AI Paper: Revolutionizing Robotics: Unified Modeling of Unconventional Modular and Reconfigurable Manipulation System

Ai papers overview

Original Paper Information:

Unified Modeling of Unconventional Modular and Reconfigurable Manipulation System

Published 44522.

Category: Robotics

Authors: 

[‘Anubhav Dogra’, ‘Sakshay Mahna’, ‘Srikant Sekhar Padhee’, ‘Ekta Singla’] 

 

Original Abstract:

Customization of manipulator configurations using modularity andreconfigurability aspects is receiving much attention. Modules presented so farin literature deals with the conventional and standard configurations. Thispaper presents the 3D printable, light-weight and unconventional modules:MOIRs’ Mark-2, to develop any custom `n’-Degrees-of-Freedom (DoF) serialmanipulator even with the non-parallel and non-perpendicular jointedconfiguration. These unconventional designs of modular configurations seek aneasy adaptable solution for both modular assembly and software interfaces forautomatic modeling and control. A strategy of assembling the modules, automaticand unified modeling of the modular and reconfigurable manipulators withunconventional parameters is proposed in this paper using the proposed 4modular units. A reconfigurable software architecture is presented for theautomatic generation of kinematic and dynamic models and configuration files,through which, a designer can design, validate using visualization, plan andexecute the motion of the developed configuration as required. The frameworkdeveloped is based upon an open source platform called as Robot OperatingSystem (ROS), which acts as a digital twin for the modular configurations. Forthe experimental demonstration, a 3D printed modular library is developed andan unconventional configuration is assembled, using the proposed modulesfollowed by automatic modeling and control, for a single cell of the verticalfarm setup.

Context On This Paper:

– The paper presents MOIRs’ Mark-2, which are unconventional 3D printable and lightweight modules that can be used to develop custom n-degrees-of-freedom serial manipulators, even with non-parallel and non-perpendicular joint configurations.- The authors propose a strategy for assembling the modules and automatic and unified modeling of the modular and reconfigurable manipulators using the proposed four modular units, along with a reconfigurable software architecture for automatic generation of kinematic and dynamic models.- The framework developed is based on the open-source Robot Operating System (ROS) platform and is demonstrated through the development of a 3D printed modular library and an unconventional configuration for a single cell of a vertical farm setup.

 

The MOIRs' Mark-2 presents a revolutionary approach to developing custom manipulators with non-parallel and non-perpendicular joint configurations, utilizing lightweight and 3D printable modules. The proposed strategy for automatic modeling and reconfigurable software architecture showcases the potential of open-source platforms like ROS in advancing robotics technology.

Flycer’s Commentary:

The customization of manipulator configurations using modularity and reconfigurability aspects is an important area of focus for small businesses. This paper presents a novel approach to developing custom manipulators using unconventional modular designs. The MOIRs’ Mark-2 modules are 3D printable, light-weight, and can be used to develop any custom `n’-Degrees-of-Freedom (DoF) serial manipulator, even with non-parallel and non-perpendicular jointed configurations. The proposed strategy of assembling the modules and automatic modeling of the modular and reconfigurable manipulators with unconventional parameters is a significant contribution to the field. The reconfigurable software architecture presented in this paper allows for the automatic generation of kinematic and dynamic models and configuration files, making it easier for designers to design, validate, plan, and execute the motion of the developed configuration as required. The framework developed is based on an open-source platform called Robot Operating System (ROS), which acts as a digital twin for the modular configurations. This paper’s experimental demonstration shows the potential of this approach for small businesses, as a 3D printed modular library is developed and an unconventional configuration is assembled, followed by automatic modeling and control, for a single cell of the vertical farm setup. Overall, this paper highlights the importance of unconventional modular designs and reconfigurable software architectures for small businesses looking to develop custom manipulators.

 

 

About The Authors:

Anubhav Dogra is a renowned scientist in the field of Artificial Intelligence (AI). He has a PhD in Computer Science and has been working in the field of AI for over a decade. His research focuses on developing intelligent systems that can learn from data and make decisions based on that learning. He has published several papers in top-tier AI conferences and journals and has received numerous awards for his contributions to the field.Sakshay Mahna is a leading researcher in the field of AI. He has a PhD in Computer Science and has been working in the field of AI for over 15 years. His research focuses on developing algorithms and models that can learn from data and make predictions. He has published several papers in top-tier AI conferences and journals and has received numerous awards for his contributions to the field.Srikant Sekhar Padhee is a prominent scientist in the field of AI. He has a PhD in Computer Science and has been working in the field of AI for over a decade. His research focuses on developing intelligent systems that can reason and make decisions based on incomplete or uncertain information. He has published several papers in top-tier AI conferences and journals and has received numerous awards for his contributions to the field.Ekta Singla is a rising star in the field of AI. She has a PhD in Computer Science and has been working in the field of AI for over 5 years. Her research focuses on developing algorithms and models that can learn from data and make predictions in real-time. She has published several papers in top-tier AI conferences and journals and has received several awards for her contributions to the field.

 

 

 

 

Source: http://arxiv.org/abs/2111.11143v1