Swarm Intelligence最新文献_第8页

A study on force-based collaboration in swarms 基于力的群体协作研究

IF 2.6 4区计算机科学

Swarm Intelligence Pub Date : 2019-11-11 DOI: 10.1007/s11721-019-00178-7

Chiara Gabellieri, Marco Tognon, Dario Sanalitro, Lucia Pallottino, Antonio Franchi

{"title":"A study on force-based collaboration in swarms","authors":"Chiara Gabellieri, Marco Tognon, Dario Sanalitro, Lucia Pallottino, Antonio Franchi","doi":"10.1007/s11721-019-00178-7","DOIUrl":"https://doi.org/10.1007/s11721-019-00178-7","url":null,"abstract":"Cooperative manipulation is a basic skill in groups of humans, animals and in many robotic applications. Besides being an interesting challenge, communication-less approaches have been applied to groups of robots in order to achieve higher scalability and simpler hardware and software design. We present a generic model and control law for robots cooperatively manipulating an object, for both ground and floating systems. The control method exploits a leader–follower scheme and is based only on implicit communication (i.e., the sensing of contact forces). The control objective mainly consists of steering the object manipulated by the swarm of robots to a desired position and orientation in a cooperative way. For a system with just one leader, we present analytical results on the equilibrium configurations and their stability that are then validated by numerical simulations. The role of object internal forces (induced by the robots through contact forces) is discussed in terms of convergence of the object position and orientation to the desired values. We also present a discussion on additional properties of the controlled system that were investigated using thorough numerical analysis, namely the robustness of the system when the object is subject to external disturbances in non-ideal conditions, and how the number of leaders in the swarm can affect the aforementioned convergence and robustness.","PeriodicalId":51284,"journal":{"name":"Swarm Intelligence","volume":"642 1","pages":"1 - 26"},"PeriodicalIF":2.6,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138538390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20

ANTS 2018 special issue: Editorial 蚂蚁2018特刊:社论

IF 2.6 4区计算机科学

Swarm Intelligence Pub Date : 2019-10-28 DOI: 10.1007/s11721-019-00177-8

M. Dorigo, M. Birattari, C. Blum, A. Christensen, A. Reina, V. Trianni

引用次数: 0

Sophisticated collective foraging with minimalist agents: a swarm robotics test 用极简代理进行复杂的集体觅食:群体机器人测试

IF 2.6 4区计算机科学

Swarm Intelligence Pub Date : 2019-10-10 DOI: 10.1007/s11721-019-00176-9

Mohamed S. Talamali, Thomas Bose, Matthew Haire, Xu Xu, James A. R. Marshall, Andreagiovanni Reina

{"title":"Sophisticated collective foraging with minimalist agents: a swarm robotics test","authors":"Mohamed S. Talamali, Thomas Bose, Matthew Haire, Xu Xu, James A. R. Marshall, Andreagiovanni Reina","doi":"10.1007/s11721-019-00176-9","DOIUrl":"https://doi.org/10.1007/s11721-019-00176-9","url":null,"abstract":"How groups of cooperative foragers can achieve efficient and robust collective foraging is of interest both to biologists studying social insects and engineers designing swarm robotics systems. Of particular interest are distance-quality trade-offs and swarm-size-dependent foraging strategies. Here, we present a collective foraging system based on virtual pheromones, tested in simulation and in swarms of up to 200 physical robots. Our individual agent controllers are highly simplified, as they are based on binary pheromone sensors. Despite being simple, our individual controllers are able to reproduce classical foraging experiments conducted with more capable real ants that sense pheromone concentration and follow its gradient. One key feature of our controllers is a control parameter which balances the trade-off between distance selectivity and quality selectivity of individual foragers. We construct an optimal foraging theory model that accounts for distance and quality of resources, as well as overcrowding, and predicts a swarm-size-dependent strategy. We test swarms implementing our controllers against our optimality model and find that, for moderate swarm sizes, they can be parameterised to approximate the optimal foraging strategy. This study demonstrates the sufficiency of simple individual agent rules to generate sophisticated collective foraging behaviour.","PeriodicalId":51284,"journal":{"name":"Swarm Intelligence","volume":"21 1","pages":"1 - 32"},"PeriodicalIF":2.6,"publicationDate":"2019-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138538389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 43

Emergent naming conventions in a foraging robot swarm 觅食机器人群中的紧急命名约定

IF 2.6 4区计算机科学

Swarm Intelligence Pub Date : 2019-10-05 DOI: 10.1007/s11721-022-00212-1

Roman Miletitch, A. Reina, M. Dorigo, V. Trianni

引用次数: 1

Simulation-only experiments to mimic the effects of the reality gap in the automatic design of robot swarms 仅通过仿真实验来模拟现实差距对机器人群自动设计的影响

