Path planning of UAV for oilfield inspections in a three-dimensional dynamic environment with moving obstacles based on an improved pigeon-inspired optimization algorithm

Ge, Fawei; Li, Kun; Han, Ying; Xu, Wensu; Wang, Yian

doi:10.1007/s10489-020-01650-2

Cited by 35 publications

(16 citation statements)

References 36 publications

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“…They considered the following seven performance metrics: total time, setup time, flight time, battery consumption, inaccuracy of land, haptic control effort, and coverage ratio. Similarly, Ge et al 26 studied path planing for oilfield inspection within a 3D environment. Here, the cost function includes metrics such as: distance, height, time and electricity consumption.…”

Section: State Of the Artmentioning

confidence: 99%

Model-based analysis of multi-UAV path planning for surveying postdisaster building damage

Nagasawa¹,

Mas

Moya

et al. 2021

Sci Rep

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Emergency responders require accurate and comprehensive data to make informed decisions. Moreover, the data should be acquired and analyzed swiftly to ensure an efficient response. One of the tasks at hand post-disaster is damage assessment within the impacted areas. In particular, building damage should be assessed to account for possible casualties, and displaced populations, to estimate long-term shelter capacities, and to assess the damage to services that depend on essential infrastructure (e.g. hospitals, schools, etc.). Remote sensing techniques, including satellite imagery, can be used to gathering such information so that the overall damage can be assessed. However, specific points of interest among the damaged buildings need higher resolution images and detailed information to assess the damage situation. These areas can be further assessed through unmanned aerial vehicles and 3D model reconstruction. This paper presents a multi-UAV coverage path planning method for the 3D reconstruction of postdisaster damaged buildings. The methodology has been implemented in NetLogo3D, a multi-agent model environment, and tested in a virtual built environment in Unity3D. The proposed method generates camera location points surrounding targeted damaged buildings. These camera location points are filtered to avoid collision and then sorted using the K-means or the Fuzzy C-means methods. After clustering camera location points and allocating these to each UAV unit, a route optimization process is conducted as a multiple traveling salesman problem. Final corrections are made to paths to avoid obstacles and give a resulting path for each UAV that balances the flight distance and time. The paper presents the details of the model and methodologies, and an examination of the texture resolution obtained from the proposed method and the conventional overhead flight with the nadir-looking method used in 3D mappings. The algorithm outperforms the conventional method in terms of the quality of the generated 3D model.

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Section: State Of the Artmentioning

confidence: 99%

Model-based analysis of multi-UAV path planning for surveying postdisaster building damage

Nagasawa¹,

Mas

Moya

et al. 2021

Sci Rep

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“…Various optimization algorithms have been utilized in the automobile engineering field (Gao et al, 2016a, 2016b), such as Fruit Fly Optimization Algorithm (Li et al, 2020), bat algorithm (Wang et al, 2020), pigeon-inspired optimization (Ge et al, 2020) and brain storm optimization (Li et al, 2020). Among these, the ALO algorithm is a new bionic intelligent optimization algorithm inspired by the hunting mechanism of antlions.…”

Section: Proposed Procedures Of the Nonlinear Underdetermined Blind Source Separation Solutionmentioning

confidence: 99%

Bearing fault diagnosis based on kernel independent component analysis and antlion optimization

Zhong

Liu

Wang

et al. 2021

Transactions of the Institute of Measurement and Control

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Fault diagnosis of gearboxes based on vibration signal processing is challenging, as vibration signals collected by acceleration sensors are typically a nonlinear mixture of unknown signals. Furthermore, the number of source signals is usually larger than that of sensors because of the practical limitation on sensor positions. Hence, the fault characterization is actually a nonlinear underdetermined blind source separation (NUBSS) problem. In this paper, a novel NUBSS algorithm based on kernel independent component analysis (KICA) and antlion optimization (ALO) is proposed to address the technical challenge. The mathematical model demonstrates the nonlinear mixing of source signals in the underdetermined cases. Ensemble empirical mode decomposition is used as a preprocessing tool to decompose the observed signals into a set of intrinsic mode functions that suffers from the problem of redundant components. The correlation coefficient is utilized to eliminate the redundant components. An adaptive threshold singular value decomposition method is proposed to estimate the number of source signals. Then a whitening process is carried out to transform the overdetermined blind source separation (BSS) into determined BSS, which can be solved by the KICA method. However, the reasonable selection of parameters in KICA limits its application to some extent. Therefore, ALO and Fisher’s linear discriminant analysis are adopted to further enhance the accuracy of the KICA method. The separation performance of the proposed method is assessed through simulation. The numerical results show that the proposed method can accurately estimate the number of source signals and attains a higher separation quality in tackling nonlinear mixed signals when compared with the existing methods. Finally, the inner ring fault experiment is conducted to preliminarily validate the practicability of the proposed method in bearing fault diagnosis.

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“…In the scenario where the UAV is used for 3D oilfield detection, the pigeon inspired optimization (PIO) algorithm is used to optimize the initial path, and the fruit fly optimization algorithm (FOA) is used to perform local optimization to avoid obstacles while finding the best path [62]. In the static rough terrain environment, the path length, height, and adjustment angle need to be considered.…”

Section: Figure 6 Mainstream Path Planning Algorithmsmentioning

confidence: 99%

“…At the same time, the model prediction controller of the follower can predict the leader's movement state, so that the UAV formation remains stable [84]. Faced the problem of time-varying formation control of singular multi-agent systems with switched topologies, [62] designed a distributed formation controller based on output factors, through the impulse-free and equivalent exchange of singular multi-agent systems, An algorithm for solving distributed controller is designed, and the effectiveness of the method is verified by numerical simulation. Zhang J proposed a cooperative guidance control method based on the backstepping method to quickly form the desired formation and achieve a multi-UAV steady state.…”

Section: C) Flight and Formation Control Technologymentioning

confidence: 99%

UAV Swarm Intelligence: Recent Advances and Future Trends

2020

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The dynamic uncertain environment and complex tasks determine that the unmanned aerial vehicle (UAV) system is bound to develop towards clustering, autonomy，and intelligence. In this paper, we present a comprehensive survey of UAV swarm intelligence from the hierarchical framework perspective. Firstly, we review the basics and advances of UAV swarm intelligent technology. Then we look inside to investigate the research work by classifying UAV swarm intelligence research into five layers, i.e., decision-making layer, path planning layer, control layer, communication layer, and application layer. Furthermore, the relationship between each level is explicitly illustrated, and the research trends of each layer are given. Finally, limitations and possible technology trends of swarm intelligence are also covered to enable further research interests. Through this in-depth literature review, we intend to provide novel insights into the latest technologies in UAV swarm intelligence.

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Path planning of UAV for oilfield inspections in a three-dimensional dynamic environment with moving obstacles based on an improved pigeon-inspired optimization algorithm

Cited by 35 publications

References 36 publications

Model-based analysis of multi-UAV path planning for surveying postdisaster building damage

Model-based analysis of multi-UAV path planning for surveying postdisaster building damage

Bearing fault diagnosis based on kernel independent component analysis and antlion optimization

UAV Swarm Intelligence: Recent Advances and Future Trends

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