- Tytuł:
- Generalised regression neural network (GRNN) architecture-based motion planning and control of an e-puck robot in V-REP software platform
- Autorzy:
-
Panwar, Vikas Singh
Pandey, Anish
Hasan, Muhammad Ehtesham - Powiązania:
- https://bibliotekanauki.pl/articles/2106239.pdf
- Data publikacji:
- 2021
- Wydawca:
- Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
- Tematy:
-
e-puck robot
generalised regression neural network architecture
virtual robot experimentation platform software
scattered obstacle
Infra-Red sensor - Opis:
- This article focuses on the motion planning and control of an automated differential-driven two-wheeled E-puck robot using Generalized Regression Neural Network (GRNN) architecture in the Virtual Robot Experimentation Platform (V-REP) software platform among scattered obstacles. The main advantage of this GRNN over the feedforward neural network is that it provides accurate results in a short period with minimal error. First, the designed GRNN architecture receives real-time obstacle information from the Infra-Red (IR) sensors of an E-puck robot. According to IR sensor data interpretation, this architecture sends the left and right wheel velocities command to the E-puck robot in the V-REP software platform. In the present study, the GRNN architecture includes the MIMO system, i.e., multiple inputs (IR sensors data) and multiple outputs (left and right wheel velocities). The three-dimensional (3D) motion and orientation results of the GRNN architecture-controlled E-puck robot are carried out in the V-REP software platform among scattered and wall-type obstacles. Further on, compared with the feedforward neural network, the proposed GRNN architecture obtains better navigation path length with minimum error results.
- Źródło:
-
Acta Mechanica et Automatica; 2021, 15, 4; 209--214
1898-4088
2300-5319 - Pojawia się w:
- Acta Mechanica et Automatica
- Dostawca treści:
- Biblioteka Nauki
Artykuł