Frontier-Based Detection and Social Force Model for Autonomous Environment Mapping and Navigation

Asyam Irsyad; Bima Sena Bayu Dewantara; Setiawardhana

doi:10.31436/iiumej.v26i3.3554

Authors

Asyam Irsyad Electronic Engineering Polytechnic Institute of Surabaya https://orcid.org/0009-0005-8656-5890
Bima Sena Bayu Dewantara Politeknik Elektronika Negeri Surabaya https://orcid.org/0000-0001-9363-9695
Setiawardhana Politeknik Elektronika Negeri Surabaya https://orcid.org/0000-0002-4160-8835

DOI:

https://doi.org/10.31436/iiumej.v26i3.3554

Keywords:

Autonomous Navigation, Social Force Model (SFM), Simultaneous Localization and Mapping (SLAM), Frontier Detection, Robot Exploration

Abstract

This paper presents a method for static environment mapping using an autonomous mobile robot by integrating the Social Force Model (SFM) for obstacle avoidance and frontier-based detection for dynamic goal selection. The objective is to enable the robot to autonomously explore unknown environments, avoid obstacles, and generate accurate maps. The system was developed and tested in the Gazebo simulator, with RViz employed to visualize real-time sensor data, including lidar and odometry. Two frontier point selection strategies were evaluated: one based solely on the nearest distance, and another incorporating both distance and orientation. Experimental results demonstrate that the first strategy enabled the robot to complete the mapping task in an average of 10.66 minutes over a distance of 76.37 meters. In contrast, the second strategy, although more directionally aware, resulted in a longer duration of 14 minutes and a travel distance of 104.08 meters. These outcomes highlight the impact of map update delays and suboptimal frontier point selection on overall navigation efficiency. The findings suggest that while the system exhibits promising autonomous navigation capabilities, further improvements in frontier map update speed and goal point optimization are necessary to enhance path efficiency and reduce unnecessary movement.

ABSTRAK: Kertas ini membentangkan kaedah pemetaan persekitaran statik menggunakan robot mudah alih autonomi dengan mengintegrasikan Model Daya Sosial (SFM) bagi mengelak halangan dan pengesanan berasaskan sempadan bagi pemilihan matlamat dinamik. Objektifnya adalah untuk robot meneroka persekitaran yang tidak diketahui secara autonomi, mengelak halangan, dan menjana peta dengan tepat. Sistem ini dibangunkan dan diuji dalam simulator Gazebo, manakala RViz digunakan bagi memvisualisasikan data sensor masa nyata termasuk input lidar dan odometri. Dua strategi pemilihan titik frontier telah dinilai: satu berdasarkan jarak terdekat sahaja, dan satu lagi yang mengambil kira jarak serta orientasi. Dapatan eksperimen menunjukkan bahawa strategi pertama membolehkan robot melengkapkan tugas pemetaan dalam purata masa 10.66 minit dengan jarak perjalanan 76.37 meter. Manakala, strategi kedua, walaupun lebih peka terhadap arah, memerlukan masa yang lebih lama iaitu 14 minit dengan jarak perjalanan 104.08 meter. Dapatan kajian ini menunjukkan bahawa kelewatan dalam mengemas kini peta dan pemilihan titik frontier yang kurang optimum memberi kesan kepada kecekapan navigasi keseluruhan. Walaupun sistem ini menunjukkan kebolehan navigasi autonomi yang baik, penambahbaikan dalam kelajuan kemas kini peta frontier dan pengoptimuman pemilihan titik matlamat diperlukan bagi meningkatkan kecekapan laluan dan mengurangkan pergerakan yang tidak perlu.

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