Heterogeneous Swarms for Maritime Dynamic Target Search and Tracking

Kwa, Hian Lee; Tokić, Grgur; Bouffanais, Roland; Yue, Dick K. P.

doi:10.1109/ieeeconf38699.2020.9389145

Cited by 16 publications

(18 citation statements)

References 28 publications

(33 reference statements)

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“…To measure the exploration and exploitation dynamics of an MRS tracking a moving target Kwa et al (2020a) and Kwa et al (2020b) proposed a metric based on an agent's direction of travel and its bearing to the target. This allowed for an individual agent's contribution, and hence the system's overall exploration and exploitation dynamic to be quantified.…”

Section: Sardinha Et Al (2020a)mentioning

confidence: 99%

“…To track a fast moving target Kwa et al (2020a), Kwa et al (2020b), Kwa et al (2021a) and Kwa et al (2021b), initially used inter-agent repulsion to promote area exploration. When a target was detected, an agent and its neighbors would activate a PSObased tracking behavior, allowing the target's information to be exploited, facilitating target tracking.…”

Section: Dedicated Exploration and Exploitation Strategiesmentioning

confidence: 99%

“…A similar repulsion scheme was adapted by Coquet et al (2019) for use in their dynamic target tracking swarm. Similarly, to prevent excessive agent clustering while carrying out a target tracking task Kwa et al (2020a), Kwa et al (2020b), Kwa et al (2021a), Kwa et al (2021b) employed an adaptive repulsion algorithm that gradually increased the strength of inter-agent repulsion while agents were exploring the area. This repulsion strength was also gradually decreased while agents were attempting to move towards a target.…”

Section: Attraction-repulsion Dynamicsmentioning

confidence: 99%

“…By combining and adjusting the strength of separate inter-agent attraction and repulsion fields Coquet et al (2021), has demonstrated that a stable equilibrium position can be attained where agents maintain a fixed relative position to each other even though the entire swarm may be in motion. This allowed for the overall surface of an MRS, and hence the Okumura et al (2018) Varying time interval at which robots regroup to trade map information Area Exploration Schumer and Steiglitz (1968), Azad and Hasançebi (2014), Hansen and Ostermeier (2001), Hansen (2006) Adaptive step size Optimization Blackwell and Bentley (2002) Exponential inter-agent repulsion strength Optimization Kernbach et al (2009), Schmickl et al (2009), Bodi et al (2012), Hereford (2013), Bodi et al (2015), Kengyel et al (2016) 2019), Coquet et al (2021), Kwa et al (2020a), Kwa et al (2020b), Kwa et al (2021a), Kwa et al (2021b) Exponential inter-agent repulsion and attraction strength Target Tracking Parker and Emmons (1997), Parker (2002), Kolling and Carpin (2006), Kolling and Carpin (2007) Linear inter-agent repulsion strength & variable target attraction strength Target Tracking (CMOMMT) overall EED of the swarm, to be controlled even though the entire system may be moving.…”

Section: Attraction-repulsion Dynamicsmentioning

confidence: 99%

“…Through these repeated local information exchanges, an agent is able to form local decisions based on the information gathered by itself and its immediate neighbors-still in the network sense. For instance, using the PSO algorithm, changing the number of neighbors an agent communicates with is one of the key methods for controlling the level of exploration and exploitation carried out by the system (Janson and Middendorf, 2005;De Oca et al, 2009;Liu et al, 2016;Blackwell and Kennedy, 2018;Kwa et al, 2020a;Kwa et al, 2020b;Kwa et al, 2021a;Kwa et al, 2021b). By increasing k, information about the global "best" solution can be propagated more swiftly around the system, thereby encouraging exploitation.…”

Section: Number Of Neighborsmentioning

confidence: 99%

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Balancing Collective Exploration and Exploitation in Multi-Agent and Multi-Robot Systems: A Review

2022

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Multi-agent systems and multi-robot systems have been recognized as unique solutions to complex dynamic tasks distributed in space. Their effectiveness in accomplishing these tasks rests upon the design of cooperative control strategies, which is acknowledged to be challenging and nontrivial. In particular, the effectiveness of these strategies has been shown to be related to the so-called exploration–exploitation dilemma: i.e., the existence of a distinct balance between exploitative actions and exploratory ones while the system is operating. Recent results point to the need for a dynamic exploration–exploitation balance to unlock high levels of flexibility, adaptivity, and swarm intelligence. This important point is especially apparent when dealing with fast-changing environments. Problems involving dynamic environments have been dealt with by different scientific communities using theory, simulations, as well as large-scale experiments. Such results spread across a range of disciplines can hinder one’s ability to understand and manage the intricacies of the exploration–exploitation challenge. In this review, we summarize and categorize the methods used to control the level of exploration and exploitation carried out by an multi-agent systems. Lastly, we discuss the critical need for suitable metrics and benchmark problems to quantitatively assess and compare the levels of exploration and exploitation, as well as the overall performance of a system with a given cooperative control algorithm.

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Section: Sardinha Et Al (2020a)mentioning

confidence: 99%

Section: Dedicated Exploration and Exploitation Strategiesmentioning

confidence: 99%