Planeaci&amp;#x00F3;n de trayectorias para un m&amp;#x00F3;vil rob&amp;#x00F3;tico en un ambiente 3D

Moreno, Róbinson Jiménez; Brito, M Laura

doi:10.1109/argencon.2014.6868483

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…However, artificial intelligence techniques are being used with remarkable success in the planning of trajectories. In Jiménez-Moreno and Brito [16], fuzzy logic algorithms are used to determine the path of the robot's movement, and in Moteir et al [17] and Wang et al [18], the authors employ deep learning techniques based on convolutional neural networks (CNN) [19]. For locational tasks, regressions CNN has shown great robustness [20].…”

Section: Introductionmentioning

confidence: 99%

Path Planning for Robotic Training in Virtual Environments using Deep Learning

Pinzón-Arenas¹,

Moreno²,

Rubiano³

2022

International Journal on Advanced Science, Engineering and Information Technology

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Reducing costs in the acquisition of industrial robots and their benefit in continuous production workdays have increased the number of investigations that expand the robot's action capabilities. This work proposes a trajectory planning system for a UR3 robot that is very usual in academic, research, and industrial applications. The system is presented based on a convolutional network training for regression tasks focused on learning the desired trajectory. A virtual environment has been developed to simulate different trajectories based on the interaction between UR3 robot and object detection and location through the convolutional network employed. This work exposes the network's training and the results of the transport of the object, where the robot can position itself on the desired tool (scissors and screwdriver), which is recognized by training a Faster network R-CNN and the re-localization of the tool in a conveyor band. For the trained trajectory's, a ResNet-50 model is proposed, and the overall performance achieved was 92.63%, with a mean square error of 24.7 mm in the trained trajectory's repetition. Also, the boxplot of each ax in the trajectory is exposed since they show in a more detailed way the deviation of each of the points in the whole validation set. The average collection time, from when the system takes the workspace capture to its initial positioning after leaving the tool on the belt, was 51.3 seconds, enough for real-time applications.

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Section: Introductionmentioning

confidence: 99%

Path Planning for Robotic Training in Virtual Environments using Deep Learning

Pinzón-Arenas¹,

Moreno²,

Rubiano³

2022

International Journal on Advanced Science, Engineering and Information Technology

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“…In the state of art, many works about mobile robots are done. The main idea is making its auto selfdriving [9,10] using planning trajectories for this task, in 2D [11] and 3D [12] environments, considering energy-aware [13], terrains characteristics [14] and implementing optimization methods [15] 1309 machine vision systems are very useful for auto self-driving, to control the mobile robot [16] and avoid obstacles [17], such as presented in the present work. The article is divided into four main parts, the first part presents some theoretical foundations necessary for the understanding of the other stages.…”

Section: Introductionmentioning

confidence: 99%

Robotic navigation algorithm with machine vision

Pachón¹,

Enciso-Aragon²,

Moreno³

2020

IJECE

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In the field of robotics, it is essential to know the work area in which the agent is going to develop, for that reason, different methods of mapping and spatial location have been developed for different applications. In this article, a machine vision algorithm is proposed, which is responsible for identifying objects of interest within a work area and determining the polar coordinates to which they are related to the observer, applicable either with a fixed camera or in a mobile agent such as the one presented in this document. The developed algorithm was evaluated in two situations, determining the position of six objects in total around the mobile agent. These results were compared with the real position of each of the objects, reaching a high level of accuracy with an average error of 1.3271% in the distance and 2.8998% in the angle.

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Artificial Vision for Manufacturing System

Mario¹,

Ignacio²

2016

Recent Advances in Image and Video Coding

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Artificial vision for manufacturing is a very important step when the process is automatic, so to improve productivity with high quality, this investigation presents a new method for manufacturing by applying artificial intelligence for tool selection; in the proposed method, a camera takes a picture and artificial program is applied to process the image and thus generate it to the size of the cutting tool. This chapter describes the development of a new method by programming using artificial vision to select the cutting tool. The experimental results show that the combination of artificial vision and programming is capable of selecting the correct tool.

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Planeación de trayectorias para un móvil robótico en un ambiente 3D

Cited by 3 publications

References 16 publications

Path Planning for Robotic Training in Virtual Environments using Deep Learning

Path Planning for Robotic Training in Virtual Environments using Deep Learning

Robotic navigation algorithm with machine vision

Artificial Vision for Manufacturing System

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