检索结果-内蒙古大学图书馆

Bootstrapped Neuro-Simulation as a method of concurrent neuro-evolution and damage recovery

robotics AND AUTONOMOUS SYSTEMS 2020年第0期124卷 103398-000页

作者： Leonard, Brydon A. du Plessis, Mathys C. Woodford, Grant W. Nelson Mandela Univ Dept Comp Sci Port Elizabeth South Africa

Bootstrapped Neuro-Simulation (BNS) is a method of concurrent simulator and robot controller evolution. The algorithm requires little domain knowledge and no pre-investigation data gathering. Additionally, it bridges the reality gap effectively, rapidly evolves functional controllers, and recovers from damage automatically. In this paper, the first evidence of the ability of BNS to evolve closed-loop controllers is shown;in this case to solve a light-following problem. The algorithm is then evaluated for its damage recovery ability for these closed-loop controllers and shown to be very effective, with only minor adaptations. (C) 2019 Elsevier B.V. All rights reserved.

关键词： evolutionary robotics evolutionary computation Machine learning

来源：评论

学校读者我要写书评

暂无评论

Effects of Congestion on Swarm Performance and Autonomous Specialization in Robotic Swarms

引用

JOURNAL OF robotics AND MECHATRONICS 2019年第4期31卷 526-534页

作者： Hiraga, Motoaki Ohkura, Kazuhiro Hiroshima Univ 1-4-1 Kagamiyama Higashihiroshima Hiroshima 7398527 Japan

This paper focuses on the effect of congestion on swarm performance by considering the number of robots and their size. Swarm robotics is the study of a large group of autonomous robots from which collective behavior emerges without reliance on any centralized control. Due to the fact that robotic swarms are composed of a large number of robots, it is important to consider the congestion among them. However, only a few studies have focused on the relationship between the congestion and the performance of robotic swarms;moreover, these studies only discuss the effect of the number of robots. In this study, experiments were conducted by computer simulation and carried out by varying both the number of robots and the size of the robots in a path formation task. The robot controller was designed with an evolutionary robotics approach. The results show that not only the number of robots but also their size are essential features in the relationship between congestion and swarm performance. In addition, autonomous specialization within the robotic swarm emerged in situations with moderate congestion.

关键词： swarm robotics evolutionary robotics congestion autonomous specialization

来源：评论

学校读者我要写书评

暂无评论

Iterative improvement in the automatic modular design of robot swarms

引用

PEERJ COMPUTER SCIENCE 2020年 6卷 e322页

作者： Kuckling, Jonas Stutzle, Thomas Birattari, Mauro Univ Libre Bruxelles IRIDIA Brussels Belgium

Iterative improvement is an optimization technique that finds frequent application in heuristic optimization, but, to the best of our knowledge, has not yet been adopted in the automatic design of control software for robots. In this work, we investigate iterative improvement in the context of the automatic modular design of control software for robot swarms. In particular, we investigate the optimization of two control architectures: finite-state machines and behavior trees. Finite state machines are a common choice for the control architecture in swarm robotics whereas behavior trees have received less attention so far. We compare three different optimization techniques: iterative improvement, Iterated F-race, and a hybridization of Iterated F-race and iterative improvement. For reference, we include in our study also (i) a design method in which behavior trees are optimized via genetic programming and (ii) EvoStick, a yardstick implementation of the neuro-evolutionary swarm robotics approach. The results indicate that iterative improvement is a viable optimization algorithm in the automatic modular design of control software for robot swarms.

关键词： Swarm robotics Automatic design Behavior trees Finite-state machines evolutionary robotics AutoMoDe Iterative improvement Optimization-based design

来源：评论

学校读者我要写书评

暂无评论

Automatic modular design of robot swarms using behavior trees as a control architecture

引用

PEERJ COMPUTER SCIENCE 2020年 6卷 e314页

作者： Ligot, Antoine Kuckling, Jonas Bozhinoski, Darko Birattari, Mauro Univ Libre Bruxelles IRIDIA Brussels Belgium Delft Univ Technol Cognit Robot Delft Netherlands

We investigate the possibilities, challenges, and limitations that arise from the use of behavior trees in the context of the automatic modular design of collective behaviors in swarm robotics. To do so, we introduce Maple, an automatic design method that combines predefined modules-low-level behaviors and conditions-into a behavior tree that encodes the individual behavior of each robot of the swarm. We present three empirical studies based on two missions: AGGREGATION and FORAGING. To explore the strengths and weaknesses of adopting behavior trees as a control architecture, we compare Maple with Chocolate, a previously proposed automatic design method that uses probabilistic finite state machines instead. In the first study, we assess Maple's ability to produce control software that crosses the reality gap satisfactorily. In the second study, we investigate Maple's performance as a function of the design budget, that is, the maximum number of simulation runs that the design process is allowed to perform. In the third study, we explore a number of possible variants of Maple that differ in the constraints imposed on the structure of the behavior trees generated. The results of the three studies indicate that, in the context of swarm robotics, behavior trees might be appealing but in many settings do not produce better solutions than finite state machines.

