Your browser doesn't support javascript.
loading
Estimate the Unknown Environment with Biosonar Echoes-A Simulation Study.
Tanveer, Muhammad Hassan; Thomas, Antony; Ahmed, Waqar; Zhu, Hongxiao.
Afiliación
  • Tanveer MH; Department of Robotics and Mechatronics Engineering, Kennesaw State University, Marietta, GA 30060, USA.
  • Thomas A; Department of Informatics, Bioengineering, Robotics, and Systems Engineering, University of Genoa, 16145 Genoa, Italy.
  • Ahmed W; PAVIS, Istituto Italiano di Tecnologia, 16152 Genoa, Italy.
  • Zhu H; DITEN, University of Genoa, 16145 Genoa, Italy.
Sensors (Basel) ; 21(12)2021 Jun 18.
Article en En | MEDLINE | ID: mdl-34207193
ABSTRACT
Unmanned aerial vehicles (UAVs) have shown great potential in various applications such as surveillance, search and rescue. To perform safe and efficient navigation, it is vitally important for a UAV to evaluate the environment accurately and promptly. In this work, we present a simulation study for the estimation of foliage distribution as a UAV equipped with biosonar navigates through a forest. Based on a simulated forest environment, foliage echoes are generated by using a bat-inspired bisonar simulator. These biosonar echoes are then used to estimate the spatial distribution of both sparsely and densely distributed tree leaves. While a simple batch processing method is able to estimate sparsely distributed leaf locations well, a wavelet scattering technique coupled with a support vector machine (SVM) classifier is shown to be effective to estimate densely distributed leaves. Our approach is validated by using multiple setups of leaf distributions in the simulated forest environment. Ninety-seven percent accuracy is obtained while estimating thickly distributed foliage.
Asunto(s)
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Árboles / Bosques Idioma: En Revista: Sensors (Basel) Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Árboles / Bosques Idioma: En Revista: Sensors (Basel) Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos