Surveillance task optimized by Evolutionary shared Tabu Inverted Ant Cellular Automata Model for swarm robotics navigation control

Lopes, Hamilton Junior Mendes; Lima, Danielli A.

doi:10.1016/j.rico.2022.100141

Cited by 9 publications

(7 citation statements)

References 49 publications

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“…There are numerous works that leverage cellular automata (CA) technologies for developing robot path planning algorithms [21][22][23]. The paper [21] presents a model, namely Genetic Shared Tabu Inverted Ant Cellular Automata (GSTIACA), for observing a group of robots by combining cellular automation technologies, genetic algorithms, ant algorithms and insights from the social behaviour of the pedestrians. The model initially implements a genetic algorithm, followed by CA technologies applied during specific navigation steps across diverse environments.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Robot Navigation Efficiency Using Cellular Automata with Active Cells

Alzoubi,

Miraz

2024

AETiC

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Enhancing robot navigation efficiency is a crucial objective in modern robotics. Robots relying on external navigation systems are often susceptible to electromagnetic interference (EMI) and encounter environmental disturbances, resulting in orientation errors within their surroundings. Therefore, the study employed an internal navigation system to enhance robot navigation efficacy under interference conditions, based on the analysis of the internal parameters and the external signals. This article presents details of the robot’s autonomous operation, which allows for setting the robot's trajectory using an embedded map. The robot’s navigation process involves counting the number of wheel revolutions as well as adjusting wheel orientation after each straight path section. In this article, an autonomous robot navigation system has been presented that leverages an embedded control navigation map utilising cellular automata with active cells which can effectively navigate in an environment containing various types of obstacles. By analysing the neighbouring cells of the active cell, the cellular environment determines which cell should become active during the robot’s next movement step. This approach ensures the robot’s independence from external control inputs. Furthermore, the accuracy and speed of the robot’s movement have been further enhanced using a hexagonal mosaic for navigation surface mapping. This concept of utilising on cellular automata with active cells has been extended to the navigation of a group of robots on a shared navigation surface, taking into account the intersections of the robots’ trajectories over time. To achieve this, a distance control module has been used that records the travelled trajectories in terms of wheel turns and revolutions.

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Section: Literature Reviewmentioning

confidence: 99%

Enhancing Robot Navigation Efficiency Using Cellular Automata with Active Cells

Alzoubi,

Miraz

2024

AETiC

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“…This concise overview facilitates a comprehensive understanding of the diverse applications of AI in swarm robotics, alongside the testing environments and specific methodologies employed across the studies. -√ Large language model (LLM) [21] -√ RL algorithm [5] √ -Dueling Double Deep Q-Network (D3QN) [6] √ -Deep Learning Trained by Genetic Algorithm (DL-GA) [8] √ -3D StringNet herding [10] √ -Decision-making mechanisms [12] √ -Deep Imitation Reinforcement Learning (DIRL) [17] Augmented Lagrangian particle swarm optimization (ALPSO) [20] √ √ Automatic modular design approach (AutoMoDe) [24] Coordination -√ AudioLocNetv(deep learning module) [31] √ -Not specified [32] √ -End-to-end Neural Networks to train robots [27] √ -Mean-field feedback control [28] √ -Deep Neural Network (DNN) model [29] √ -variant of the crawling probabilistic road map motion planning algorithm [33] √ -distributed online reinforcement learning method [34] √ -coordination algorithm [51] Optimization -√ PSO algorithm [53] -√ streamlined algorithms [36] √ -Genetic algorithm (GA) [46] √ -Particle Swarm Optimization (PSO) [49] √ -Robot Bean Optimization Algorithm (RBOA) [50] √ -Automatic modular design method: AutoMoDe-Cedrata and AutoMoDe-Maple [52] √ -PPO algorithm [54] √ -Dijkstra algorithm [55] √ -WC and WET algorithms [44] √ √ Decentralized ergodic planning [35] Optimization and Navigation √ -YOLOv8 [41] √ -Quantum-based path-planning algorithm and Grover's search algorithm [42] √ -Genetic algorithms (GA) and Cellular automata techniques [9] √ -Mean-Field Control (MFC), deep reinforcement learning (RL), and collision avoidance algorithms [22] Optimization and Coordination √ -Knowledge-Based Neural Ordinary Differential Equations (KNODE) [23] √ -Surrogate models ...…”

