Otonom Robotlar İçin KU-MCL Tabanlı Yeni Bir Hibrit Konum Belirleme Algoritması Tasarımı ve Uygulaması

Year 2024, Volume: 1 Issue: 1, 6 - 16, 20.07.2024

Ozan Vahit Altınpınar Volkan Sezer

Abstract

Lokalizasyon, bir konum tahmin problemidir ve otonom mobil robotlar üzerine yapılan çalışmalar arasında en kritik öneme sahip alanlardan biridir. Özellikle başlangıç anında robot kendi konumunu bilmiyorsa problemin zorluğu daha da artmaktadır. Robotun başlangıç anında kendi konumunu bilmemesi problemine global lokalizasyon problemi denilmiştir ve bu problemi çözmek için literatürde parçacık filtre tabanlı lokalizasyon algoritmalar mevcuttur. Bu çalışmada ise bir global lokalizasyon algoritması olan ve başlangıç anında parçacıkların daha akıllı ve efektif bir şekilde harita üzerine atanmasını sağlayan enerji tabanlı Kendinden Uyarlamalı Monte Carlo Lokalizasyon (KU-MCL) algoritması incelenerek benzer enerji bölgelerinin daha optimal bir şekilde belirlenebilmesi için bir yöntem önerilmiştir. Bunun yanında KU-MCL algoritması nispeten daha az parçacık kullanan standart MCL algoritması ile hibrit olarak çalıştığında, orijinal KU-MCL algoritmasına göre daha doğru ve güvenilir konum tahminlerinin yapıldığı simülasyon ve gerçek ortam deneyleri ile gösterilmiştir.

Keywords

Otonom robotlar, Lokalizasyon, KU-MCL, Standart MCL

Design and Implementation of a New Hybrid Localization Algorithm Based on SA-MCL for Autonomous Robots

Abstract

Localization is a position estimation problem and is one of the most critical areas of research in the field of autonomous mobile robots. Particularly, the difficulty of the problem increases when the robot lacks knowledge of its initial position. This situation is referred to as the global localization problem, and particle filter-based algorithms have been proposed in the literature to address this issue. In this study, we investigate an energy-based Self Adaptive Monte Carlo Localization (SA-MCL) algorithm, which is a global localization algorithm, and propose a method to enhance the determination of similar energy regions more optimally on the map during the initialization phase. Furthermore, we demonstrate through simulations and real-world experiments that when the SA-MCL algorithm is used in a hybrid manner with the standard MCL algorithm, which employs relatively fewer particles, it provides more accurate and reliable position estimates compared to the original SA-MCL algorithm.

Keywords

Autonomous robots, Localization, SA-MCL, Standard MCL

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