Search and rescue operations using robotic darwinian particle swarm optimization

Kumar, Arjun; Manikutty, Gayathri; Bhavani, Rao R.; Couceiro, Micael S.

doi:10.1109/icacci.2017.8126112

Cited by 36 publications

(38 citation statements)

References 6 publications

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“…This model was first published by Kennedy and Eberhart [10], and is today a swarm method to solve several problems, as optimization [10], source search [14], [26], [27], Search and Rescue (SaR) [11] and some work was done for OSL application [7], [8].…”

Section: A Original Algorithmmentioning

confidence: 99%

“…Typical applications are Chemical Plume Tracing (CPT) [19], [22], source pollution tracking , as well in an atmospheric [8], [15] or underwater context [2], [22], and rescue missions [11]. However communications are much more constrained underwater than in the air, thus atmospheric methods can not be applied directly to the underwater world.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Local Charged Particle Swarm Optimization to track an underwater mobile source

Coquet¹,

Aubry²,

Arnold³

et al. 2019

OCEANS 2019 - Marseille

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In this paper, a possible solution to track a mobile underwater source in a closed environment with N Autonomous Underwater Vehicles (AUV) in a swarm formation is adressed. The source tracking algorithm is defined as successful when the range between the source and the swarm is sufficiently low during a given duration, short enough to perform a specified action (for example a source localization). A source is defined as an entity that releases a scalar information affected by transport and diffusion in the environment. We use a generic time-varying information f (pi(t)), where pi at time t is the m-dimensional position of a tracker i and function f (.) is a function that represents sensor information. In this paper, we propose an innovative tracking method inspired by the Particle Swarm Optimization (PSO) algorithm that we call the Local Charged Particle Swarm Optimization (LCPSO). The proposed algorithm is adapted to range-dependant communication that characterizes the underwater context and includes flocking parameters. Comparison of the LCPSO against state of the art methods demonstrate the interest of our approach in an underwater scenario.

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Section: A Original Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Local Charged Particle Swarm Optimization to track an underwater mobile source

Coquet¹,

Aubry²,

Arnold³

et al. 2019

OCEANS 2019 - Marseille

View full text Add to dashboard Cite

show abstract

“…It is strongly focused in coordination, cooperation and collaboration. In particular for swarm robotics applied to SAR operations, optimization algorithms for hazardous environments and laborious tasks were proposed and development [10].…”

Section: Brief and Trends Of Sar Roboticsmentioning

confidence: 99%

Semi-Autonomous Robot Control System with an improved 3D Vision Scheme for Search and Rescue Missions. A joint research collaboration between South Africa and Argentina

Kamlofsky¹,

Naidoo²,

Bright³

et al. 2018

Adv. sci. technol. eng. syst. j.

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Rescue operations require technology to assist the rescue process. The robotic technology in these missions is becoming very important. The important aspects investigated in this study are the integration of a mechatronic system that will allow for a robotic platform with a vision system. The research collaboration between Argentina and South Africa is discussed, with the correlating research areas that each country investigated. The study permitted the development and advancement of a search and rescue system for different robots (wayfarer and drones) with different vision capabilities. A novel and innovative vision approach is presented.

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“…This behavior is resilient to external stimuli as long as the agents are within communication range. By using these methods, and despite the remaining challenges to designing the desired group behavior, the robotics community has used swarm-based approaches for applications such as target tracking [13][14][15][16][17], Search And Rescue (SAR) [18], or Odor Source Localization (OSL) [19][20][21][22][23][24][25][26], among others.…”

Section: Introductionmentioning

confidence: 99%

“…The strength of these approaches lies in using agents distributed in the workspace, sharing information to search for the optimum of a fitness function. In particular, this community strongly uses the PSO algorithm [13][14][15][18][19][20][21]28]. It is important to stress that optimization algorithms like PSO, can also drive a swarm of actual robots [29].…”

Section: Introductionmentioning

confidence: 99%

Control of a Robotic Swarm Formation to Track a Dynamic Target With Communication Constraints: Analysis and Simulation

Coquet¹,

Arnold²,

Bouvet³

2021

Preprint

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We describe and analyze the Local Charged Particle Swarm Optimization (LCPSO) algorithm, that we designed to solve the problem of tracking a moving target releasing scalar information in a constrained environment using a swarm of agents. This method is inspired by flocking algorithms and the PSO algorithm for function optimization. Four parameters drive LCPSO: the number of agents; the inertia weight; the attraction/repulsion weight; and the inter-agent distance. Using APF, we provide a mathematical analysis of the LCPSO algorithm under some simplifying assumptions. First, the swarm will aggregate and attain a stable formation, whatever the initial conditions. Second, the swarm moves thanks to an attractor in the swarm, which serves as a guide for the other agents to head for the target. By focusing on a simple application of target tracking with communication constraints, we then remove those assumptions one by one. We show the algorithm is resilient to constraints on the communication range, and the behavior of the target. Results on simulation confirm our theoretical analysis. This provides useful guidelines to understand and control the LCPSO algorithm as a function of swarm characteristics as well as the nature of the target.

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Search and rescue operations using robotic darwinian particle swarm optimization

Cited by 36 publications

References 6 publications

A Local Charged Particle Swarm Optimization to track an underwater mobile source

A Local Charged Particle Swarm Optimization to track an underwater mobile source

Semi-Autonomous Robot Control System with an improved 3D Vision Scheme for Search and Rescue Missions. A joint research collaboration between South Africa and Argentina

Control of a Robotic Swarm Formation to Track a Dynamic Target With Communication Constraints: Analysis and Simulation

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