A Decentralized Framework for Multi-Agent Robotic Systems

Jiménez, Andrés C.; García‐Díaz, Vicente; Bolaños, Sandro

doi:10.3390/s18020417

Cited by 41 publications

(20 citation statements)

References 49 publications

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“…Agent-based technology is widely used in artificial intelligence systems because it facilitates the implementation of AI techniques, especially those that allow the system to learn automatically from the environment [ 36 , 37 ]. Virtual organization of agents have evolved from traditional MAS architectures, they allow for the organization of groups according to their tasks within the system [ 38 ].…”

Section: Related Workmentioning

confidence: 99%

GreenVMAS: Virtual Organization Based Platform for Heating Greenhouses Using Waste Energy from Power Plants

González‐Briones

Chamoso

Yoe

et al. 2018

Sensors

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The gradual depletion of energy resources makes it necessary to optimize their use and to reuse them. Although great advances have already been made in optimizing energy generation processes, many of these processes generate energy that inevitably gets wasted. A clear example of this are nuclear, thermal and carbon power plants, which lose a large amount of energy that could otherwise be used for different purposes, such as heating greenhouses. The role of GreenVMAS is to maintain the required temperature level in greenhouses by using the waste energy generated by power plants. It incorporates a case-based reasoning system, virtual organizations and algorithms for data analysis and for efficient interaction with sensors and actuators. The system is context aware and scalable as it incorporates an artificial neural network, this means that it can operate correctly even if the number and characteristics of the greenhouses participating in the case study change. The architecture was evaluated empirically and the results show that the user’s energy bill is greatly reduced with the implemented system.

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Section: Related Workmentioning

confidence: 99%

GreenVMAS: Virtual Organization Based Platform for Heating Greenhouses Using Waste Energy from Power Plants

González‐Briones

Chamoso

Yoe

et al. 2018

Sensors

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“…This resembles the swarm intelligence discipline in similar ways [3,4], as it has been previously noted that multi-agent systems mainly deal with the coordination of multiple interacting agents so as to complete different tasks. The key objective of those systems is to coordinate rather simple agents instead of using a complex agent [5]. The coordination ability of agents gains different skills to these systems that individual agents may not be allowed to achieve, which are, namely, robustness, scalability, and flexibility [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…The key objective of those systems is to coordinate rather simple agents instead of using a complex agent [5]. The coordination ability of agents gains different skills to these systems that individual agents may not be allowed to achieve, which are, namely, robustness, scalability, and flexibility [5,6]. The Robocode platform on the other hand is a game developing platform and allows developers to design robot battle tanks to battle against other tanks [7].…”

Section: Introductionmentioning

confidence: 99%

A Q-Learning-Based Approach for Simple and Multi-Agent Systems

Ulusoy¹,

Güzel²,

Bostancı³

2020

Multi Agent Systems - Strategies and Applications

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This study proposes different machine learning-based solutions to both single and multi-agent systems, took place on a 2-D simulation platform, namely, Robocode. This dynamic and programmable platform allows agents to interact with the environment and each other by employing a variety of battling strategies. Q-Learning is one of the leading and popular machine learning-based solutions to be applied to such a problem. However, especially for continued spaces, the control problem gets deeper. Essentially, one of the main drawbacks of reinforcement learning (RL) is to design an appropriate reward function that the function can be described by only employing few parameters for simple tasks, whereas estimating the goal of the reward function may be a challenging problem. Recent studies prove that neural network-based approaches can handle these challenges and achieve to learn control strategies from 2-D or 1-D data. Besides those problems of RL algorithms for single robots, once the number of robots increases and the systems need to behave as multi-agent systems, the overall design requirements become more complex. Accordingly, the proposed system is validated by considering different battle scenarios. The performance of the Q-Learning-based system and the supervised learning techniques are compared by employing different scenarios for this problem. Results reveal the superiority of the ANN-based approach over other methods.

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“…MAS path planning and coordination can be achieved via centralized or decentralized methods, depending on the underlying communication infrastructure [21]. Centralized methods require persistent communication between a central station and every agent in the network, such that the central station can process and fuse all sensor measurements and use them to plan, or re-plan, the robot paths in a coordinated and collaborative fashion.…”

Section: Introductionmentioning

confidence: 99%

Scalable Gas Sensing, Mapping, and Path Planning via Decentralized Hilbert Maps

Zhu

Ferrari

Morelli

et al. 2019

Sensors

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This paper develops a decentralized approach to gas distribution mapping (GDM) and information-driven path planning for large-scale distributed sensing systems. Gas mapping is performed using a probabilistic representation known as a Hilbert map, which formulates the mapping problem as a multi-class classification task and uses kernel logistic regression to train a discriminative classifier online. A novel Hilbert map information fusion method is presented for rapidly merging the information from individual robot maps using limited data communication. A communication strategy that implements data fusion among many robots is also presented for the decentralized computation of GDMs. New entropy-based information-driven path-planning methods are developed and compared to existing approaches, such as particle swarm optimization (PSO) and random walks (RW). Numerical experiments conducted in simulated indoor and outdoor environments show that the information-driven approaches proposed in this paper far outperform other approaches, and avoid mutual collisions in real time.

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A Decentralized Framework for Multi-Agent Robotic Systems

Cited by 41 publications

References 49 publications

GreenVMAS: Virtual Organization Based Platform for Heating Greenhouses Using Waste Energy from Power Plants

GreenVMAS: Virtual Organization Based Platform for Heating Greenhouses Using Waste Energy from Power Plants

A Q-Learning-Based Approach for Simple and Multi-Agent Systems

Scalable Gas Sensing, Mapping, and Path Planning via Decentralized Hilbert Maps

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