Fast path planning in unknown environment is important to reduce the loss of human and material resources. To reduce planning time while obtaining a short path, this paper proposes a Bidirectional Associative Learning Algorithm (BALA). In the proposed algorithm, an episode is defined as a bidirectional movement between the start point and the target point. The planning process in the BALA is divided into three stages: early stage, medium stage and end stage. In the early stage, the attraction of the target point is adopted to instruct the robot to select action. This strategy not only helps the robot avoid blind search, but also provides the search scope that may contain the global shortest path for the subsequent episodes. In the medium stage, we propose an action selection strategy based on the experience guidance, where the experience obtained in the obverse and reverse movements is used alternately to improve the learning efficiency of the robot. In the end stage, a strong connectivity relationship between nodes is defined. Planning by this relationship, the length of the final planned path will be the shortest based on the experience the robot obtains. The comparison results with Q-Learning and its improved algorithm reveal that the BALA demonstrates desirable and stable performance in planning efficiency in any environment, and it can well balance the planning time and path length. Additionally, the practicability of the proposed algorithm is validated on Turtlebot3 burger robot.

在未知环境中进行快速路径规划对于减少人力和物力的浪费非常重要。为了减少获得短路径的计划时间，本文提出了一种双向联想学习算法（BALA）。在提出的算法中，情节定义为起点和目标点之间的双向运动。BALA中的计划过程分为三个阶段：早期，中期和结束阶段。在早期阶段，采用目标点的吸引力来指示机器人进行动作选择。该策略不仅可以帮助机器人避免盲目搜索，还可以提供可能包含后续情节的全球最短路径的搜索范围。在中期阶段，我们会根据经验指导提出一种行动选择策略，交替使用在正向和反向运动中获得的经验来提高机器人的学习效率。在最后阶段，定义了节点之间的牢固连接关系。根据这种关系进行规划，根据机器人获得的经验，最终规划路径的长度将最短。与Q-Learning及其改进算法的比较结果表明，BALA在任何环境下都表现出理想且稳定的计划效率，并且可以很好地平衡计划时间和路径长度。此外，在Turtlebot3汉堡机器人上验证了该算法的实用性。定义了节点之间的牢固连接关系。根据这种关系进行规划，根据机器人获得的经验，最终规划路径的长度将最短。与Q-Learning及其改进算法的比较结果表明，BALA在任何环境下都表现出理想且稳定的计划效率，并且可以很好地平衡计划时间和路径长度。此外，在Turtlebot3汉堡机器人上验证了该算法的实用性。定义了节点之间的牢固连接关系。根据这种关系进行规划，根据机器人获得的经验，最终规划路径的长度将最短。与Q-Learning及其改进算法的比较结果表明，BALA在任何环境下都表现出理想且稳定的计划效率，并且可以很好地平衡计划时间和路径长度。此外，在Turtlebot3汉堡机器人上验证了该算法的实用性。并且可以很好地平衡计划时间和路径长度。此外，在Turtlebot3汉堡机器人上验证了该算法的实用性。并且可以很好地平衡计划时间和路径长度。此外，在Turtlebot3汉堡机器人上验证了该算法的实用性。