Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges

Aggarwal, Shubhani; Kumar, Neeraj

doi:10.1016/j.comcom.2019.10.014

Cited by 618 publications

(313 citation statements)

References 90 publications

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“…In any case, when area coverage algorithms consider path planning, it is often from a global point of view, leaving the local planning to the individual agents. A detailed description of path planning algorithms including approaches of linear programming, control theory, multi-objective optimization models, probabilistic models, and meta-heuristic models for different types of UAVs is available in [133]. While some of these algorithms are generic and only take into account the origin and objective position, together with obstacle positions, others also consider the dynamics of the vehicles and constraints that these naturally impose in local curvatures, such as Dubin curves [133].…”

Section: B Area Coverage and Path Planningmentioning

confidence: 99%

“…Multiple works have been devoted to reviewing the different path planning strategies for unmanned vehicles in different mediums: aerial robots [133], surface robots [148], underwater robots [149], [150], and ground robots for urban [55], or wilderness [151] environments. From these works, we have summarized the main constraints to be considered in path planning algorithms in Fig.…”

Section: Planning For Different Robots: Uavs Ugvs Uuvs and Usvsmentioning

confidence: 99%

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Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

et al. 2020

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Search and rescue (SAR) operations can take significant advantage from supporting autonomous or teleoperated robots and multi-robot systems. These can aid in mapping and situational assessment, monitoring and surveillance, establishing communication networks, or searching for victims. This paper provides a review of multi-robot systems supporting SAR operations, with system-level considerations and focusing on the algorithmic perspectives for multi-robot coordination and perception. This is, to the best of our knowledge, the first survey paper to cover (i) heterogeneous SAR robots in different environments, (ii) active perception in multi-robot systems, while (iii) giving two complementary points of view from the multi-agent perception and control perspectives. We also discuss the most significant open research questions: shared autonomy, sim-to-real transferability of existing methods, awareness of victims' conditions, coordination and interoperability in heterogeneous multi-robot systems, and active perception. The different topics in the survey are put in the context of the different challenges and constraints that various types of robots (ground, aerial, surface, or underwater) encounter in different SAR environments (maritime, urban, wilderness, or other post-disaster scenarios). The objective of this survey is to serve as an entry point to the various aspects of multi-robot SAR systems to researchers in both the machine learning and control fields by giving a global overview of the main approaches being taken in the SAR robotics area.

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Section: B Area Coverage and Path Planningmentioning

confidence: 99%

Section: Planning For Different Robots: Uavs Ugvs Uuvs and Usvsmentioning

confidence: 99%

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

et al. 2020

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“…Although, drones having countless applications in various areas, but its ungoverned deployment causes serious security problems [19] and require intelligent transportation techniques [20]. It can be used for transfer of unlawful material like explosives or can violate the boundary of security sensitive organizations.…”

Section: Ref No Year Of Publicationmentioning

confidence: 99%

Amateur Drones Detection: A machine learning approach utilizing the acoustic signals in the presence of strong interference

Uddin

Altaf

Bilal

et al. 2020

Computer Communications

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Owing to small size, sensing capabilities and autonomous nature, the Unmanned Air Vehicles (UAVs) have enormous applications in various areas e.g., remote sensing, navigation, archaeology, journalism, environmental science, and agriculture. However, the un-monitored deployment of UAVs called the amateur drones (AmDr) can lead to serious security threats and risk to human life and infrastructure. Therefore, timely detection of the AmDr is essential for the protection and security of sensitive organizations, human life and other vital infrastructure. Am-Drs can be detected using different techniques based on sound, video, thermal, and radio frequencies. However, the performance of these techniques is limited in * sever atmospheric conditions. In this paper, we propose an efficient un-supervise machine learning approach of independent component analysis (ICA) to detect various acoustic signals i.e., sounds of bird, airplanes, thunderstorm, rain, wind and the UAVs in practical scenario. After unmixing the signals, the features like Mel Frequency Cepstral Coefficients (MFCC), the power spectral density (PSD) and the Root Mean Square Value (RMS) of the PSD are extracted by using ICA. The PSD and the RMS of PSD signals are extracted by first passing the signals from octave band filter banks. Based on the above features the signals are classified using Support Vector Machines (SVM)and K Nearest Neighbor (KNN)to detect the presence or absence of AmDr. Unique feature of the proposed technique is the detection of a single or multiple AmDrs at a time in the presence of multiple acoustic interfering signals. The proposed technique is verified through extensive simulations and it is observed that the RMS values of PSD with KNN performs better than the MFCC with KNN and SVM.

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“…Path planning of UAV is an important preliminary step in UAV flight mission which can be fulfilled by finding the optimum solution for an optimization problem [1]. Path planning is one of the most important problems to be explored in UAV for finding an optimal path between source and destination [2] , which also has attracted more and more attentions of researchers and developed rapidly due to its wide applications [3].…”

Section: Introductionmentioning

confidence: 99%

Beetle Colony Optimization Algorithm and its Application

Zhang

Jiang

et al. 2020

IEEE Access

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Massive data sets and complex scheduling processes have high-dimensional and non-convex features bringing challenges on various applications. With deep insight into the bio-heuristic opinion, we propose a novel Beetle Colony Optimization (BCO) being able to adapt NP-hard issues to meet growing application demands. Two important mechanisms are introduced into the proposed BCO algorithm. The first one is Beetle Antennae Search (BAS), which is a mechanism of random search along the gradient direction but not use gradient information at all. The second one is swarm intelligence, which is a collective mechanism of decentralized and self-organized agents. Both of them have reached a performance balance to elevate the proposed algorithm to maintain a wide search horizon and high search efficiency. Finally, our algorithm is applied to traveling salesman problem, and quadratic assignment problem and possesses excellent performance, which also shows that the algorithm has good applicability from the side. The effectiveness of the algorithm is also substantiated by comparing the results with the original ant colony optimization (ACO) algorithm in 3D simulation model experimental path planning.

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Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges

Cited by 618 publications

References 90 publications

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

Amateur Drones Detection: A machine learning approach utilizing the acoustic signals in the presence of strong interference

Beetle Colony Optimization Algorithm and its Application

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