, we show that evolved solutions are able to self-repair after damage, which is a critical property for smart materials.

关键词： Bio-micro-robots molecular programming evolutionary robotics swarm robotics collective behavior self-assembly sef-repair

来源：评论

学校读者我要写书评

暂无评论

Engineering evolutionary Control for Real-world Robotic Systems

Engineering Evolutionary Control for Real-world Robotic Syst...

引用

作者： Miguel Antonio Frade Duarte ISCTE-Instituto Universitario de Lisboa

学位级别：博士

evolutionary robotics (ER) is the field of study concerned with the appli- cation of evolutionary computation to the design of robotic systems. Two main issues have prevented ER from being applied to real-world tasks, namely scaling to complex tasks and the transfer of control to real-robot systems. Finding solutions to complex tasks is challenging for evolutionary approaches due to the bootstrap problem and deception. When the task goal is too difficult, the evolutionary pro- cess will drift in regions of the search space with equally low levels of performance and therefore fail to bootstrap. Furthermore, the search space tends to get rugged (deceptive) as task complexity increases, which can lead to premature convergence. Another prominent issue in ER is the reality gap. Behavioral control is typically evolved in simulation and then only transferred to the real robotic hardware when a good solution has been found. Since simulation is an abstraction of the real world, the accuracy of the robot model and its interactions with the environment is limited. As a result, control evolved in a simulator tends to display a lower performance in reality than in simulation. In this thesis, we present a hierarchical control synthesis approach that enables the use of ER techniques for complex tasks in real robotic hardware by mitigating the bootstrap problem, deception, and the reality gap. We recursively decom- pose a task into sub-tasks, and synthesize control for each sub-task. The individ- ual behaviors are then composed hierarchically. The possibility of incrementally transferring control as the controller is composed allows transferability issues to be addressed locally in the controller hierarchy. Our approach features hybridity, allowing different control synthesis techniques to be combined. We demonstrate our approach in a series of tasks that go beyond the complexity of tasks where ER has been successfully applied. We further show that hierarchical control can be ap- p

关键词： evolutionary robotics hierarchical control systems hybrid control swarm robotics multirobot systems artificial neural networks

来源：评论

学校读者我要写书评

暂无评论

evolutionary Planning for Multi-User Multi-Task Missions

Evolutionary Planning for Multi-User Multi-Task Missions

引用

IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Kala, Rahul Indian Inst Informat Technol Robot & Machine Intelligence Lab Allahabad Prayagraj India

ISBN: (纸本)9781728121536

The problem of mission planning is to enable robots solve complex missions with Boolean and temporal operators in the mission specification. Typically, missions are specified using a Linear Temporal Logic Formulation and solved by using a model verification approach which has an exponential complexity. Given a language which is polynomial verifiable, evolutionary paradigm of mission planning can enable probabilistic optimality and probabilistic completeness, thus enabling the use of solvers for a very high number of variables, which is impossible to do using the model verification techniques. This paper motivates the heuristic of a mission consisting of a number of tasks, such that each task is a complex instruction given by a user, while many such users share a robot. The heuristic is used to generate a near-optimal solution of tasks that can then be fused optimally by a Dynamic Programming approach to make the solution of the mission. However, the problem is not decomposable and optimal solutions of tasks do not result in an optimal solution to mission. Hence a 2-step algorithm is used. The first step computes a near-optimal solution of the tasks. The second step does a full Genetic Algorithm search to generate task solutions that eventually fused by a Dynamic Programming approach produce an optimal mission solution. Comparative analysis is done with numerous baselines and the proposed approach is experimentally shown to perform better than all baselines. The experiments are also done on the Pioneer LX robot using the Robot Operating System framework.

关键词： mission planning robot motion planning evolutionary robotics evolutionary computation memetic computation

来源：评论

学校读者我要写书评

暂无评论

Developing evolutionary neural controllers for teams of mobile robots playing a complex game

Developing evolutionary neural controllers for teams of mobi...

引用

IEEE International Conference on Information Reuse and Integration (IRI-2003)

作者： Nelson, AL Grant, E Lee, G N Carolina State Univ Dept Elect & Comp Engn Ctr Robot & Intelligent Machines Raleigh NC 27695 USA

ISBN: (纸本)0780382420

This research develops methods of automating the production of behavioral robotics controllers. Population-based artificial evolution was employed to train neural network-based controllers to play a robotic version of the team game Capture the Flag. The robot agents used processed video data for sensing their environment. To accommodate the 35 to 150 sensor inputs required, large neural networks of arbitrary connectivity and structure were evolved. An intra-population competitive genetic algorithm was used and selection at each generation was based on whether the different controllers won or lost games over the course of a tournament. This paper focuses on the evolutionary neural controller architecture. Evolved controllers were tested in a series of competitive games and transferred to real robots for physical verification.

