全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于Boltzmann机神经网络认知机制的机器人趋光控制

DOI: 10.13195/j.kzyjc.2013.1105, PP. 2189-2194

Keywords: 移动机器人,趋光技能,认知机制,Boltzmann机

Full-Text   Cite this paper   Add to My Lib

Abstract:

针对移动机器人未知环境下的趋光控制问题,模拟人或动物“感知-行动”认知机制,对具有趋光特性的移动机器人进行设计,提出一种基于Boltzmann机神经网络的趋光控制方法.该方法首先应用知识集对机器人趋光控制器的Boltzmann机神经网络进行趋光训练;然后应用Boltzmann机神经网络的运行机制实现趋光控制.仿真实验表明,该方法能够提高机器人学习的控制精度.

References

[1]  (Zhou C, Cao Z Q, Wang S, et al. A miniature fish robot design and real-time path planning[J]. Acta Automatica Sinica, 2008, 34(7): 772-777.)
[2]  Jose A F, Gerardo G A, Miguel A M. Behavioral control through evolutionary neurocontrollers for autonomous mobile robot navigation[J]. Robotics and Autonomous Systems, 2009, 57(4): 411-419.
[3]  Dai L Z, Ruan X G, Wang G W, et al. Neural networks based autonomous learning for a desktop robot[C]. Proc of theWorld Congress on Intelligent Control and Automation. New York: IEEE, 2012: 739-742.
[4]  何光锋, 王凌云, 徐加鹏. 基于模块化控制的多功能智能小车设计[J]. 现代电子技术, 2013, 36(16): 137-142.
[5]  (He G F, Wang L Y, Xu J P. Multifunction smart car based on modular control[J]. Modern Electronic Technology, 2013, 36(16): 137-142.)
[6]  Crespi A, Lachat D, Pasquier A. Controlling swimming and crawling in a fish robot using a central pattern generator[J]. Autonomous Robots, 2008, 25(1/2): 3-13.
[7]  周超, 曹志强, 王硕, 等. 微小型仿生机器鱼设计与实时路径规划[J]. 自动化学报, 2008, 34(7): 772-777.
[8]  Sumoto H, Yamaguchi S. Development of a motion control system using phototaxis for a fish type robot[C]. Proc of the 20th Int Offshore and Polar Engineering Conf. Cupertino: ISOPE, 2010: 307-310.
[9]  Watson R A, Ficici S G, Pollack J B. Embodied evolution: Distributing an evolutionary algorithm in a population of robots[J]. Robotics and Autonomous Systems, 2002, 39(1): 1-18.
[10]  Tuci E, Ampatzis C, Vicentini F, et al. Evolving homogeneous neurocontrollers for a group of heterogeneous robots: Coordinated motion, cooperation, and acoustic communication[J]. Artificial Life, 2008, 14(2): 157-178.
[11]  Asada M, Uchibe E, Hosoda K. Cooperative behavior acquisition for mobile robots in dynamically changing real worlds via vision-based reinforcement learning and development[J]. Artificial Intelligence, 1999, 110(2): 275-292.
[12]  Mihai D, Grigore B F, Ahmed T. Obstacle avoidance of redundant manipulators using neural networks based reinforcement learning[J]. Robotics and Computer Integrated Manufacturing, 2012, 28(2): 132-146.
[13]  Hinton G E, Sejnowski T J. Learning and relearning in Boltzmann machines[M]. Cambridge: MIT Press, 1986: 282-317.
[14]  Agliari E, Barra A, De Antoni A, et al. Parallel retrieval of correlated patterns: From hopfield networks to Boltzmann machines[J]. Neural Networks, 2013, 38(2): 52-63.
[15]  Lazo A V, Rathie P. On the entropy of continuous probability distributions[J]. IEEE Trans on Information Theory, 1978, 24(1): 120-122.
[16]  Metropolis N, Rosenbluth A W, Rosenbluth M N, et al. Equation of state calculations by fast computing machines[J]. The J of Chemical Physics, 1953, 21(6): 1087-1090.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133