Autonomous Exploration Guided by Optimisation Metaheuristic

Santos, Raphael Gomes; Freitas, Edison Pignaton de; Cheng, Sen; Ribeiro, Paulo Rogério de Almeida; Oliveira, Alexandre César Muniz de

doi:10.1109/icarcv.2018.8581136

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…In Santos et al [13], the authors introduce a novel exploration strategy utilizing a continuous multi-modal utility function and metaheuristic optimization. They frame the exploration problem as an optimization task, generating data for a Firefly-based algorithm (FF) tasked with searching solution space cells to construct a map.…”

Section: Related Workmentioning

confidence: 99%

Inverse Firefly-Based Search Algorithms for Multi-Target Search Problem

Zedadra,

Guerrieri,

Seridi

et al. 2024

BDCC

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Efficiently searching for multiple targets in complex environments with limited perception and computational capabilities is challenging for multiple robots, which can coordinate their actions indirectly through their environment. In this context, swarm intelligence has been a source of inspiration for addressing multi-target search problems in the literature. So far, several algorithms have been proposed for solving such a problem, and in this study, we propose two novel multi-target search algorithms inspired by the Firefly algorithm. Unlike the conventional Firefly algorithm, where light is an attractor, light represents a negative effect in our proposed algorithms. Upon discovering targets, robots emit light to repel other robots from that region. This repulsive behavior is intended to achieve several objectives: (1) partitioning the search space among different robots, (2) expanding the search region by avoiding areas already explored, and (3) preventing congestion among robots. The proposed algorithms, named Global Lawnmower Firefly Algorithm (GLFA) and Random Bounce Firefly Algorithm (RBFA), integrate inverse light-based behavior with two random walks: random bounce and global lawnmower. These algorithms were implemented and evaluated using the ArGOS simulator, demonstrating promising performance compared to existing approaches.

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Section: Related Workmentioning

confidence: 99%

Inverse Firefly-Based Search Algorithms for Multi-Target Search Problem

Zedadra,

Guerrieri,

Seridi

et al. 2024

BDCC

View full text Add to dashboard Cite

show abstract

“…Computing and processing this data require computationally intensive algorithms such as Simultaneous Location and Mapping (SLAM), sampling-based planning, adaptive planning under uncertainty and autonomous exploration (Santos et al, 2018). Exploring cloud computing facilities would be much faster than relying on robots' on-board computers, as each robot could request the cloud to allocate tasks, recognize objects and compute the task and motion plan.…”

Section: Example Of Cloud Robotics Usage Scenariosmentioning

confidence: 99%

Ontological concepts for information sharing in cloud robotics

Freitas

Olszewska

Carbonera

et al. 2020

J Ambient Intell Human Comput

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Recent research and developments in Cloud Robotics (CR) require appropriate knowledge representation to ensure interoperable data, information, and knowledge sharing within cloud infrastructures. As an important branch of the Internet of Things (IoT), these demands to advance it forward motivates academic and industrial sectors to invest on it. The IEEE 'Ontologies for Robotics and Automation' Working Group (ORA WG) has been developing standard ontologies for different robotic domains, including industrial and autonomous robots. The use of such robotic standards has the potential to benefit the Cloud Robotic Community (CRC) as well, supporting the provision of ubiquitous intelligent services by the CR-based systems. This paper explores this potential by developing an ontological approach for effective information sharing in cloud robotics scenarios. It presents an extension to the existing ontological standards to cater for the CR domain. The use of the new ontological elements is illustrated through its use in a couple of CR case studies. To the best of our knowledge, this is the first work ever that implements an ontology comprising concepts and axioms applicable to the CR domain.

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Autonomous Exploration Guided by Optimisation Metaheuristic

Cited by 2 publications

References 11 publications

Inverse Firefly-Based Search Algorithms for Multi-Target Search Problem

Inverse Firefly-Based Search Algorithms for Multi-Target Search Problem

Ontological concepts for information sharing in cloud robotics

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