Naslov
Influence of Data Collection Parameters on Performance of Neural Network-based Obstacle Avoidance

Autori
Kružić, Stanko ; Musić, Josip ; Stančić, Ivo ; Papić, Vladan

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
3rd International Conference on Smart and Sustainable Technologies (SpliTech 2018) : proceedings / Lorenz, Pascal ; Nižetić, Sandro ; Jara, Antonio - Split : Fakultet elektrotehnike i računarstva Sveučilišta u Zagrebu, 2018

ISBN
978-953-290-081-1

Skup
3rd International Conference on Smart and Sustainable Technologies (SpliTech 2018)

Mjesto i datum
Split, Hrvatska, 26.06.2018. - 29.06.2018

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
obstacle avoidance ; neural networks ; simulation ; Lidar

Sažetak
Neural networks are becoming wide-spread, including applications in mobile robotics and related fields. Most state - of-the-art approaches to training neural networks use video cameras for generating training datasets. However, these data are hard and time-consuming to collect resulting in a bottleneck for neural network training procedure. Thus, the paper briefly presents simulation-based LiDAR data collection for the training of neural networks for obstacle avoidance. The influence of two data collection parameters in simulation (distance to obstacles and number of LiDAR points) on the performance of the realworld mobile robot is analysed in more depth. Experimental testing was performed in a narrow corridor (augmented with additional obstacles) in order to fully test the neural networks and detect possible limitations. For a better understanding of proposed algorithms and analysis of their performance in reallife scenarios, a simple test-bed was devised with Turtbebot 2 as a test vehicle. Based on obtained results, and with safety in mind, conclusions are drawn and possible future improvements proposed.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Temeljne tehničke znanosti

POVEZANOST RADA