A Swarm-Intelligence Approach to Oil Spill Mapping using Unmanned Aerial Vehicles

Ball, Zachary; Odonkor, Philip; Chowdhury, Souma

doi:10.2514/6.2017-1157

Cited by 6 publications

(5 citation statements)

References 21 publications

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“…The bid value c i consists of two parts. First, we calculate ζ i of module m i according to Equation (3). Then, we assume that the UAV is in module m i , then calculate the ζ of module m 1 , m 2 , and m 4 corresponding to module m i and record the maximum value as ζ m .…”

Section: Two-step Auctionmentioning

confidence: 99%

“…Unmanned aerial vehicles (UAV) have the characteristics of low operating cost, good maneuverability, and no risk of casualties [1,2]. Accordingly, it has been applied to many fields, such as surveying and mapping [3], surveillance [4], bathymetry [5][6][7][8][9], and disaster rescue [10,11]. As a typical application, area coverage with UAVs has attracted great attention in robotics.…”

Section: Introductionmentioning

confidence: 99%

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Multi-UAV Coverage through Two-Step Auction in Dynamic Environments

Sun

Tan

Yan

et al. 2022

Drones

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The cooperation of multiple unmanned aerial vehicles (Multi-UAV) can effectively solve the area coverage problem. However, developing an online multi-UAV coverage approach remains a challenge due to energy constraints and environmental dynamics. In this paper, we design a comprehensive framework for area coverage with multiple energy-limited UAVs in dynamic environments, which we call MCTA (Multi-UAV Coverage through Two-step Auction). Specifically, the online two-step auction mechanism is proposed to select the optimal action. Then, an obstacle avoidance mechanism is designed by defining several heuristic rules. After that, considering energy constraints, we develop the reverse auction mechanism to balance workload between multiple UAVs. Comprehensive experiments demonstrate that MCTA can achieve a high coverage rate while ensuring a low repeated coverage rate and average step deviation in most circumstances.

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Section: Two-step Auctionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-UAV Coverage through Two-Step Auction in Dynamic Environments

Sun

Tan

Yan

et al. 2022

Drones

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show abstract

“…Figure 5 provides a representative illustration of a DC maneuver, showing both the planned and the controller executed trajectory of the UAVs. Both strategies are designed in a way that the maneuvers do not delay (or advance) entire operation after these maneuvers are finished which is vital for sensitive missions like 19 where entire operation schedule does not have flexibility.…”

Section: Collision Avoidance Strategiesmentioning

confidence: 99%

Training Detection-Range-Frugal Cooperative Collision Avoidance Models for Quadcopters via Neuroevolution

Behjat

Gabani

Chowdhury

2019

AIAA Aviation 2019 Forum

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Cooperative autonomous approaches to avoiding collisions among small Unmanned Aerial Vehicles (UAVs) is central to safe integration of UAVs within the civilian airspace. One potential online cooperative approach is the concept of reciprocal actions, where both UAVs take pre-trained mutually coherent actions that do not require active online coordination (thereby avoiding the computational burden and risk associated with it). This paper presents a learning based approach to train such reciprocal maneuvers. Neuroevolution, which uses evolutionary algorithms to simultaneously optimize the topology and weights of neural networks, is used as the learning method -which operates over a set of sample approach scenarios. Unlike most existing work (that minimize travel distance, energy or risk), the training process here focuses on the objective of minimizing the required detection range; this has important practical implications w.r.t. alleviating the dependency on sophisticated sensing and their reliability under various environments. A specialized design of experiments and line search is used to identify the minimum detection range for each sample scenarios. In order to allow an efficient training process, a classifier is used to discard actions (without simulating them) where the controller would fail. The model obtained via neuroevolution is observed to generalize well to (i.e., successful collision avoidance over) unseen approach scenarios.

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“…In general terms, they are more user friendly than all the geoinformatics and remote sensing technologies, relatively cheaper, smaller, lighter and practical, and also offer an alternative to conventional platforms for high-resolution remote sensing data acquisition, with a lower cost and higher operational flexibility (Klemas 2015, Bayirhan & Gazioglu 2020). These RPAS vehicles can be applied in a diverse range of areas, that includes precision agriculture (Zhang & Kovacs 2012, Freeman & Freeland 2015, surveillance (Hodgson et al 2016) (Cunliffe et al 2016, Ball et al 2017, Bayirhan & Gazioglu 2020. The necessity for a precise, flexible and low-cost system that offers a higher spatial resolution, temporal frequency and global accessibility, make the RPAS a great choice that satisfies these needs (e.g., Yang et al 2013, Odonkor et al 2019.…”

Section: Introductionmentioning

confidence: 99%

Assessing Remotely Piloted Aerial Systems in the characterization of rocky shores for oil spills environmental sensitivity mapping, northern São Paulo littoral, Brazil

Cerri

Rodrigues

Oliveira

et al. 2022

An. Acad. Bras. Ciênc.

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The Environmental Sensitivity Index (ESI) for oil spills was developed to assist coordinators to evaluate oil spill impact along shorelines and also to coordinate the allocation of resources during and after the incident, aiming to reduce environmental damage and consequences. Recently, Remotely Piloted Aerial Systems (RPAS) are being used in a wide range of areas, since they complement traditional remote sensing data (e.g., satellite images) and offer a rapidly, precise, detail and high-resolution images that fit well for environmental studies. Herein, the use of high-resolution RPAS images for ESI analysis of rocky shores in the Brazilian territory was performed. Using RPAS images, with their higher-resolution compared with ArcGIS Pro Basemap satellite images, increased the detailed of ESI analysis for oil spills, increasing the number of regions in the rocky shore that are more sensitive to oil spills. The RPAS images were able to decrease the number of areas that were less sensitive to oil spills, and increase areas that are more sensitive to oil spills. This increase is important, since they were not detected in the ESI analysis using conventional ArcGIS Pro Basemap satellite images. The RPAS images permit to delineate precisely rocky shores, improving ESI interpretation in rocky shores.

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A Swarm-Intelligence Approach to Oil Spill Mapping using Unmanned Aerial Vehicles

Cited by 6 publications

References 21 publications

Multi-UAV Coverage through Two-Step Auction in Dynamic Environments

Multi-UAV Coverage through Two-Step Auction in Dynamic Environments

Training Detection-Range-Frugal Cooperative Collision Avoidance Models for Quadcopters via Neuroevolution

Assessing Remotely Piloted Aerial Systems in the characterization of rocky shores for oil spills environmental sensitivity mapping, northern São Paulo littoral, Brazil

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