&lt;i&gt;AutoRobot&lt;/i&gt;: A Multi-Agent Software Framework for Autonomous Robots

Liu, Zhe; Mao, Xinjun; Yang, Shuo

doi:10.1587/transinf.2017edp7382

Cited by 5 publications

(2 citation statements)

References 21 publications

(24 reference statements)

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“…Path planning is purely an optimization problem as it requires optimizing the cost from source to the destination. It is an NP-hard problem and therefore, there is a need for a framework for autonomous robots [15] to optimally find their paths. In [22], the problem has been solved by dividing time into the safe and unsafe interval and acting accordingly.…”

Section: Related Workmentioning

confidence: 99%

Robot navigation and target capturing using nature-inspired approaches in a dynamic environment

Verma

Saxena

Tiwari

2020

2020 10th International Conference on Cloud Computing, Data Science &Amp; Engineering (Confluence)

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Path Planning and target searching in a three-dimensional environment is a challenging task in the field of robotics. It is an optimization problem as the path from source to destination has to be optimal. This paper aims to generate a collision-free trajectory in a dynamic environment. The path planning problem has sought to be of extreme importance in the military, search and rescue missions and in life-saving tasks. During its operation, the unmanned air vehicle operates in a hostile environment, and faster replanning is needed to reach the target as optimally as possible. This paper presents a novel approach of hierarchical planning using multiresolution abstract levels for faster replanning. Economic constraints like path length, total path planning time and the number of turns are taken into consideration that mandate the use of cost functions. Experimental results show that the hierarchical version of GSO gives better performance compared to the BBO, IWO and their hierarchical versions.

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Section: Related Workmentioning

confidence: 99%

Robot navigation and target capturing using nature-inspired approaches in a dynamic environment

Verma

Saxena

Tiwari

2020

2020 10th International Conference on Cloud Computing, Data Science &Amp; Engineering (Confluence)

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“…These works focus on developing intelligent multi-agent networks composed by robots and smart sensors in service applications. AutoRobot [28,29] combines ROS and JADE to enable support to autonomous and rational service robots. It offers a series of reusable packages, templates and tools to help with the integration of new agents (robot or sensor) within their framework.…”

Section: Related Workmentioning

confidence: 99%

A Generic Multi-Layer Architecture Based on ROS-JADE Integration for Autonomous Transport Vehicles

Martin

Casquero

Fortes

et al. 2018

Sensors

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The design and operation of manufacturing systems is evolving to adapt to different challenges. One of the most important is the reconfiguration of the manufacturing process in response to context changes (e.g., faulty equipment or urgent orders, among others). In this sense, the Autonomous Transport Vehicle (ATV) plays a key role in building more flexible and decentralized manufacturing systems. Nowadays, robotic frameworks (RFs) are used for developing robotic systems such as ATVs, but they focus on the control of the robotic system itself. However, social abilities are required for performing intelligent interaction (peer-to-peer negotiation and decision-making) among the different and heterogeneous Cyber Physical Production Systems (such as machines, transport systems and other equipment present in the factory) to achieve manufacturing reconfiguration. This work contributes a generic multi-layer architecture that integrates a RF with a Multi-Agent System (MAS) to provide social abilities to ATVs. This architecture has been implemented on ROS and JADE, the most widespread RF and MAS framework, respectively. We believe this to be the first work that addresses the intelligent interaction of transportation systems for flexible manufacturing environments in a holistic form.

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