DocumentCode :
394456
Title :
Environmental feature extraction and mergence: make the past serve the present
Author :
Liu, Juan ; Cai, Zixing ; Tu, Chunming
Author_Institution :
Coll. of Inf. Sci. & Eng., Central South Univ., Hunan, China
Volume :
4
fYear :
2002
fDate :
18-22 Nov. 2002
Firstpage :
2108
Abstract :
This paper proposes a connectionist model to learn a spatial representation of the world based on temporal memory of perceptions and actions of a mobile robot. It is constructed at run-time to merge past experiences and retrieved in later runs to guide the robot to perform the navigation task. A coding strategy is introduced to extract the directional information from the perception sequence, which endows the robot with localization ability. The temporal sequence processing network (TSPN) transforms routing knowledge learned from robot´s experiences into temporal characteristics of cell firing and enables the implicit building of a world representation. The navigation system integrating TSPN and a reactive safeguard module performs collision-free navigation, dynamic landmark and heading detection, route learning and path planning in a noisy world. The simulation and real world experiments demonstrate the flexibility and robustness of the system.
Keywords :
encoding; feature extraction; mobile robots; neurocontrollers; robot vision; robust control; TSPN; cell firing; coding strategy; collision-free navigation; connectionist model; directional information extraction; dynamic heading detection; dynamic landmark detection; environmental feature extraction; environmental mergence; localization ability; mobile robot actions; mobile robot perceptions; path planning; perception sequence; robot guidance; robot navigation; route learning; routing knowledge; spatial representation; system flexibility; system robustness; temporal characteristics; temporal memory; temporal sequence processing network; world representation; Artificial neural networks; Biomedical signal processing; Educational institutions; Feature extraction; Information science; Mobile robots; Robot sensing systems; Runtime; Sonar navigation; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN :
981-04-7524-1
Type :
conf
DOI :
10.1109/ICONIP.2002.1199048
Filename :
1199048
Link To Document :
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