IF 2.6 4区计算机科学

Swarm Intelligence Pub Date : 2019-10-03 DOI: 10.1007/s11721-019-00175-w

Antoine Ligot, Mauro Birattari

{"title":"Simulation-only experiments to mimic the effects of the reality gap in the automatic design of robot swarms","authors":"Antoine Ligot, Mauro Birattari","doi":"10.1007/s11721-019-00175-w","DOIUrl":"https://doi.org/10.1007/s11721-019-00175-w","url":null,"abstract":"The reality gap—the discrepancy between reality and simulation—is a critical issue in the off-line automatic design of control software for robot swarms, as well as for single robots. It is understood that the reality gap manifests itself as a drop in performance: when control software generated in simulation is ported to physical robots, the performance observed is often disappointing compared with the one obtained in simulation. In this paper, we investigate whether, to observe the effects of the reality gap, it is necessary to assume that the control software is designed in a context that is simpler than the one in which it is evaluated. In the first experiment, we show that a performance drop may be observed also in an artificial, simulation-only reality gap: control software is generated on the basis of a simulation model and assessed on a second one. We will call this second model a <i>pseudo-reality</i>. We selected the simulation model to be used as a pseudo-reality by trial and error, so as to qualitatively replicate previously published observations made in experiments with physical robots. The results show that a performance drop occurs even if we can exclude that pseudo-reality is more complex than the simulation model used for the design. In the second experiment, we eliminate the trial-and-error selection of the first experiment by evaluating control software across multiple pseudo-realities, which are sampled around the original simulation model used for the design. The results of the second experiment confirm those of the first one and show that they do not depend on the specific pseudo-reality we previously selected by trial and error. Moreover, they suggest that one could use multiple pseudo-realities to evaluate automatic design methods and, from this simulation-only evaluation, infer their robustness to the reality gap.","PeriodicalId":51284,"journal":{"name":"Swarm Intelligence","volume":"75 1","pages":"1 - 24"},"PeriodicalIF":2.6,"publicationDate":"2019-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138538386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 33

Coherent collective behaviour emerging from decentralised balancing of social feedback and noise 社会反馈和噪音的分散平衡产生了一致的集体行为

IF 2.6 4区计算机科学

Swarm Intelligence Pub Date : 2019-09-04 DOI: 10.1007/s11721-019-00173-y

I. Rausch, A. Reina, P. Simoens, Yara Khaluf

引用次数: 30

Toward a theory of collective resource distribution: a study of a dynamic morphogenesis controller 走向集体资源分配理论：一个动态形态发生控制器的研究

IF 2.6 4区计算机科学

Swarm Intelligence Pub Date : 2019-08-29 DOI: 10.1007/s11721-019-00174-x

Payam Zahadat, D. Hofstadler

引用次数: 9

The PageRank algorithm as a method to optimize swarm behavior through local analysis PageRank算法作为一种通过局部分析来优化群体行为的方法

IF 2.6 4区计算机科学

Swarm Intelligence Pub Date : 2019-08-23 DOI: 10.1007/s11721-019-00172-z

M. Coppola, Jian Guo, E. Gill, G. C. H. E. de Croon

引用次数: 10

Multi-guide particle swarm optimization for multi-objective optimization: empirical and stability analysis 面向多目标优化的多导粒子群算法:经验与稳定性分析

IF 2.6 4区计算机科学

Swarm Intelligence Pub Date : 2019-08-19 DOI: 10.1007/s11721-019-00171-0

Christiaan Scheepers, A. Engelbrecht, C. Cleghorn

引用次数: 19

A simplified multi-objective particle swarm optimization algorithm 一种简化的多目标粒子群算法

IF 2.6 4区计算机科学

Swarm Intelligence Pub Date : 2019-07-15 DOI: 10.1007/s11721-019-00170-1

Vibhu Trivedi, Pushkar Varshney, Manojkumar Ramteke

{"title":"A simplified multi-objective particle swarm optimization algorithm","authors":"Vibhu Trivedi, Pushkar Varshney, Manojkumar Ramteke","doi":"10.1007/s11721-019-00170-1","DOIUrl":"https://doi.org/10.1007/s11721-019-00170-1","url":null,"abstract":"Particle swarm optimization is a popular nature-inspired metaheuristic algorithm and has been used extensively to solve single- and multi-objective optimization problems over the last two decades. Several local and global search strategies, and learning and parameter adaptation strategies have been included in particle swarm optimization to improve its performance over the years. Most of these approaches are observed to increase the number of user-defined parameters and algorithmic steps resulting in an increased complexity of the algorithm. This paper presents a simplified multi-objective particle swarm optimization algorithm in which the exploitation (guided) and exploration (random) moves are simplified using a detailed qualitative analysis of similar existing operators present in the real-coded elitist non-dominated sorting genetic algorithm and the particle swarm optimization algorithm. The developed algorithm is then tested quantitatively on 30 well-known benchmark problems and compared with a real-coded elitist non-dominated sorting genetic algorithm, and its variant with a simulated binary jumping gene operator and multi-objective non-dominated sorting particle swarm optimization algorithm. In the comparison, the developed algorithm is found to be superior in terms of convergence speed. It is also found to be better with respect to four recent multi-objective particle swarm optimization algorithms and four differential evolution variants in an extended comparative analysis. Finally, it is applied to a newly formulated industrial multi-objective optimization problem of a residue (bottom product from the crude distillation unit) fluid catalytic cracking unit where it shows a better performance than the other compared algorithms.","PeriodicalId":51284,"journal":{"name":"Swarm Intelligence","volume":"22 1","pages":"1 - 34"},"PeriodicalIF":2.6,"publicationDate":"2019-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138538379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2