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Wrapyfi: A Python Wrapper for Integrating Robots, Sensors, and Applications across Multiple Middleware.

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Bibliographic Details
Title:	Wrapyfi: A Python Wrapper for Integrating Robots, Sensors, and Applications across Multiple Middleware.
Authors:	Abawi, Fares, Allgeuer, Philipp, Fu, Di, Wermter, Stefan
Source:	ACM/IEEE International Conference on Human-Robot Interaction; Mar2024, p860-864, 5p
Subject Terms:	HUMAN-robot interaction, ROBOT control systems, MIDDLEWARE, COMMUNICATION models, COMMUNICATION patterns, DEEP learning
Abstract:	Message oriented and robotics middleware play an important role in facilitating robot control, abstracting complex functionality, and unifying communication patterns between sensors and devices. However, using multiple middleware frameworks presents a challenge in integrating different robots within a single system. To address this challenge, we present Wrapyfi, a Python wrapper supporting multiple message oriented and robotics middleware, including ZeroMQ, YARP, ROS, and ROS 2. Wrapyfi also provides plugins for exchanging deep learning framework data, without additional encoding or preprocessing steps. Using Wrapyfi eases the development of scripts that run on multiple machines, thereby enabling cross-platform communication and workload distribution. We finally present the three communication schemes that form the cornerstone of Wrapyfi's communication model, along with examples that demonstrate their applicability. [ABSTRACT FROM AUTHOR]
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Database:	Complementary Index

Description
Abstract:	Message oriented and robotics middleware play an important role in facilitating robot control, abstracting complex functionality, and unifying communication patterns between sensors and devices. However, using multiple middleware frameworks presents a challenge in integrating different robots within a single system. To address this challenge, we present Wrapyfi, a Python wrapper supporting multiple message oriented and robotics middleware, including ZeroMQ, YARP, ROS, and ROS 2. Wrapyfi also provides plugins for exchanging deep learning framework data, without additional encoding or preprocessing steps. Using Wrapyfi eases the development of scripts that run on multiple machines, thereby enabling cross-platform communication and workload distribution. We finally present the three communication schemes that form the cornerstone of Wrapyfi's communication model, along with examples that demonstrate their applicability. [ABSTRACT FROM AUTHOR]
DOI:	10.1145/3610977.3637471