scholarly journals Design of Neural Network Mobile Robot Motion Controller

10.5772/7584 ◽  
2010 ◽  
Author(s):  
Jasmin Velagic ◽  
Nedim Osmic ◽  
Bakir Lacevic
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Robot Motion ◽  
Motion Controller

scholarly journals Predicting the Energy Consumption of a Robot in an Exploration Task Using Optimized Neural Networks

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 920
Author(s):  
Liesle Caballero ◽  
Álvaro Perafan ◽  
Martha Rinaldy ◽  
Winston Percybrooks
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Mobile Robot ◽  
Energy Budget ◽  
Dynamic Models ◽  
Pearson Correlation ◽  
Experimental Conditions ◽  
Grid Map ◽  
Proposed Model ◽  
Exploration Task

This paper deals with the problem of determining a useful energy budget for a mobile robot in a given environment without having to carry out experimental measures for every possible exploration task. The proposed solution uses machine learning models trained on a subset of possible exploration tasks but able to make predictions on untested scenarios. Additionally, the proposed model does not use any kinematic or dynamic models of the robot, which are not always available. The method is based on a neural network with hyperparameter optimization to improve performance. Tabu List optimization strategy is used to determine the hyperparameter values (number of layers and number of neurons per layer) that minimize the percentage relative absolute error (%RAE) while maximize the Pearson correlation coefficient (R) between predicted data and actual data measured under a number of experimental conditions. Once the optimized artificial neural network is trained, it can be used to predict the performance of an exploration algorithm on arbitrary variations of a grid map scenario. Based on such prediction, it is possible to know the energy needed for the robot to complete the exploration task. A total of 128 tests were carried out using a robot executing two exploration algorithms in a grid map with the objective of locating a target whose location is not known a priori by the robot. The experimental energy consumption was measured and compared with the prediction of our model. A success rate of 96.093% was obtained, measured as the percentage of tests where the energy budget suggested by the model was enough to actually carry out the task when compared to the actual energy consumed in the test, suggesting that the proposed model could be useful for energy budgeting in actual mobile robot applications.

scholarly journals The Motion Controller Based on Neural Network S-plane Model for Fixed-Wing UAVs

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Pengyun Chen ◽  
Guobing Zhang ◽  
Tong Guan ◽  
Meini Yuan ◽  
Jian Shen
Keyword(s):  
Neural Network ◽  
Motion Controller ◽  
Plane Model

Constraint-following servo control for an underactuated mobile robot under hard constraints

2021 ◽  
pp. 095965182110248
Author(s):  
Duo Fu ◽  
Jin Huang ◽  
Wen-Bin Shangguan ◽  
Hui Yin
Keyword(s):  
Mobile Robot ◽  
Control Problem ◽  
Servo Control ◽  
Robot Motion ◽  
Soft Constraints ◽  
Rigorous Proof ◽  
Control Approach ◽  
Hard Constraints

This article formulates the control problem of underactuated mobile robot as servo constraint-following, and develops a novel constraint-following servo control approach for underactuated mobile robot under both servo soft and hard constraints. Servo soft constraints are expressed as equalities, which may be holonomic or non-holonomic. Servo hard constraints are expressed as inequalities. It is required that the underactuated mobile robot motion eventually converges to servo soft constraints, and satisfies servo hard constraints at all times. Diffeomorphism is employed to incorporate hard constraints into soft constraints, yielding new soft constraints to relax hard constraints. By this, we design a constraint-following servo control based on the new servo soft constraints, which drives the system to strictly follow the original servo soft and hard constraints. The effectiveness of the proposed approach is proved by rigorous proof and simulations.

scholarly journals A Human Aware Mobile Robot Motion Planner

2007 ◽  
Vol 23 (5) ◽  
pp. 874-883 ◽  
Author(s):  
E.A. Sisbot ◽  
L.F. Marin-Urias ◽  
R. Alami ◽  
T. Simeon
Keyword(s):  
Mobile Robot ◽  
Robot Motion
Sign in / Sign up

Export Citation Format

Share Document