Autonomous Robot Navigation Based on Pattern Recognition Techniques and Artificial Neural Networks

Quiñonez, Yadira; Ramírez, Mario; Lizárraga, Carmen; Tostado, Iván; Bekios, Juan

doi:10.1007/978-3-319-18833-1_34

Cited by 19 publications

(13 citation statements)

References 20 publications

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“…SCG is a fast, fully-automatic and robust optimization algorithm used in wide range of applications from prediction, classification to pattern recognition purposes etc. [14,15]. SCG is found to be as fast as LM on function approximation but is faster than LM in cases with large datasets [13].…”

Section: Fig 10 Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

Characterization of a Mobile Waste-Robot: A Heuristic Method to Path Planning using Artificial Neural Networks

Corpuz

2019

IJRTE

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In the field of mobile robotics, path planning is one of the most widely-sought areas of interest due to its nature of complexity, where such issue is also practically evident in the case of mobile robots used for waste disposal purposes. To overcome issues on path planning, researchers have studied various classical and heuristic methods, however, the extent of optimization applicability and accuracy still remain an opportunity for further improvements. This paper presents the exploration of Artificial Neural Networks (ANN) in characterizing the path planning capability of a mobile waste-robot in order to improve navigational accuracy and path tracking time. The author utilized proximity and sound sensors as input vectors, dual H-bridge Direct Current (DC) motors as target vectors, and trained the ANN model using Levenberg-Marquardt (LM) and Scaled Conjugate (SCG) algorithms. Results revealed that LM was significantly more accurate than SCG algorithm in local path planning with Mean Square Error (MSE) values of 1.75966, 2.67946, and 2.04963, and Regression (R) values of 0.995671, 0.991247, and 0.983187 in training, testing, and validation environments, respectively. Furthermore, based on simulation results, LM was also found to be more accurate and faster than SCG with Pearson R correlation coefficients of rx=.975, nx=6, px=0.001 and ry=.987, ny=6, py=0.000 and path tracking time of 8.47s.

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Section: Fig 10 Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

Characterization of a Mobile Waste-Robot: A Heuristic Method to Path Planning using Artificial Neural Networks

Corpuz

2019

IJRTE

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“…This robot, known as Turtlebot-II, is often utilized in literature for preliminary studies and offers an easy and modular structure for attaching of different types of sensors. In particular, due to the compatibility with the Robot Operating System (ROS), it is frequently preferred by researchers and allows the use of different types of algorithms [13].…”

Section: A Robot and Algorithmmentioning

confidence: 99%

Sensor Comparison for a Real-Time SLAM Application

Korkmaz¹,

Durdu²,

Tusun³

2018

IJIEE

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His main studies are fractional control theory and mobile robotics. Akif Durdu has been an assistant professor with the Eletrical-Electronics Engineering Department at the Selcuk University (SU) since 2013. He earned a PhD degree in eletrical-electronics engineering from the Middle East Technical University (METU), in 2012. He received his B.Sc. degree in eletrical-electronics engineering in 2001 at the Selcuk University. His research interests include mechatronic design, search & rescue robotics, robot manipulators, human-robot interaction, multi-robots networks and sensor networks. Dr. Durdu is teaching courses in control engineering, robotic and mechatronic systems Yunus Emre Tusun received the B.Sc. degrees

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“…Currently, robot consciousness [6] is an emerging field that addresses the problem of designing and implementing computational models of consciousness in a robot [7] [8] [9] [10]. Models of self-observation in learning [11] and machine vision [12] have been significantly developed in the last two decades [13].…”

Section: Introduction and Hypothetical Modelmentioning

confidence: 99%

Astrocyte-Synapse Receptor Coupling in Tripartite Synapses: A Mechanism for Self-Observing Robots

Mitterauer¹

2018

ABB

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A model of an intentional self-observing system is proposed based on the structure and functions of astrocyte-synapse interactions in tripartite synapses. Astrocyte-synapse interactions are cyclically organized and operate via feedforward and feedback mechanisms, formally described by proemial counting. Synaptic, extrasynaptic and astrocyte receptors are interpreted as places with the same or different quality of information processing described by the combinatorics of tritograms. It is hypothesized that receptors on the astrocytic membrane may embody intentional programs that select corresponding synaptic and extrasynaptic receptors for the formation of receptorreceptor complexes. Basically, the act of self-observation is generated if the actual environmental information is appropriate to the intended observation processed by receptor-receptor complexes. This mechanism is implemented for a robot brain enabling the robot to experience environmental information as "its own". It is suggested that this mechanism enables the robot to generate matches and mismatches between intended observations and the observations in the environment, based on the cyclic organization of the mechanism. In exploring an unknown environment the robot may stepwise construct an observation space, stored in memory, commanded and controlled by the intentional self-observing system. Finally, the role of self-observation in machine consciousness is shortly discussed.

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Autonomous Robot Navigation Based on Pattern Recognition Techniques and Artificial Neural Networks

Cited by 19 publications

References 20 publications

Characterization of a Mobile Waste-Robot: A Heuristic Method to Path Planning using Artificial Neural Networks

Characterization of a Mobile Waste-Robot: A Heuristic Method to Path Planning using Artificial Neural Networks

Sensor Comparison for a Real-Time SLAM Application

Astrocyte-Synapse Receptor Coupling in Tripartite Synapses: A Mechanism for Self-Observing Robots

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