关键词： Swarm robotics Automatic design Behavior trees Finite state machines evolutionary robotics AutoMoDe Optimisation-based design

来源：评论

学校读者我要写书评

暂无评论

2D-VSR-Sim: A simulation tool for the optimization of 2-D voxel-based soft robots

引用

SOFTWAREX 2020年 12卷

作者： Medvet, Eric Bartoli, Alberto De Lorenzo, Andrea Seriani, Stefano Univ Trieste Dept Engn & Architecture Trieste Italy

Voxel-based soft robots (VSRs) are robots composed of many small, cubic blocks of a soft material with mechanical properties similar to those of living tissues and that can change their volume based on signals emitted by the robot controller, i.e., by its brain. Designing the body and the brain of a VSR suitable for a specific task is a complex activity that requires suitable optimization heuristics. We here present a software, 2D-VSR-Sim, for facilitating research on the optimization of VSRs body and brain. 2D-VSR-Sim, written in Java, provides consistent interfaces for all the VSRs aspects suitable for optimization and considers by design the presence of sensing, i.e., the possibility of exploiting the feedback from the environment for controlling the VSR. We present the mechanical model employed by 2D-VSR-Sim and we experimentally characterize the computational burden of the simulation. Finally, we show how 2D-VSR-Sim can be used to repeat the experiments of significant previous studies and, in perspective, to provide experimental answers to a variety of research questions. (C) 2020 The Author(s). Published by Elsevier B.V.

关键词： evolutionary robotics Soft robotics Optimization Learning

来源：评论

学校读者我要写书评

暂无评论

Evolution of robust high speed optical-flow-based landing for autonomous MAVs

引用

robotics AND AUTONOMOUS SYSTEMS 2020年第0期124卷 103380-000页

作者： Scheper, Kirk Y. W. de Croon, Guido C. H. E. Delft Univ Technol Micro Air Vehicle Lab Aerosp Engn Delft Netherlands

Automatic optimization of robotic behavior has been the long-standing goal of evolutionary robotics. Allowing the problem at hand to be solved by automation often leads to novel approaches and new insights. A common problem encountered with this approach is that when this optimization occurs in a simulated environment, the optimized policies are subject to the reality gap when implemented in the real world. This often results in sub-optimal behavior, if it works at all. This paper investigates the automatic optimization of neurocontrollers to perform quick but safe landing maneuvers for a quadrotor micro air vehicle using the divergence of the optical flow field of a downward looking camera. The optimized policies showed that a piece-wise linear control scheme is more effective than the simple linear scheme commonly used, something not yet considered by human designers. Additionally, we show the utility in using abstraction on the input and output of the controller as a tool to improve the robustness of the optimized policies to the reality gap by testing our policies optimized in simulation on real world vehicles. We tested the neurocontrollers using two different methods to generate and process the visual input, one using a conventional CMOS camera and one a dynamic vision sensor, both of which perform significantly differently than the simulated sensor. The use of the abstracted input resulted in near seamless transfer to the real world with the controllers showing high robustness to a clear reality gap. (C) 2019 Elsevier B.V. All rights reserved.

关键词： evolutionary robotics Bio-inspired landing Reality gap High speed flight

来源：评论

学校读者我要写书评

暂无评论

Design of Powered Floor Systems for Mobile Robots with Differential Evolution 1

引用

22nd International Conference on Applications of evolutionary Computation (EvoApplications) Held as Part of EvoStar Conference

作者： Medvet, Eric Seriani, Stefano Bartoli, Alberto Gallina, Paolo Univ Trieste Dept Engn & Architecture Trieste Italy

ISBN: (数字)9783030166922

ISBN: (纸本)9783030166915;9783030166922

Mobile robots depend on power for performing their task. Powered floor systems, i.e., surfaces with conductive strips alternatively connected to the two poles of a power source, are a practical and effective way for supplying power to robots without interruptions, by means of sliding contacts. Deciding where to place the sliding contacts so as to guarantee that a robot is actually powered irrespective of its position and orientation is a difficult task. We here propose a solution based on Differential Evolution: we formally define problem-specific constraints and objectives and we use them for driving the evolutionary search. We validate experimentally our proposed solution by applying it to three real robots and by studying the impact of the main problem parameters on the effectiveness of the evolved designs for the sliding contacts. The experimental results suggest that our solution may be useful in practice for assisting the design of powered floor systems.

