A bio-inspired distributed approach for searching underwater acoustic source using a team of AUVs

Shaukat, Mansoor; Ong, Sim Heng

doi:10.1109/oceans-bergen.2013.6607954

Cited by 11 publications

(5 citation statements)

References 48 publications

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“…This is also true for the single-sensor based temporal sampling implementations which either require centralized (Ogren, Fiorelli, & Leonard, 2004) or decentralized explicit inter-agent communication (Bachmayer & Leonard, 2002; Shaukat et al, 2013). Strategies that use implicit communication for social behaviors are inspired from ants’ pheromone sensing and hence use stigmergy (Deveza, Thiel, Russell, & Mackay-Sim, 1994; Panait & Luke, 2004; Russell, 1999; Sugawara et al, 2004).…”

Section: Related Workmentioning

confidence: 97%

“…As far as the cooperative multi-agent source localization approaches are concerned, they are either scaled versions of the individualistic behavior (Li, Meng, Bai, Li, & Popescu, 2008) or require explicit inter-agent communication for social behaviors (Marjovi, Nunes, Sousa, Faria, & Marques, 2010) to achieve cooperation. This is also true for the single-sensor based temporal sampling implementations which either require centralized (Ogren, Fiorelli, & Leonard, 2004) or decentralized explicit inter-agent communication (Bachmayer & Leonard, 2002; Shaukat et al, 2013). Strategies that use implicit communication for social behaviors are inspired from ants’ pheromone sensing and hence use stigmergy (Deveza, Thiel, Russell, & Mackay-Sim, 1994; Panait & Luke, 2004; Russell, 1999; Sugawara et al, 2004).…”

Section: Related Workmentioning

confidence: 97%

“…The problem of source localization can be solved cooperatively by having a mix of individualistic and social behaviors (Ioannou, Singh, & Couzin, 2015; Matarić, 1995; Shaukat, Chitre, & Ong, 2013). An individualistic behavior may be self-sufficient in localizing the source, i.e.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive behaviors in multi-agent source localization using passive sensing

Shaukat¹

2016

Adaptive Behavior

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In this paper, the role of adaptive group cohesion in a cooperative multi-agent source localization problem is investigated. A distributed source localization algorithm is presented for a homogeneous team of simple agents. An agent uses a single sensor to sense the gradient and two sensors to sense its neighbors. The algorithm is a set of individualistic and social behaviors where the individualistic behavior is as simple as an agent keeping its previous heading and is not self-sufficient in localizing the source. Source localization is achieved as an emergent property through agent’s adaptive interactions with the neighbors and the environment. Given a single agent is incapable of localizing the source, maintaining team connectivity at all times is crucial. Two simple temporal sampling behaviors, intensity-based-adaptation and connectivity-based-adaptation, ensure an efficient localization strategy with minimal agent breakaways. The agent behaviors are simultaneously optimized using a two phase evolutionary optimization process. The optimized behaviors are estimated with analytical models and the resulting collective behavior is validated against the agent’s sensor and actuator noise, strong multi-path interference due to environment variability, initialization distance sensitivity and loss of source signal.

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Section: Related Workmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 97%

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Adaptive behaviors in multi-agent source localization using passive sensing

Shaukat¹

2016

Adaptive Behavior

Self Cite

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“…For instance, we consider the case where the agents are a swarm of Autonomous Underwater Vehicles (AUV). Sensors could be pressure sensors, including acoustic sensors [1], [3], [16], [18]; magnetic sensors [5]; detectors of various chemicals [12]. The applications include securing a specified area to detect intruders; to prevent illegal oil spilling by tankers; localizing a pipeline leak [2], [22], or various other applications yet to be imagined.…”

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

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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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“…O VER years, Autonomous Underwater Vehicles (AUVs) have been increasingly developed for underwater searching [1], exploring [2], reconnaissance [3], mining [4], etc. To achieve these tasks, it is necessary for algorithms to keep track of the AUVs' positions, which is known as localization.…”

Section: Introductionmentioning

confidence: 99%

Improving Localization Accuracy for an Underwater Robot With a Slow-Sampling Sonar Through Graph Optimization

Chen

Wang

et al. 2015

IEEE Sensors J.

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Abstract-This paper proposes a novel localization algorithm for an autonomous underwater vehicle equipped with a mechanical scanning sonar that has a slow frequency of data sampling. The proposed approach incrementally constructs a pose graph and conducts graph optimization to correct the robot poses. The construction of a pose graph has three stages: 1) scan generation which incorporates an extended Kalman filter-based dead reckoning algorithm that takes the robot motion into account while eliminating the sonar scan distortion caused by the motion; 2) data association which is based on Mahanalobis distance and shape matching for determining loop closures; and 3) scan matching which calculates constraints constructs pose graph. The constructed pose graph is then fed into a graph optimizer to find the optimal poses corresponding to each scan. A trajectory correction module uses these optimized poses to correct intermediate poses during the process of scan generation. Both simulation and practical experiments are conducted to verify the viability and accuracy of the proposed algorithm.

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A bio-inspired distributed approach for searching underwater acoustic source using a team of AUVs

Cited by 11 publications

References 48 publications

Adaptive behaviors in multi-agent source localization using passive sensing

Adaptive behaviors in multi-agent source localization using passive sensing

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

Improving Localization Accuracy for an Underwater Robot With a Slow-Sampling Sonar Through Graph Optimization

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