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. 2023 Mar 30;18(3):e0283751.
doi: 10.1371/journal.pone.0283751. eCollection 2023.

A search and rescue robot search method based on flower pollination algorithm and Q-learning fusion algorithm

Bing Hao  1 , Jianshuo Zhao  1 , He Du  1 , Qi Wang  1 , Qi Yuan  2 , Shuo Zhao  2
Affiliations

A search and rescue robot search method based on flower pollination algorithm and Q-learning fusion algorithm

Bing Hao et al. PLoS One. .

Abstract

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.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. SAR robot movement space.
Fig 2
Fig 2. Interaction models for reinforcement learning.
Fig 3
Fig 3. The classical grid map.
Fig 4
Fig 4. The improved grid map.
Fig 5
Fig 5. Initialize Q-table with FPA.
Fig 6
Fig 6. SAR robot search overall process.
Fig 7
Fig 7. Iteration times with different learning factor.
Fig 8
Fig 8. Typical grid map path planning for SAR robot in different environments.
Fig 9
Fig 9. Mean path length of different algorithms in typical grid map for SAR robot.
Fig 10
Fig 10. The optimal path in the improvement gird map using FIQL algorithm.
Fig 11
Fig 11. Mean path length of different algorithms in improved grid map for SAR robot.
See this image and copyright information in PMC

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