2014 IEEE Symposium on Swarm Intelligence最新文献_第5页

Sensor-based autonomous robot navigation under unknown environments with grid map representation 基于传感器的未知环境下网格地图表示自主机器人导航

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011782

C. Luo, Jiyong Gao, Xinde Li, Hongwei Mo, Qimi Jiang

{"title":"Sensor-based autonomous robot navigation under unknown environments with grid map representation","authors":"C. Luo, Jiyong Gao, Xinde Li, Hongwei Mo, Qimi Jiang","doi":"10.1109/SIS.2014.7011782","DOIUrl":"https://doi.org/10.1109/SIS.2014.7011782","url":null,"abstract":"Real-time navigation and mapping of an autonomous robot is one of the major challenges in intelligent robot systems. In this paper, a novel sensor-based biologically inspired neural network algorithm to real-time collision-free navigation and mapping of an autonomous mobile robot in a completely unknown environment is proposed. A local map composed of square grids is built up through the proposed neural dynamics for robot navigation with restricted incoming sensory information. With equipped sensors, the robot can only sense a limited reading range of surroundings with grid map representation. According to the measured sensory information, an accurate map with grid representation of the robot with local environment is dynamically built for the robot navigation. The real-time robot motion is planned through the varying neural activity landscape, which represents the dynamic environment. The proposed model for autonomous robot navigation and mapping is capable of planning a real-time reasonable trajectory of an autonomous robot. Simulation and comparison studies are presented to demonstrate the effectiveness and efficiency of the proposed methodology that concurrently performs collision-free navigation and mapping of an intelligent robot.","PeriodicalId":380286,"journal":{"name":"2014 IEEE Symposium on Swarm Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125879957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 26

A parametric testing of the Firefly algorithm in the determination of the optimal osmotic drying parameters for papaya 萤火虫算法在确定木瓜最佳渗透干燥参数中的参数测试

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011770

J. Yeomans, Raha Imanirad

引用次数: 3

Feature selection for problem decomposition on high dimensional optimization 高维优化问题分解的特征选择

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011809

Pedro Reta, Ricardo Landa

引用次数: 0

OCbotics: An organic computing approach to collaborative robotic swarms 机器人:协作机器人群的有机计算方法

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011781

S. Mammen, Sven Tomforde, J. Hähner, Patrick Lehner, Lukas Forschner, A. Hiemer, Mirela Nicola, Patrick Blickling

{"title":"OCbotics: An organic computing approach to collaborative robotic swarms","authors":"S. Mammen, Sven Tomforde, J. Hähner, Patrick Lehner, Lukas Forschner, A. Hiemer, Mirela Nicola, Patrick Blickling","doi":"10.1109/SIS.2014.7011781","DOIUrl":"https://doi.org/10.1109/SIS.2014.7011781","url":null,"abstract":"In this paper we present an approach to designing swarms of autonomous, adaptive robots. An observer/controller framework that has been developed as part of the Organic Computing initiative provides the architectural foundation for the individuals' adaptivity. Relying on an extended Learning Classifier System (XCS) in combination with adequate simulation techniques, it empowers the individuals to improve their collaborative performance and to adapt to changing goals and changing conditions. We elaborate on the conceptual details, and we provide first results addressing different aspects of our multi-layered approach. Not only for the sake of generalisability, but also because of its enormous transformative potential, we stage our research design in the domain of quad-copter swarms that organise to collaboratively fulfil spatial tasks such as maintenance of building facades. Our elaborations detail the architectural concept, provide examples of individual self-optimisation as well as of the optimisation of collaborative efforts, and we show how the user can control the swarm at multiple levels of abstraction. We conclude with a summary of our approach and an outlook on possible future steps.","PeriodicalId":380286,"journal":{"name":"2014 IEEE Symposium on Swarm Intelligence","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122399506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

Path planning for swarms in dynamic environments by combining probabilistic roadmaps and potential fields 结合概率路线图和势场的动态环境下蜂群路径规划

