J4 ›› 2005, Vol. 27 ›› Issue (12): 72-75.
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周宏志[1] 王伊卿[1] 樊长虹[2]
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摘要:
针对移动机器人未知环境下的安全路径规划,本文采用了一种局部连接Hopfield神经网络(ANN)规划器。对任意形状环境,ANN中兼顾处理了“过近”和“过远”来形成安全 路径,而无需学习过程。为在单处理器上进行有效的在线路径规划,提出用基于距离变换的串行模拟,加速数值势场的传播。仿真表明,该方法具有较高的实时性和环境适 应性。
关键词: 移动机器人 安全路径规划 神经网络 约束距离变换
Abstract:
For the safe path planning of mobile robots in unknown environments, the paper proposes a locally linked Hopfield artificial neural network (ANN) pl anner. For the environments of arbitrary shape, without the learning process, ANN plans a safe path with the consideration of both“too close”and“too far”. For the effective application on a single processor to plan a path on-line, the simulation based on constrained distance transformation is propo osed to accelerate the propagation of the numerical potential field of ANN. Simulations demonstrate the method has high real-time ability and adaptabili ty to environments.
Key words: (mobile robot, safe path planning;neural networks;constrained distance transformation)
周宏志[1] 王伊卿[1] 樊长虹[2]. 基于神经网络的移动机器人路径规划[J]. J4, 2005, 27(12): 72-75.
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http://joces.nudt.edu.cn/CN/Y2005/V27/I12/72