DocumentCode :
250789
Title :
An effective vector-driven biologically-motivated neural network algorithm to real-time autonomous robot navigation
Author :
Chaomin Luo ; Yang, Simon X. ; Krishnan, Mohan ; Paulik, Mark
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Detroit Mercy, Detroit, MI, USA
fYear :
2014
fDate :
May 31 2014-June 7 2014
Firstpage :
4094
Lastpage :
4099
Abstract :
A novel biologically-motivated neural networks approach associated with developed vector-driven autonomous robot navigation is proposed in this paper. The biologically-motivated neural networks (BNN) algorithm is employed to guide an autonomous robot to reach goal with obstacle avoidance motivated by Grossberg´s model for a biological neural system. As the robot plans its trajectory toward the goal, unreasonable path will be inevitably planned. A vector-based guidance paradigm is developed for guidance of the robot locally so as to plan more reasonable trajectories. In addition, square cell map representations are proposed for realtime autonomous robot navigation. The BNN based scheme demonstrates that the algorithms avoid the issue of local minima in path planning. In this paper, both simulation and comparison studies of an autonomous robot navigation demonstrate that the proposed model is capable of planning more reasonable and shorter collision-free paths in non-stationary and unstructured environments compared with other approaches.
Keywords :
collision avoidance; neurocontrollers; robots; trajectory control; vectors; BNN; Grossberg model; collision-free path planning; obstacle avoidance; realtime autonomous robot navigation; trajectory planning; vector-based guidance paradigm; vector-driven autonomous robot navigation; vector-driven biologically-motivated neural network algorithm; Biological neural networks; Biological system modeling; Collision avoidance; Navigation; Neurons; Robots; Trajectory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robotics and Automation (ICRA), 2014 IEEE International Conference on
Conference_Location :
Hong Kong
Type :
conf
DOI :
10.1109/ICRA.2014.6907454
Filename :
6907454
Link To Document :
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