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Published on 24 January 2025
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Song,Y.;Duan,Q.;Liu,J. (2025). Path Planning for Robots in Fire Scenarios based on Dijkstra Algorithm and Genetic Algorithm. Applied and Computational Engineering,132,95-103.
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Path Planning for Robots in Fire Scenarios based on Dijkstra Algorithm and Genetic Algorithm

Yingwei Song *,1, Qihong Duan 2, Juntian Liu 3
  • 1 Xidian University, Xi’an, China
  • 2 Beijing Jiaotong Unitversity, Beijing, China
  • 3 Beijing No.2 highschool, Beijing, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/2024.20625

Abstract

Robots have emerged as a pivotal element in enhancing firefighting operations, offering a blend of efficiency and safety to human responders. This paper delves into the development of a path planning strategy for robots navigating through fire scenarios. Particularly we fused the Dijkstra Algorithm and Genetic Algorithm (GA). Our methodology commences with a simplified yet comprehensive definition of the fire environment, incorporating factors such as obstacle height, surface roughness, and fire sources. The environment and surroundings are represented by a 2.5-dimensional grid map. On the other hand, the robot’s traversal and ascent capabilities modeled to reflect varying velocities across different terrains. The Dijkstra Algorithm is subsequently utilized to identify the optimal path from the starting point to the destination, ensuring a balance between minimal traversal time and reduced thermal exposure. Our results, demonstrated through MATLAB simulations, reveal a marked improvement in path planning when GA optimization is applied. The comparative analysis across three scenarios underscores the versatility and effectiveness of our approach, showcasing a significant reduction in both traversal time and thermal exposure. Index Terms—Robots, Firefighting, Path planning, Dijkstra, GA

Keywords

Robots, Firefighting, Path planning, Dijkstra, GA

[1]. G. M. Kruijff, et al.: “Rescue Robots at Earthquake-Hit Mirandola, Italy: A field report,” Proc. of the 10th IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR2012), pp.1–8, 2012.

[2]. Bogue, R. (2021), ”The role of robots in firefighting”, Industrial Robot, Vol. 48 No. 2, pp. 174-178. https://doi.org/10.1108/IR-10-2020-0222

[3]. Ruinan Chen, Jie Hu et al. “An RRT-Dijkstra-Based Path Planning Strategy for Autonomous Vehicles.” Applied Sciences (2022). Ruinan Chen; Jie Hu; Wencai Xu.

[4]. DongKai Fan and Ping Shi. “Improvement of Dijkstra’s algorithm and its application in route planning.” 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (2010). 1901-1904.. DongKai Fan; Ping Shi.

[5]. Alican Bozyigit, Gazihan Alankus et al. “Public transport route planning: Modified dijkstra’s algorithm.” 2017 International Conference on Computer Science and Engineering (UBMK) (2017). 502-505.. Alican Bozyigit; Gazihan Alankus; E. Nasiboglu.˘

[6]. W. Dougherty and R. D. Blanton. “Using regression analysis for GA-based ATPG parameter optimization.” Proceedings International Conference on Computer Design. VLSI in Computers and Processors (Cat. No.98CB36273) (1998). 516521.. W. Dougherty; R. D. Blanton.

[7]. Wenrui Zhao and Borui Wu. “Improved GA and Its Application in Performance Optimization of Electronic Components.” Advances in Multimedia (2022).. Wenrui Zhao; Borui Wu.

[8]. R. Ngamtawee and P. Wardkein. “Simplified Genetic Algorithm: Simplify and Improve RGA for Parameter Optimizations.” (2014). 55-64.. R. Ngamtawee; P. Wardkein.

[9]. V. Cicirello and Stephen F. Smith. “Modeling GA Performance for Control Parameter Optimization.” Annual Conference on Genetic and Evolutionary Computation (2000).. V. Cicirello; Stephen F. Smith.

[10]. Lim Wei Jer, Gerald Lee Jun Xiong et al. “GA-based Optimization for Circuit Design Assistance.” 2012 Third International Conference on Intelligent Systems Modelling and Simulation (2012). 732-736.. Lim Wei Jer; Gerald Lee Jun Xiong; S. Neoh; Arjuna Marzuki.

[11]. N. Mohan and N. Kasabov. “Transductive modeling with GA parameter optimization.” Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. (2005). 839-844 vol. 2.. N. Mohan; N. Kasabov.

[12]. Nathan P. Koenig, A. Howard. “Design and use paradigms for Gazebo, an open-source multi-robot simulator.” IEEE/RJS International Conference on Intelligent Robots and Systems (2004).. Nathan P. Koenig; A. Howard.

[13]. David Whitney, Eric Rosen et al. “ROS Reality: A Virtual Reality Framework Using Consumer-Grade Hardware for ROSEnabled Robots.” IEEE/RJS International Conference on Intelligent Robots and Systems (2018). 1-9.. David Whitney; Eric Rosen; D. Ullman; Elizabeth Phillips; Stefanie Tellex.

[14]. Brian P. Gerkey, R. Vaughan et al. “The Player/Stage Project: Tools for Multi-Robot and Distributed Sensor Systems.” (2003).. Brian P. Gerkey; R. Vaughan; A. Howard.

Cite this article

Song,Y.;Duan,Q.;Liu,J. (2025). Path Planning for Robots in Fire Scenarios based on Dijkstra Algorithm and Genetic Algorithm. Applied and Computational Engineering,132,95-103.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation

Conference website: https://2024.confmla.org/
ISBN:978-1-83558-941-0(Print) / 978-1-83558-942-7(Online)
Conference date: 21 November 2024
Editor:Mustafa ISTANBULLU
Series: Applied and Computational Engineering
Volume number: Vol.132
ISSN:2755-2721(Print) / 2755-273X(Online)

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