Section: Internationalmentioning

confidence: 99%

AI Driven Approaches in Swarm Robotics - A review

Alahmadi,

Barri,

Aldhahri

et al. 2024

IJCI

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The integration of artificial intelligence (AI) and swarm robotics has brought about significant advancements. Swarm robotics is based on decentralized control and self-organization, taking inspiration from natural swarms. It involves employing a large number of uncomplicated robots to collaboratively complete intricate tasks. The algorithms underpinning swarm robotics, which is artificial intelligence, vary depending on the specific role of AI - such as error detection, navigation, coordination, and optimization - and according to the tasks that these robots aim to undertake. In this systematic review, we aim to explore algorithms based on artificial intelligence in swarm robots and the advantages of applying them in the real world. In this systematic review, 74 scientific papers published between the years 2020 to 2024 were examined, but 53 of them were included after applying our methodology to them. The review investigated the common role of AI in swarm robotics, the most commonly used AI algorithms, and the percentage of the research that was conducted and tested in the real world. In conclusion, we discovered that there is a need for research that develops fault detection and coordination strategies, as well as a need for real-world testing.

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“…Cellular automata are widely used in several areas of science to develop the spatial modeling of complex systems that have a large number of local interactions and that can exhibit unpredictable behavior, among them, we can mention forest modeling Lima, 2014; Brasiel andLima, 2023), modeling of diseases (Monteiro et al, 2020), and even robotics control (Lopes and Lima, 2022), which is the focus of our work. Different works have already been proposed with the objective of create swarm robotics control through CA and using bio-inspired strategies, among them, we can mention the work of (Lopes and Lima, 2022) in which CA was used in a 2D form. In this article, we will explore their use in creating controllers, a complex process influenced by several variables, including the number of robots, vegetation type, pheromone and queues.…”

Section: Cellular Automatamentioning

confidence: 99%

Swarm Robotics surveillance control with Ant Cellular Automata Model in the Cerrado Biome for preserving biodiversity

Brasiel,

Lima

2024

JIS

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The Cerrado biome in Brazil plays a vital role in preserving biodiversity, providing essential ecosystem services, and supporting agriculture, making it a crucial and valuable natural resource. Sete Cidades National Park stands out for its rock formations, 10,000-year-old cave paintings and its Cerrado vegetation. The Cerrado is known for being a pyrobiome, so its patrolling becomes essential. In this context, this paper introduces a novel approach to swarm robotics patrolling in the unique ecosystem of the Sete Cidades National Park, located within the Cerrado biome. The study presents three distinct cellular automata models designed for the task, aiming to enhance the efficiency and coverage of patrolling efforts. The key difference between our model presented in this research and the previous one is our focus on a map that encompasses diverse vegetation types, specifically designed to represent the Sete Cidades National Park, with a primary goal of monitoring forest fires. The results demonstrated that the best-performing model was the Forest Tabu Inverted Ant Cellular Automata, which achieved an average of 21.77 complete patrol cycles with 95% confidence. This outcome was obtained using three robots, a tabu queue of |Q| = 80, and a maximum pheromone per cell equals to ρ = 103. These parameters highlight the efficacy of this model in optimizing patrol cycles and the efficient use of resources for environmental surveillance in the Sete Cidades National Park, particularly in the context of fire prevention.

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Surveillance task optimized by Evolutionary shared Tabu Inverted Ant Cellular Automata Model for swarm robotics navigation control

Cited by 9 publications

References 49 publications

Enhancing Robot Navigation Efficiency Using Cellular Automata with Active Cells

Enhancing Robot Navigation Efficiency Using Cellular Automata with Active Cells

AI Driven Approaches in Swarm Robotics - A review

Swarm Robotics surveillance control with Ant Cellular Automata Model in the Cerrado Biome for preserving biodiversity

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