关键词： evolutionary robotics robot colonies neural networks evolutionary neural computing behavioral robotics

来源：评论

学校读者我要写书评

暂无评论

evolutionary Predator-prey Robot Systems: from simulation to real world 19

Evolutionary Predator-prey Robot Systems: from simulation to...

引用

Genetic and evolutionary Computation Conference (GECCO)

作者： Lan, Gongjin Chen, Jiunhan Eiben, A. E. Vrije Univ Amsterdam Amsterdam Netherlands Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9781450367486

We present a feasibility study on evolving controllers for a group of wheeled robot predators that need to capture a prey robot. Our solution method works by evolving controllers in simulation for 100 generations, followed by 10 generations on real robots. The best controllers are further evaluated by their sensitivity for the initial positions. The results demonstrate the practical feasibility of this approach and give an indication of the time required to develop good solutions for the predator-prey problem.

关键词： evolutionary robotics Predator-prey system Reality Gap

来源：评论

学校读者我要写书评

暂无评论

Evolving robot brains and bodies together: An experimental investigation

引用

JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 2003年第2期26卷 125-132页

作者： Lee, WP Natl Pingtung Univ Sci & Technol Dept Management Informat Syst Pingtung 912 Taiwan

In recent years, evolutionary algorithms have been proposed to synthesize robot controllers. However, admittedly, it is not satisfactory just to evolve the control system, because the performance of the control system depends on other hardware parameters, the robot body plan. In this paper, an evolutionary framework is presented to evolve complete robot systems, including controllers and bodies, to achieve fitness-specified tasks. In order to assess the performance of the developed system, we use it to evolve controller and body plan together for a robot in a simulated environment to achieve an obstacle avoidance task. Experimental results show the promise of this hybrid evolutionary approach. In addition, the importance of simultaneously evolving controller and morphology is emphasized, and the distribution of body parameters in the morphological space is also studied.

关键词： evolutionary computation autonomous robots artificial life evolutionary robotics

来源：评论

学校读者我要写书评

暂无评论

Accelerating Self-Modeling in Cooperative Robot Teams

引用

IEEE TRANSACTIONS ON evolutionary COMPUTATION 2009年第2期13卷 321-332页

作者： Bongard, Josh C. Univ Vermont Dept Comp Sci Burlington VT 05405 USA

One of the major obstacles to achieving robots capable of operating in real-world environments is enabling them to cope with a continuous stream of unanticipated situations. In previous work, it was demonstrated that a robot can autonomously generate self-models, and use those self-models to diagnose unanticipated morphological change such as damage. In this paper, it is shown that multiple physical quadrupedal robots with similar morphologies can share self-models in order to accelerate modeling. Further, it is demonstrated that quadrupedal robots which maintain separate self-modeling algorithms but swap self-models perform better than quadrupedal robots that rely on a shared self-modeling algorithm. This finding points the way toward more robust robot teams: it robot can diagnose and recover from unanticipated situations faster by drawing on the previous experiences of the other robots.

关键词： Collective robotics evolutionary robotics self-modeling

来源：评论

学校读者我要写书评

暂无评论

Evolving self-organizing behaviors for a swarm-bot

引用

AUTONOMOUS ROBOTS 2004年第2-3期17卷 223-245页

作者： Dorigo, M Trianni, V Sahin, E Gross, R Labella, TH Baldassarre, G Nolfi, S Deneubourg, JL Mondada, F Floreano, D Gambardella, LM Free Univ Brussels IRIDIA Brussels Belgium Middle E Tech Univ Dept Comp Engn KOVAN TR-06531 Ankara Turkey CNR Inst Cognit Sci & Technol Rome Italy Free Univ Brussels CENOLI B-1050 Brussels Belgium Ecole Polytech Fed Lausanne Autonomous Syst Lab CH-1015 Lausanne Switzerland IDSIA USI SUPSI Manno Lugano Switzerland

In this paper, we introduce a self-assembling and self-organizing artifact, called a swarm-bot, composed of a swarm of s-bots, mobile robots with the ability to connect to and to disconnect from each other. We discuss the challenges involved in controlling a swarm-bot and address the problem of synthesizing controllers for the swarm-bot using artificial evolution. Specifically, we study aggregation and coordinated motion of the swarm-bot using a physics-based simulation of the system. Experiments, using a simplified simulation model of the s-bots, show that evolution can discover simple but effective controllers for both the aggregation and the coordinated motion of the swarm-bot. Analysis of the evolved controllers shows that they have properties of scalability, that is, they continue to be effective for larger group sizes, and of generality, that is, they produce similar behaviors for configurations different from those they were originally evolved for. The portability of the evolved controllers to real s-bots is tested using a detailed simulation model which has been validated against the real s-bots in a companion paper in this same special issue.

关键词： swarm robotics swarm intelligence swarm-bot evolutionary robotics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：