College of Computer and Control Engineering, Qiqihar University, Qiqihar, China.
College of Telecommunication and Electronic Engineering, Qiqihar University, Qiqihar, China.
PLoS One. 2023 Mar 30;18(3):e0283751. doi: 10.1371/journal.pone.0283751. eCollection 2023.
Search algorithm plays an important role in the motion planning of the robot, it determines whether the mobile robot complete the task. To solve the search task in complex environments, a fusion algorithm based on the Flower Pollination algorithm and Q-learning is proposed. To improve the accuracy, an improved grid map is used in the section of environment modeling to change the original static grid to a combination of static and dynamic grids. Secondly, a combination of Q-learning and Flower Pollination algorithm is used to complete the initialization of the Q-table and accelerate the efficiency of the search and rescue robot path search. A combination of static and dynamic reward function is proposed for the different situations encountered by the search and rescue robot during the search process, as a way to allow the search and rescue robot to get better different feedback results in each specific situation. The experiments are divided into two parts: typical and improved grid map path planning. Experiments show that the improved grid map can increase the success rate and the FIQL can be used by the search and rescue robot to accomplish the task in a complex environment. Compared with other algorithms, FIQL can reduce the number of iterations, improve the adaptability of the search and rescue robot to complex environments, and have the advantages of short convergence time and small computational effort.
搜索算法在机器人的运动规划中起着重要作用，它决定了移动机器人是否能够完成任务。为了解决复杂环境中的搜索任务，提出了一种基于花授粉算法和 Q-learning 的融合算法。为了提高精度，在环境建模部分使用了改进的栅格地图，将原始静态栅格转换为静态和动态栅格的组合。其次，将 Q-learning 和花授粉算法相结合，完成 Q 表的初始化，加速搜索和救援机器人路径搜索的效率。针对搜索和救援机器人在搜索过程中遇到的不同情况，提出了静态和动态奖励函数的组合，以使搜索和救援机器人在每种特定情况下获得更好的不同反馈结果。实验分为典型和改进的栅格地图路径规划两部分。实验表明，改进的栅格地图可以提高成功率，FIQL 可用于搜索和救援机器人在复杂环境中完成任务。与其他算法相比，FIQL 可以减少迭代次数，提高搜索和救援机器人对复杂环境的适应性，具有收敛时间短、计算量小的优点。
