An optimized modular neural network controller based on environment classification and selective sensor usage for mobile robot reactive navigation
SCIE
SCOPUS
- Title
- An optimized modular neural network controller based on environment classification and selective sensor usage for mobile robot reactive navigation
- Authors
- SeongJoo Han; Oh, SY
- Date Issued
- 2008-03
- Publisher
- SPRINGER
- Abstract
- A new approach to the design of a neural network (NN) based navigator is proposed in which the mobile robot travels to a pre-defined goal position safely and efficiently without any prior map of the environment. This navigator can be optimized for any user-defined objective function through the use of an evolutionary algorithm. The motivation of this research is to develop an efficient methodology for general goal-directed navigation in generic indoor environments as opposed to learning specialized primitive behaviors in a limited environment. To this end, a modular NN has been employed to achieve the necessary generalization capability across a variety of indoor environments. Herein, each NN module takes charge of navigating in a specialized local environment, which is the result of decomposing the whole path into a sequence of local paths through clustering of all the possible environments. We verify the efficacy of the proposed algorithm over a variety of both simulated and real unstructured indoor environments using our autonomous mobile robot platform.
- Keywords
- reactive navigation; evolutionary robotics; neurocontroller; environment classification; cooperative coordination; TIME OBSTACLE AVOIDANCE; BEHAVIOR
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/22899
- DOI
- 10.1007/S00521-006-0
- ISSN
- 0941-0643
- Article Type
- Article
- Citation
- NEURAL COMPUTING & APPLICATIONS, vol. 17, no. 2, page. 161 - 173, 2008-03
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.