Finding Optimal Path Using Python

Authors

  • Bishnu Deo Kumar Assistant Professor, Department of Electronics and Communication Engineering, Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, India
  • Amber Gupta Student, Department of Electronics and Communication Engineering, Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, India
  • Anushka Dwivedi Student, Department of Electronics and Communication Engineering, Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, India
  • Astha Singh Student, Department of Electronics and Communication Engineering, Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, India

Keywords:

A* algorithm, optimal path, python, algorithm, dijsktra’s algorithms

Abstract

This paper introduces a smart operation to find the optimal path. The evolution of finding the shortest path became more useful and systematic in several fields. Hence, the proposed model was created by implementing the A* algorithm associated with the Python language. This algorithm is an approach to search the shortest route between two points, either starting or ending. The experimental results showed a high accuracy to find the optimal path between two obstacles. People have been searching for the best solution to difficulties for a very long time to increase the productivity at work. There are several uses for the problem of shortest path in network technology research in computer science, communications, operations research, geographic information science, and other domains. Designing a problem-solving algorithm and advancing research are crucial steps in solving the problem.

References

Ramezanlou MT, Azimirad V, Zakeri M. Hybrid path planning of robots through optimal control and PSO algorithm. In: International Conference on Robotics and Mechatronics, Tehran, Iran, November 20–21, 2019. pp. 259–264.

Alyasin A, Abbas EI, Hasan SD. An efficient optimal path finding for mobile robot based on Dijkstra method. In: 2019 4th Scientific International Conference Najaf (SICN), Al-Najef, Iraq, April 29–30, 2019. pp. 11–14.

Junfeng Y, Binbin Z, Qingda Z. The optimization of A* algorithm in the practical path finding application. In: IEEE WRI World Congress on Software Engineering, Xiamen, China, May 19–21, 2009. pp. 514–518.

Li Y, Nie Z, Zhou X. Finding the optimal shortest path tree with respect to single link failure recovery. In: International Conference on Networked Computing and Advanced Information Management, Gyeongju, South Korea, September 2–4, 2008. pp. 412–415.

Afzal ZR, Prabhakar P, and Prabhakar P. Optimal tool path planning for 3D printing with spatio-temporal and thermal constraints. In: 2019 Sixth Indian Control Conference (ICC), Hyderabad, India, December 18–20, 2019. pp. 176–181.

Wang Z, Peng H, Jiang D, Zhang S. Optimal path planning of two-wheeled mobile robots in the presence of dynamic obstacles. In: 2017 36th Chinese Control Conference (CCC), Dalian, China, July 26–28, 2017. pp. 2483–2488.

Chen L, Sun H. Picking path optimization of mobile robotic arm based on differential evolution and improved A* algorithm. IEEE Access. 2021; 9: 154413–154422.

Zhivkov P, Simidchiev A. Development of software tool for optimization and evaluation of cycling routes by characterizing cyclist exposure to air pollution. Ann Computer Sci Inform Syst. 2022; 32: 105–112.

Qing G, Zheng Z, Yue X. Path-planning of automated guided vehicle based on improved Dijkstra algorithm. In2017 29th Chinese control and decision conference (CCDC) 2017 May 28 (pp. 7138-7143). IEEE.

Gibbons S, Lyytikäinen T, Overman HG, Sanchis-Guarner R. New road infrastructure: the effects on firms. J Urban Econ. 2019; 110: 35–50.

Published

2023-10-31