Optimized autonomous UAV design with obstacle avoidance capability

Tai, Jun Jet; Phang, Swee King; Wong, Yen Myan Felicia

doi:10.1063/5.0001372

Cited by 4 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Otro criterio relacionado es el número de ciclos de procesamiento, equivale al número aproximado de operaciones realizadas para completar una misión (Tai et al, 2020). Se debe tener en cuenta que diferentes tipos de robots pueden tener diferente tipo de procesador, es decir, diferente capacidad de computo, lo que influye el tiempo total del robot en completar la misión y la comparación de los algoritmos con este criterio.…”

Section: Tiempo En Alcanzar La Meta O Misiónunclassified

Criterios de desempeño para evaluar algoritmos de navegación de robots móviles: una revisión

Ceballos

Suarez-Rivera

2022

Rev. iberoam. autom. inform. ind.

View full text Add to dashboard Cite

En este artículo se presenta una revisión de literatura sobre criterios de desempeño para evaluar la navegación de un robot móvil, los cuales ayudan a comparar cuantitativamente diferentes características, como: el sistema de control, la navegación en diferentes entornos de trabajo, el desempeño energético, etc. El interés en criterios de desempeño y procedimiento de comparación (benchmarks) ha crecido en los últimos años, principalmente por investigadores y fabricantes de robots que buscan satisfacer la creciente demanda de aplicaciones en el mercado global, cada vez más competido. El conjunto de criterios está compuesto por métricas, índices, mediciones y benchmarks, desde el más básico como contabilizar el éxito en alcanzar la meta, pasando por otros más elaborados como los de seguridad en la trayectoria generada en la evasión de obstáculos, hasta criterios que comparan aspectos más complejos de la navegación como el consumo energético. Finalmente, se describen algunos benchmarks y software para simulación y comparación de algoritmos de navegación. Estos criterios se constituyen en una importante herramienta para diseñadores e investigadores en robótica móvil.

show abstract

Section: Tiempo En Alcanzar La Meta O Misiónunclassified

Criterios de desempeño para evaluar algoritmos de navegación de robots móviles: una revisión

Ceballos

Suarez-Rivera

2022

Rev. iberoam. autom. inform. ind.

View full text Add to dashboard Cite

show abstract

“…Therefore, this work is to introduce a 2-D navigation system for UAVs to navigate in a GPS denied environment by using Light Detection and Ranging (LIDAR) sensors and a programmed called Simultaneous Localization and Mapping (SLAM). SLAM is actually very important as it helps to provide data for certain application such as path planning via Obstacle Avoidance and Navigation (OAN) algorithm [5].…”

Section: Introductionmentioning

confidence: 99%

2-D UAV navigation solution with LIDAR sensor under GPS-denied environment

Phang

Mun

2021

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Unmanned aerial vehicle (UAV) is widely used by many industries these days such as militaries, agriculture, and surveillance. However, one of the main challenges of UAV is navigating through an environment where global positioning system (GPS) is being denied. The main purpose of this paper is to find a solution for UAV to be able to navigate in a GPS denied surrounding without affecting the drone flight performance. There are two ways to overcome these challenges such as using visual odometry (VO) or by using simultaneous localization and mapping (SLAM). However, VO has a drawback because camera sensors require good lighting which will affect the performance of the UAV when it is navigating through a low light intensity environment. Hence, in this paper 2-D SLAM will be use as a solution to help UAV to navigate under a GPS-denied environment with the help of a light detection and ranging (LIDAR) sensor which known as a LIDAR-based SLAM. This is because SLAM can help UAVs to localize itself and map the surrounding of the environment. The concept and idea of this paper will be fully simulated using MATLAB, where the drone navigation will be simulated in MATLAB to extract LIDAR data and to use the LIDAR data to carry out SLAM via pose graph optimization. Besides, the contribution to this research work has also identified that in pose graph optimization, the loop closure threshold and loop closure radius play an important role. The loop closure threshold can affect the accuracy of the trajectory of the drone and the accuracy of mapping the environment as compared to ground truth. On the other hand, the loop closure search radius can increase the processing speed of obtaining the data via pose graph optimization. The main contribution to this research work is shown that the processing speed can increase up to 45 % and the accuracy of the trajectory of the drone and the mapped surrounding is quite accurate as compared to ground truth.

show abstract