The Impact of Social Force Model Parameters On Frontier-Based Exploration Performance

DOI: https://doi.org/10.24003/emitter.v13i2.994
Keywords: Autonomous exploration, Social Force Model (SFM), Frontier-based exploration, TurtleBot3, Obstacle avoidance, SLAM

Abstract

Autonomous exploration is one of the most challenging tasks in mobile robotics, particularly in environments that contain dynamic obstacles and require fully autonomous mapping without human intervention. This study addresses the dual problem of enabling navigation in the presence of potential static obstacles and achieving autonomous map building. To solve this, we utilize the Social Force Model (SFM), which offers a behavior-based approach suitable for dynamic and uncertain environments. The objective of this research is to investigate how different SFM parameters—Gain (ks), Radius (rR), and Effective range (ψs)—influence the effectiveness of autonomous exploration. Experiments were conducted using a TurtleBot3 robot in a simulated 155 m² environment, where various parameter combinations were tested. Evaluation metrics included mapping completion, failure types, travel distance, and exploration duration. Results indicate that tuning the SFM parameters significantly affects the robot's ability to explore autonomously and avoid obstacles. Extremely low parameter values led to collisions, while excessively high values caused unstable or inefficient behavior. The Radius parameter had a major impact on spatial awareness, and moderate effective range values contributed to stable tracking. Furthermore, higher frontier sensing latency resulted in longer exploration times. This study provides practical insights into the sensitivity of SFM parameters and offers guidance for optimizing navigation systems for fully autonomous exploration in both simulated and real-world settings. 

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Author Biographies

Bima Sena Bayu Dewantara Dewantara, Politeknik Elektronika Negeri Surabaya

Bima Sena Bayu Dewantara is a lecturer in the department of Computer Engineering from Politeknik Elektronika Negeri Surabaya, Indonesia. He received the B.Eng degree in Information Technology from Politeknik Elektronika Negeri Surabaya, Indonesia, the M.Eng. degree in Electrical Engineering from Sepuluh Nopember Institute of Technology, Indonesia, and the Dr. Eng. degree in Computer Science and Engineering from Toyohashi University ofTechnology, Japan, in 2004, 2010, and 2016, respectively. Currently, he works in the major field of autonomous intelligent systems that cover pattern recognition, computer vision, machine learning, signal processing, robotics system, and human-machine interaction.

Setiawardhana, Politeknik Elektronika Negeri Surabaya

Setiawardhana is a lecturer in the Department of Computer Engineering from Politeknik Elektronika Negeri Surabaya, Indonesia. He received the bachelor’s, master’s and doctoral degrees in electronics engineering from Institut Teknologi Sepuluh Nopember, Indonesia, in 2000, 2010, and 2021. He also a member of IEEE with member number #98144593. His research interests include the areas of artificial intelligents, image processing, internet of things, and robotics.

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Published
2025-12-17
How to Cite
Irsyad, A., Dewantara, B. S. B. D., & Setiawardhana. (2025). The Impact of Social Force Model Parameters On Frontier-Based Exploration Performance. EMITTER International Journal of Engineering Technology, 13(2), 268-291. https://doi.org/10.24003/emitter.v13i2.994
Issue
Vol 13 No 2 (2025)
Section
Articles

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