【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。更多还原

【Abstract】 For the safe path planning of mobile robots in unknown environments, the paper proposes a locally linked Hopfield artificial neural network (ANN) planner. 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 proposed to accelerate the propagation of the numerical potential field of ANN. Simulations demonstrate the method has high real-time ability and adaptability to environments.更多还原