Applying PBL in the Development and Modeling of kinematics for Robotic Manipulators with Interdisciplinarity between Computer-Assisted Project, Robotics, and Microcontrollers

• URL: http://arxiv.org/abs/2203.16927v1
• Date: Thu, 31 Mar 2022 10:01:24 GMT
• Title: Applying PBL in the Development and Modeling of kinematics for Robotic Manipulators with Interdisciplinarity between Computer-Assisted Project, Robotics, and Microcontrollers
• Authors: Afonso Henriques Fontes Neto Segundo, Joel Sotero da Cunha Neto, Paulo Cirillo Souza Barbosa, Raul Fontenele Santana
• Abstract summary: This article proposes the application of Project Based Learning (ABP) through the design, development, mathematical modeling of a robotic manipulator. It is an integrative project of the disciplines of Industrial Robotics, Microcontrollers and Computer Assisted Design with students of the Control and Automation Engineering of the University of Fortaleza.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Considering the difficulty of students in calculating the direct and inverse kinematics of a robotic manipulator using only conventional tools of a classroom, this article proposes the application of Project Based Learning (ABP) through the design, development, mathematical modeling of a robotic manipulator as an integrative project of the disciplines of Industrial Robotics, Microcontrollers and Computer Assisted Design with students of the Control and Automation Engineering of the University of Fortaleza. Once designed and machined, the manipulator arm was assembled using servo motors connected to a microcontroled prototyping board, to then have its kinematics calculated. At the end are presented the results that the project has brought to the learning of the disciplines on the optics of the tutor and students.

Related papers

• Unifying 3D Representation and Control of Diverse Robots with a Single Camera [48.279199537720714]
  We introduce Neural Jacobian Fields, an architecture that autonomously learns to model and control robots from vision alone. Our approach achieves accurate closed-loop control and recovers the causal dynamic structure of each robot.
  arXiv  Detail & Related papers  (2024-07-11T17:55:49Z)
• Towards Building AI-CPS with NVIDIA Isaac Sim: An Industrial Benchmark and Case Study for Robotics Manipulation [18.392301524812645]
  As a representative cyber-physical system (CPS), robotic manipulator has been widely adopted in various academic research and industrial processes. Recent studies in robotics manipulation have started employing artificial intelligence (AI) approaches as controllers to achieve better adaptability and performance. We propose a public industrial benchmark for robotics manipulation in this paper.
  arXiv  Detail & Related papers  (2023-07-31T18:21:45Z)
• Hybrid Paradigm-based Brain-Computer Interface for Robotic Arm Control [0.9176056742068814]
  Brain-computer interface (BCI) uses brain signals to communicate with external devices without actual control. We propose a knowledge distillation-based framework to manipulate robotic arm through hybrid paradigm induced EEG signals for practical use.
  arXiv  Detail & Related papers  (2022-12-14T08:13:10Z)
• Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems [79.07468367923619]
  We propose a backpropagation-free approach to robotic control through the neuro-cognitive computational framework of neural generative coding (NGC) We design an agent built completely from powerful predictive coding/processing circuits that facilitate dynamic, online learning from sparse rewards. We show that our proposed ActPC agent performs well in the face of sparse (extrinsic) reward signals and is competitive with or outperforms several powerful backprop-based RL approaches.
  arXiv  Detail & Related papers  (2022-09-19T16:49:32Z)
• Printable Flexible Robots for Remote Learning [0.0]
  Students design flexible robotic components using CAD software, upload their designs to a remote 3D printing station, monitor the print with a web camera, and inspect the components with lab staff. At the end of the course, students will have iterated through several designs and created fluidically-driven soft robots.
  arXiv  Detail & Related papers  (2022-07-15T19:51:54Z)
• Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots [57.27442333662654]
  Machine learning (ML) has become a pervasive tool across computing systems. Tiny robot learning is the deployment of ML on resource-constrained low-cost autonomous robots. Tiny robot learning is subject to challenges from size, weight, area, and power (SWAP) constraints. This paper gives a brief survey of the tiny robot learning space, elaborates on key challenges, and proposes promising opportunities for future work in ML system design.
  arXiv  Detail & Related papers  (2022-05-11T19:36:15Z)
• Aplica\c{c}\~ao de ros como ferramenta de ensino a rob\'otica / using ros as a robotics teaching tool [0.0]
  The study of robotic manipulators is the main goal of Industrial Robotics Class. This article aims to expose the use of the Robot Operation System (ROS) as a tool to develop a robotic arm.
  arXiv  Detail & Related papers  (2022-03-31T09:48:21Z)
• OpenDR: An Open Toolkit for Enabling High Performance, Low Footprint Deep Learning for Robotics [0.0]
  We present the Open Deep Learning Toolkit for Robotics (OpenDR) OpenDR aims at developing an open, non-proprietary, efficient, and modular toolkit that can be easily used by robotics companies and research institutions.
  arXiv  Detail & Related papers  (2022-03-01T12:59:59Z)
• V-MAO: Generative Modeling for Multi-Arm Manipulation of Articulated Objects [51.79035249464852]
  We present a framework for learning multi-arm manipulation of articulated objects. Our framework includes a variational generative model that learns contact point distribution over object rigid parts for each robot arm.
  arXiv  Detail & Related papers  (2021-11-07T02:31:09Z)
• Machine Learning-Based Automated Design Space Exploration for Autonomous Aerial Robots [55.056709056795206]
  Building domain-specific architectures for autonomous aerial robots is challenging due to a lack of systematic methodology for designing onboard compute. We introduce a novel performance model called the F-1 roofline to help architects understand how to build a balanced computing system. To navigate the cyber-physical design space automatically, we subsequently introduce AutoPilot.
  arXiv  Detail & Related papers  (2021-02-05T03:50:54Z)
• A Reference Software Architecture for Social Robots [64.86618385090416]
  We propose a series of principles that social robots may benefit from. These principles lay also the foundations for the design of a reference software architecture for Social Robots.
  arXiv  Detail & Related papers  (2020-07-09T17:03:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.