Abstract: The growing frequency of forest area fires poses critical challenges for emergency response, necessitating progressive solutions for effective navigation and direction planning in dynamic environments. This study investigates an adaptive technique to enhance the performance of autonomous robots deployed in forest area fireplace scenarios. The primary objective is to develop a hybrid methodology that integrates advanced studying strategies with optimization techniques to enhance route planning beneath unexpectedly changing situations. To reap this, a simulation-based total framework became hooked up, in which self-reliant robots were tasked with navigating diverse forest fire eventualities. The method includes schooling a model to dynamically adapt to environmental modifications at the same time as optimizing direction choice in real time. Performance metrics together with direction efficiency, adaptability to obstacles, and reaction time been analyzed to assess the effectiveness of the proposed solution. Results indicate an enormous improvement in path planning performance as compared to traditional methods, with more suitable adaptability main to faster response instances and extra effective navigation. The findings underscore the functionality of the proposed method to cope with the complexities of forest area fire environments, demonstrating its potential for real-world applications in disaster response. The results are shown in the conceived DRL-PSO framework where execution time is reduced up to 95% and the success rate of 95 % for the proposed method compared to the conventional ones. Python is used to implement the proposed work. Compared to the proposed method’s execution time of 68. 3 seconds and the highest success rate among evaluated strategies, so it can be used as a powerful solution for autonomous drone navigation in dangerous situations. In the end, this research contributes precious insights into adaptive route planning for self-sufficient robots in unsafe situations, providing a strong framework for destiny advancements in disaster management technologies.

N. K. Thakre, Divya Nimma, Anil V Turukmane, Akhilesh Kumar Singh, Divya Rohatgi and Balakrishna Bangaru, “Dynamic Path Planning for Autonomous Robots in Forest Fire Scenarios Using Hybrid Deep Reinforcement Learning and Particle Swarm Optimization” International Journal of Advanced Computer Science and Applications(IJACSA), 15(9), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150950

@article{Thakre2024,
title = {Dynamic Path Planning for Autonomous Robots in Forest Fire Scenarios Using Hybrid Deep Reinforcement Learning and Particle Swarm Optimization},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150950},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150950},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {9},
author = {N. K. Thakre and Divya Nimma and Anil V Turukmane and Akhilesh Kumar Singh and Divya Rohatgi and Balakrishna Bangaru}
}