Orientasi dan Perpindahan Robot Sepak Bola Beroda Tiga Roda Menggunakan Algoritma Potential Field Obstacle Avoidance

Rahmat Zidan, Amalia Prameswari Alvina, Putra Wisnu Agung Sucipto

Abstract


Penelitian ini membahas implementasi Potential Field Obstacle Avoidance (PFOA) pada robot beroda tiga untuk navigasi otonom di arena simulasi berukuran 10×10 m dengan variasi rintangan. PFOA memanfaatkan interaksi gaya atraktif F_att menuju target dan gaya repulsif F_rep dari rintangan untuk menghasilkan gaya total F_total yang disertai heading decision yang mengarahkan robot secara adaptif. Evaluasi dilakukan pada tiga skenario arena dan hasilnya dibandingkan dengan dua metode pembanding, yaitu Multistage Hybrid A* (RPP global heuristik) dan Bug Method (RPP lokal klasik). Analisis mencakup lintasan navigasi, distribusi orientasi, serta nilai certainty value (CV) yang merepresentasikan kedekatan robot terhadap hambatan. Hasil simulasi menunjukkan bahwa PFOA mampu menjaga kelancaran lintasan dan stabilitas orientasi dengan waktu tempuh yang kompetitif, Bug Method lebih cepat dan efisien namun kurang optimal pada manuver halus, sedangkan A* mengalami keterbatasan pada lingkungan padat karena kompleksitas iterasi dan manuver tajam. Secara komparatif, klasifikasi RPP menempatkan PFOA sebagai metode lokal yang unggul dalam menjaga keseimbangan antara kecepatan, keamanan, dan adaptivitas navigasi, sementara Bug Method relevan untuk skenario efisiensi sederhana, dan A* lebih sesuai pada kondisi ruang terbuka dengan kepadatan rintangan rendah.

Kata kunci— navigasi, potential field, certainty value, grid map, obstacle avoidance, path planning

Abstract

– This study presents the implementation of the Potential Field Obstacle Avoidance (PFOA) algorithm for three-wheeled mobile robot navigation in a 10×10 m simulated arena with varying obstacle configurations. PFOA combines an attractive force F_att toward the goal and a repulsive force F_rep from obstacles to generate a total force F_total with heading decision that adaptively guides the robot. The evaluation was conducted across three arena scenarios and benchmarked against two alternative approaches, namely the Multistage Hybrid A* (global heuristic RPP) and the Bug Method (classical local RPP). The analysis includes trajectory paths, orientation distribution, and certainty value (CV), which represents the robot’s proximity to obstacles. Simulation results show that PFOA achieves smooth trajectories and stable orientation with competitive travel times, the Bug Method offers faster and more efficient responses but lacks smooth maneuvering, while A* faces limitations in dense environments due to high computational complexity and sharp turns. In a comparative view, PFOA stands out as a local RPP method that balances speed, safety, and adaptivity, whereas the Bug Method is more suitable for efficient yet simple scenarios, and A* performs better in open spaces with lower obstacle density..

Keywords— Navigation, Potential Field, Certainty Value, Grid Mapping, Obstacle Avoidance, Path Planning

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