Generalised Regression Neural Network (GRNN) Architecture-Based Motion Planning and Control of an E-Puck Robot in V-REP Software Platform

Abstract 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.

Download Full-text

Intelligent Motion Planning and Control for Robotic Joints Using Bio-Inspired Spiking Neural Networks

International Journal of Humanoid Robotics ◽

10.1142/s0219843619500129 ◽

2019 ◽

Vol 16 (04) ◽

pp. 1950012 ◽

Cited By ~ 2

Author(s):

Mircea Hulea ◽

Adrian Burlacu ◽

Constantin-Florin Caruntu

Keyword(s):

Neural Network ◽

Neural Networks ◽

Motion Planning ◽

Control Method ◽

Inhibitory Neurons ◽

Robotic Arm ◽

Control Approach ◽

Planning And Control ◽

Robotic Joints ◽

And Control

This paper details an intelligent motion planning and control approach for a one-degree of freedom joint of a robotic arm that can be used to implement anthropomorphic robotic hands. This intelligent control method is based on bio-inspired electronic neural networks and contractile artificial muscles implemented with shape memory alloy (SMA) actuators. The spiking neural network (SNN) includes several excitatory neurons that naturally determine the contraction force of the actuators, and unevenly distributed inhibitory neurons that regulate the excitatory activity. To validate the proposed concept, the experiments highlight the motion planning and control of a single-joint robotic arm. The results show that the electronic neural network is able to intelligently activate motion and hold with high precision the mobile link to the target positions even if the arm is slightly loaded. These results are encouraging for the development of improved biologically plausible neural structures that are able to control simultaneously multiple muscles.

Download Full-text

Parking Motion Planning and Control for Motor Vehicles Using a Neural Network with Additional Rules.

TRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series C ◽

10.1299/kikaic.62.2172 ◽

1996 ◽

Vol 62 (598) ◽

pp. 2172-2177

Author(s):

Motoji YAMAMOTO ◽

Katsuki OHBA ◽

Akira MOHRI

Keyword(s):

Neural Network ◽

Motion Planning ◽

Motor Vehicles ◽

Planning And Control ◽

And Control

Download Full-text

Abundant Computer and Robot Experiments Verifying Minimum Joint Motion Planning and Control of Redundant Arms via Zhang Neural Network

10.1109/ijcnn52387.2021.9534059 ◽

2021 ◽

Author(s):

Wuyi Yang ◽

Jianrong Chen ◽

Yunong Zhang ◽

Jiansheng Sun ◽

Zhijun Zhang

Keyword(s):

Neural Network ◽

Motion Planning ◽

Joint Motion ◽

Planning And Control ◽

Zhang Neural Network ◽

And Control

Download Full-text

Parking Motion Planning and Control of a Car-Like Robot Using a Fuzzy Neural Network

Journal of Robotics and Mechatronics ◽

10.20965/jrm.1995.p0052 ◽

1995 ◽

Vol 7 (1) ◽

pp. 52-56 ◽

Cited By ~ 2

Author(s):

Motoji Yamamoto ◽

◽

Masaaki Kobayashi ◽

Akira Mohri

Keyword(s):

Neural Network ◽

Motion Planning ◽

Fuzzy Neural Network ◽

Control Method ◽

Difficult Problem ◽

Human Control ◽

Planning And Control ◽

Fuzzy Neural ◽

Neural Network System ◽

And Control

This paper discusses a parking motion planning and control of a car-like robot. Because of non-holonomic constraints of the system, motion planning and control is regarded as a difficult problem. In this paper, constraints of steering operation and obstacle avoidance with garage and walls are also considered. As one approach to this problem, extracting human control strategy can be considered, because many drivers can easily park their cars in garages. This paper proposes a motion planning and control method using a fuzzy neural network (FNN). The fuzzy neural network system for parking motion planning learns good parking motions by human operations to generate motion strategy of parking. The fuzzy neural network is then used for parking motion planning in a restricted area surrounded by walls. Computer simulation demonstrates the effectiveness of the planning method. Furthermore, the method can be considered as a feedback control law for the parking of car-like robot. Therefore, an experiment of parking motion control using the fuzzy neural network is also tested.

Download Full-text