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011808

Alex Wallar, E. Plaku

引用次数: 4

A MOPSO based on hyper-heuristic to optimize many-objective problems 基于超启发式的多目标优化MOPSO

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011803

Olacir Rodrigues Castro Junior, A. Pozo

{"title":"A MOPSO based on hyper-heuristic to optimize many-objective problems","authors":"Olacir Rodrigues Castro Junior, A. Pozo","doi":"10.1109/SIS.2014.7011803","DOIUrl":"https://doi.org/10.1109/SIS.2014.7011803","url":null,"abstract":"Multi-Objective Problems (MOPs) presents two or more objective functions to be simultaneously optimized. MOPs presenting more than three objective functions are called Many-Objective Problems (MaOPs) and pose challenges to optimization algorithms. Multi-objective Particle Swarm Optimization (MOPSO) is a promising meta-heuristic to solve MaOPs. Previous works have proposed different leader selection methods and archiving strategies to tackle the challenges caused by MaOPs, however, selecting the most appropriated components for a given problem is not a trivial task. Moreover, the algorithm can take advantage by using a variety of methods in different phases of the search. The concept of hyper-heuristic emerges for automatically selecting heuristic components for effectively solve a problem. However few works on the literature apply hyper-heuristics on multi-objective optimizers. In this work, we use a simple hyper-heuristic to select leader and archiving methods during the search. Unlike other studies our hyper-heuristic is guided by the R2 indicator due to its good measuring characteristics and low computational cost. An experimental study was conducted to evaluate the ability of the proposed hyper-heuristic in guiding the search towards its preferred region. The study compared the performance of the H-MOPSO and its low-level heuristics used separately regarding the R2 indicator. The results show that the hyper-heuristic proposed is able to guide the search through selecting the right components in most cases.","PeriodicalId":380286,"journal":{"name":"2014 IEEE Symposium on Swarm Intelligence","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122558305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

Particle swarm optimization based distributed agreement in multi-agent dynamic systems 基于粒子群优化的多智能体动态系统分布式协议

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011792

V. Gazi, R. Ordóñez

引用次数: 3

Analysis of stagnation behaviour of competitive coevolutionary trained neuro-controllers 竞争性协同进化训练神经控制器的停滞行为分析

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011795

Christiaan Scheepers, A. Engelbrecht

引用次数: 3

Naturally inspired optimization algorithms as applied to mobile robotic path planning 应用于移动机器人路径规划的自然启发优化算法

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011779

S. E. Muldoon, C. Luo, S. Furao, Hongwei Mo

{"title":"Naturally inspired optimization algorithms as applied to mobile robotic path planning","authors":"S. E. Muldoon, C. Luo, S. Furao, Hongwei Mo","doi":"10.1109/SIS.2014.7011779","DOIUrl":"https://doi.org/10.1109/SIS.2014.7011779","url":null,"abstract":"Global path planning as applied to mobile robotics can be approached in a similar fashion as classic optimization problems involving combinational constraints (e.g. the Traveling Salesman Problem). A single, exact optimal solution for the shortest path may not exist, and obtaining near-optimal solutions selected and ranked by criteria, or deemed “good-enough”, can satisfy the problem. An overview is provided on a select subset of naturally inspired iterative search algorithms; Simulated Annealing (SA), Genetic Algorithm (GA), and Ant Colony Optimization (ACO) have all been studied and applied to the task of mobile robotic path planning. These three techniques or algorithms (respectively) represent a broader range of naturally inspired physical processes, evolutionary or biological processes, and animal kingdom behavioral examples. It has been demonstrated that these algorithms have been utilized on their own, or as part of a collaborative hybridization of iterative algorithms and heuristic modifiers, to effectively balance the constraints, strengths and weaknesses in a given path planning approach. A brief contextual summary of current literature provides insights regarding implementation of this category of algorithms, and suggests approaches for future experimentation and research in this topic area.","PeriodicalId":380286,"journal":{"name":"2014 IEEE Symposium on Swarm Intelligence","volume":"227 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129204153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7