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All Journal Electrician : Jurnal Rekayasa dan Teknologi Elektro
Muchamad Oktaviandri
Universitas Pembangunan Nasional Veteran Jakarta
Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Adaptive Path Planning Using Depth-First Search in Environments with Dynamic Obstacle Ni Putu Devira Ayu Martini; Muchamad Oktaviandri; Elvi Armadani
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 20 No. 1 (2026)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v20n1.3002

Abstract

Depth-First Search (DFS) is one of the classical algorithms used for robotic path planning due to its simplicity and deterministic exploration strategy. However, its performance can degrade significantly in environments where obstacles move or appear unexpectedly. This study analyzes the behavior of the DFS algorithm under both static and dynamic obstacle conditions using a grid-based simulation. Three primary performance indicators were evaluated: total path length, turning complexity, and overall path efficiency. In static environments, where all obstacle positions were known beforehand, DFS demonstrated stable and efficient navigation, achieving approximately 117% shorter paths, 42% higher efficiency, and 133% fewer turning maneuvers compared to scenarios with dynamic obstacles. When unexpected obstacles were introduced, the robot frequently performed backtracking and route replanning, which increased the total travel distance and the number of directional changes, ultimately lowering navigation efficiency. Despite these challenges, the DFS algorithm was still capable of reaching the goal after multiple replanning steps. These findings highlight both the robustness and limitations of DFS and suggest that integrating adaptive sensing or hybrid algorithms could improve performance in unpredictable, dynamic environments.
Co-Authors Budhi Martana Elvi Armadani MARTINI, NI PUTU DEVIRA AYU Mohraffi Azura Pasha