TY - CHAP

T1 - Experimental Evaluation of Data Fusion Techniques and Adaptive Control for Mobile Robot Localization

AU - Neto, Wolmar Araújo

AU - Villa, Daniel Khede Dourado

AU - Sarcinelli-Filho, Mário

AU - Dos Santos, Murillo Ferreira

AU - Lima, José

AU - Pereira, Ana Isabel

AU - De Morais, Maurício Herche Fófano

AU - Mercorelli, Paolo

N1 - Publisher Copyright: © 2025 IEEE.

PY - 2025

Y1 - 2025

N2 - This study presents an approach to improving the localization of mobile robots in a controlled test environment through sensor fusion. Currently, the position of the ground robot (Unmanned Ground Vehicle (UGV) P3DX) can be estimated using an optimization algorithm or another technique based on a previously known map. In contrast, the aerial robot (Unmanned Aerial Vehicle (UAV) Bebop Parrot 2) can determine its relative position by detecting a marker on the ground robot using the Robot Operating System (ROS) WhyCon package. However, ambiguities or disturbances may compromise the accuracy of the robots' localization. To mitigate these limitations, Ultra-WideBand (UWB) sensors were incorporated into the system, enabling a more robust data fusion by integrating information from multiple sensors. Additionally, a filter was applied to reduce the impact of high-frequency noise on position estimation. Accurate localization test data were used to construct a simulated scenario and analyze the performance of the proposed approach. The simulation results demonstrate that sensor fusion, combined with noise filtering, significantly improves localization accuracy, making the approach promising for applications in mobile robot navigation.

AB - This study presents an approach to improving the localization of mobile robots in a controlled test environment through sensor fusion. Currently, the position of the ground robot (Unmanned Ground Vehicle (UGV) P3DX) can be estimated using an optimization algorithm or another technique based on a previously known map. In contrast, the aerial robot (Unmanned Aerial Vehicle (UAV) Bebop Parrot 2) can determine its relative position by detecting a marker on the ground robot using the Robot Operating System (ROS) WhyCon package. However, ambiguities or disturbances may compromise the accuracy of the robots' localization. To mitigate these limitations, Ultra-WideBand (UWB) sensors were incorporated into the system, enabling a more robust data fusion by integrating information from multiple sensors. Additionally, a filter was applied to reduce the impact of high-frequency noise on position estimation. Accurate localization test data were used to construct a simulated scenario and analyze the performance of the proposed approach. The simulation results demonstrate that sensor fusion, combined with noise filtering, significantly improves localization accuracy, making the approach promising for applications in mobile robot navigation.

KW - Adaptive Control

KW - Data Fusion

KW - Position Estimation

KW - UAV

KW - UGV

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=105008967312&partnerID=8YFLogxK

U2 - 10.1109/ICCC65605.2025.11022810

DO - 10.1109/ICCC65605.2025.11022810

M3 - Article in conference proceedings

AN - SCOPUS:105008967312

T3 - Proceedings of the 2025 26th International Carpathian Control Conference, ICCC 2025

BT - Proceedings of the 2025 26th International Carpathian Control Conference, ICCC 2025

A2 - Kacur, Jan

A2 - Skovranek, Tomas

A2 - Laciak, Marek

A2 - Mojzisova, Andrea

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 26th International Carpathian Control Conference, ICCC 2025

Y2 - 19 May 2025 through 21 May 2025

ER -