中文摘要：

针对Pioneer3-DX移动机器人，提出了基于强化学习的自主导航策略，完成了基于动态神经网络的移动机器人导航算法设计．动态神经网络可以根据机器人环境状态的复杂程度自动地调整其结构，实时地实现机器人的状态与其导航动作之间的映射关系，有效地解决了强化学习中状态变量表的维数爆炸问题．通过对Pioneer3-DX移动机器人导航进行仿真和实物实验，证明该方法的有效性，且导航效果明显优于人工势场法．

英文摘要：

For the navigation of Pioneer3-DX mobile robot in unknown environment, we propose a self-navigation strategy with learning reinforcement, and develop the navigation algorithm based on the dynamical neural network. The dynamically self-organizing neural network can automatically adjust its structure according to the complexity of the working environments of the mobile robot to realize the mapping between environmental states and robot actions, effectively avoiding the dimension explosion in learning reinforcement. Simulations and real robot navigation experiments are carried out; results show that the proposed method is effective in applications. It gives a better navigation performance than that of the artificial potential-field method.