Comparison of Algorithms for Path Planning Collision Avoidance
DOI:
https://doi.org/10.21439/jme.v8i.123Keywords:
Robot Operation System, Localization, Mobile robots, Autonomous robotsAbstract
Collision-free path planning is a critical aspect of mobile robotic navigation, with significant applications in autonomous systems and intelligent transportation. The choice of an algorithm directly influences computational efficiency and trajectory quality. This study evaluates three widely used paradigms: Artificial Potential Fields (APF), Probabilistic Roadmap Method (PRM), and Rapidly-Exploring Random Tree (RRT). Experiments were conducted in a two-dimensional environment with two and three stationary obstacles, assessing each method based on execution time and path length. The results indicate that APF is simple and fast but prone to local minima. PRM is effective for complex environments but comes with higher computational costs. RRT efficiently explores space, but often generates nonoptical trajectories. The best approach depends on environmental constraints and computational requirements. For future work, increasing scenario complexity or validating the results through real-world robotic experiments is recommended.
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