An Efficient Neural Network Approach to Dynamic Robot Motion Planning and Map Building
In this paper, a novel biologically inspired neural network approach is proposed for real-time simultaneous map building and path planning with limited measurable sensor information in a nonstationary environment. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation with both excitatory and inhibitory connections. There are only local connections in the proposed neural network. The environment is assumed completely unknown, and subject to sudden change. The map of the environment is built during the real-time robot navigation with its sensor information that is limited to a short range. The realtime robot path is generated through the dynamic activity landscape of the neural network. The system stability is guaranteed by a Lyapunov stability theory. The effectiveness and the efficiency are demonstrated by simulations studies.
neural dynamics neural activity landscape path planning map building mobile robot
Simon X. Yang
ARIS (Advanced Robotics & Intelligent, Systems) Lab School of Engineering, University of Guelph, Guelph, ON NlG 2W1, Canada
国际会议
8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)
上海
英文
1189-1194
2001-11-14（万方平台首次上网日期，不代表论文的发表时间）