Neural network application in robot motion planning

X. Yang, M. Meng
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引用次数: 5

Abstract

The application of neural networks to real-time motion planning of robotic systems is studied. The proposed framework, using biologically inspired neural networks, for robot motion planning with obstacle avoidance in a nonstationary environment is computationally efficient. The neural dynamics of each neuron in the topologically organized neural network is characterized by a simple shunting equation derived from Hodgkin and Huxley's (1952) membrane model. The real-time optimal robot motion is planned through the dynamic activity landscape of the neural network that represents the dynamic environment. The proposed model can deal with point mobile robots, manipulation robots, holonomic and nonholonomic car-like robots and multi-robot systems. The efficiency and effectiveness are demonstrated by simulation studies.
神经网络在机器人运动规划中的应用
研究了神经网络在机器人系统实时运动规划中的应用。提出的框架,使用生物启发的神经网络,机器人运动规划与避障在非平稳环境是计算效率高。在拓扑组织的神经网络中,每个神经元的神经动力学由霍奇金和赫胥黎(1952)膜模型的简单分流方程表征。通过代表动态环境的神经网络的动态活动景观来规划机器人的实时最优运动。该模型适用于点移动机器人、操作机器人、完整和非完整类车机器人以及多机器人系统。仿真研究证明了该方法的有效性和有效性。
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