Abstract

This paper explores the application of Dijkstra's algorithm for robot path planning in a 2D environment, with a focus on weighted paths. The goal is to enable efficient and optimized navigation from a source to a goal while accommodating obstacles. The methodology begins by defining objectives, identifying source and goal points, and representing the environment as a weighted graph. Dijkstra's algorithm is then applied iteratively, utilizing priority queues for optimal node exploration. The resulting path is determined through backtracking, considering factors like distance and terrain complexity. The methodology accounts for dynamic changes in the environment through real-time adaptations. Validation and optimization involve testing the generated path in simulated environments and assessing performance metrics. Implemented and integrated into the robot's software framework, the system accommodates sensor data for dynamic updates. This comprehensive approach ensures adaptable and efficient navigation, optimizing paths based on specific weights and environmental constraints. We conclude with a demonstration of the methodology's effectiveness through simulation and performance evaluations in a concise, practical manner.