关键词： Multi-objective optimization Automatic design Swarm robotics evolutionary robotics

来源：评论

学校读者我要写书评

暂无评论

Exploring Self-Assembling Behaviors in a Swarm of Bio-micro-robots using Surrogate-Assisted MAP-Elites

Exploring Self-Assembling Behaviors in a Swarm of Bio-micro-...

引用

IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Cazenille, Leo Bredeche, Nicolas Aubert-Kato, Nathanael Ochanomizu Univ Dept Informat Sci Tokyo Japan Sorbonne Univ Inst Syst Intelligents & Robot ISIR CNRS F-75005 Paris France

ISBN: (纸本)9781728124858

Swarms of molecular robots are a promising approach to create specific shapes at the microscopic scale through self-assembly. However, controlling their behavior is a challenging problem as it involves complex non-linear dynamics and high experimental variability. Hand-crafting a molecular controller will often be time-consuming and give sub-optimal results. Optimization methods, like the BIONEAT algorithm, were previously employed to partially overcome these difficulties, but they still had to cope with deceptive high-dimensional search spaces and computationally expensive simulations. Here, we describe a novel approach to solve this problem by using MAP-Elites, an algorithm that searches for both high-performing and diverse solutions. We then apply it to a molecular robotic framework we recently introduced that allows sensing, signaling and self-assembly at the micro-scale and show that MAP-Elites outperforms previous approaches. Additionally, we propose a surrogate model of micro-robots physics and chemical reaction dynamics to reduce the computational costs of simulation. We show that the resulting methodology is capable of optimizing controllers with similar accuracy as when using only a full-fledged realistic model, with half the computational budget.

关键词： Bio-micro-robots swarm robotics molecular programming evolutionary robotics surrogate models quality-diversity algorithms MAP-Elites

来源：评论

学校读者我要写书评

暂无评论

Body Symmetry in Morphologically Evolving Modular Robots 1

引用

22nd International Conference on Applications of evolutionary Computation (EvoApplications) Held as Part of EvoStar Conference

作者： van de Velde, T. Rossi, C. Eiben, A. E. Univ Amsterdam Amsterdam Netherlands UPM Ctr Automat & Robot CSIC Madrid Spain Vrije Univ Amsterdam Amsterdam Netherlands

ISBN: (数字)9783030166922

ISBN: (纸本)9783030166915;9783030166922

Almost all animals natural evolution has produced on Earth have a symmetrical body. In this paper we investigate the evolution of body symmetry in an artificial system where robots evolve. To this end, we define several measures to quantify symmetry in modular robots and see how these relate to fitness that corresponds to a locomotion task. We find that, although there is only a weak correlation between symmetry and fitness over the course of a single evolutionary run, there is a positive correlation between the level of symmetry and maximum fitness when a set of runs is taken into account.

关键词： evolutionary robotics Modular robots Symmetry

来源：评论

学校读者我要写书评

暂无评论

Autonomous skill discovery with Quality-Diversity and Unsupervised Descriptors

Autonomous skill discovery with Quality-Diversity and Unsupe...

引用

Genetic and evolutionary Computation Conference (GECCO)

作者： Cully, Antoine Imperial Coll London London England

ISBN: (纸本)9781450361118

Quality-Diversity optimization is a new family of optimization algorithms that, instead of searching for a single optimal solution to solving a task, searches for a large collection of solutions that all solve the task in a different way. This approach is particularly promising for learning behavioral repertoires in robotics, as such a diversity of behaviors enables robots to be more versatile and resilient. However, these algorithms require the user to manually define behavioral descriptors, which is used to determine whether two solutions are different or similar. The choice of a behavioral descriptor is crucial, as it completely changes the solution types that the algorithm derives. In this paper, we introduce a new method to automatically define this descriptor by combining Quality-Diversity algorithms with unsupervised dimensionality reduction algorithms. This approach enables robots to autonomously discover the range of their capabilities while interacting with their environment. The results from two experimental scenarios demonstrate that robot can autonomously discover a large range of possible behaviors, without any prior knowledge about their morphology and environment. Furthermore, these behaviors are deemed to be similar to handcrafted solutions that uses domain knowledge and significantly more diverse than when using existing unsupervised methods.

关键词： Behavioral repertoires Quality-diversity optimization evolutionary robotics Deep Learning Representation Learning Auto-encoders

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：