Path Planning Algorithms for Unmanned Aerial Vehicles

Dhulkefl, Elaf Jirjees; Durdu, Akif

doi:10.31142/ijtsrd23696

Cited by 18 publications

(14 citation statements)

References 9 publications

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“…(2016)), D star (D, Raheem et al. (2018)) and Dijkstra (Dhulkefl et al., 2019). These techniques uses a heuristic function that runs over all nodes (i.e.…”

Section: Path-planning Algorithmsmentioning

confidence: 99%

“…Such methods support the representation of an environment in memory (see Figure 3), which results in path planning and robot localisation (Yang et al, 2014;Liu et al, 2016). There are several grid-based techniques used for path planning, including A star (A ★ , Guruji et al ( 2016)), D star (D ★ , Raheem et al ( 2018)) and Dĳkstra (Dhulkefl et al, 2019). These techniques uses a heuristic function 𝑓 (𝑛) that runs over all nodes 𝑛 (i.e.…”

Section: Grid-based Techniquesmentioning

confidence: 99%

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A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture

et al. 2022

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Multi-rotor Unmanned Aerial Vehicles (UAVs), although originally designed and developed for defence and military purposes, in the last ten years have gained momentum, especially for civilian applications, such as search and rescue, surveying and mapping, and agricultural crops and monitoring. Thanks to their hovering and Vertical Take-Off and Landing (VTOL) capabilities and the capacity to carry out tasks with complete autonomy, they are now a standard platform for both research and industrial uses. However, while the flight control architecture is well established in the literature, there are still many challenges in designing autonomous guidance and navigation systems to make the UAV able to work in constrained and cluttered environments or also indoors. Therefore, the main motivation of this work is to provide a comprehensive and exhaustive literature review on the numerous methods and approaches to address path-planning problems for multi-rotor UAVs. In particular, the inclusion of a review of the related research in the context of Precision Agriculture (PA) provides a unified and accessible presentation for researchers who are initiating their endeavours in this subject.

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“…(2016)), D star (D, Raheem et al. (2018)) and Dijkstra (Dhulkefl et al., 2019). These techniques uses a heuristic function that runs over all nodes (i.e.…”

Section: Path-planning Algorithmsmentioning

confidence: 99%

Section: Grid-based Techniquesmentioning

confidence: 99%

A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture

et al. 2022

View full text Add to dashboard Cite

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“…With those challenges, there is a need to optimize the drone flight path planning based on the locations of the sensors to minimize flight time and overcome battery limitations [101]. To optimize path planning capability, algorithms such as the traveling salesman problem, A Star (A*) algorithm [226], Dijkstra algorithm [226], and modified and improved Dijkstra algorithm [227,228] could be utilized. Optimizing the drone's flight capability would reduce cost, faster execution of missions, and increase navigation time, so there is a need to improve existing path planning algorithms to optimize the drone's navigation time.…”

Section: Challenges and Future Trendsmentioning

confidence: 99%

A Review of Unmanned System Technologies with Its Application to Aquaculture Farm Monitoring and Management

Ubina

Cheng

2022

Drones

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This paper aims to provide an overview of the capabilities of unmanned systems to monitor and manage aquaculture farms that support precision aquaculture using the Internet of Things. The locations of aquaculture farms are diverse, which is a big challenge on accessibility. For offshore fish cages, there is a difficulty and risk in the continuous monitoring considering the presence of waves, water currents, and other underwater environmental factors. Aquaculture farm management and surveillance operations require collecting data on water quality, water pollutants, water temperature, fish behavior, and current/wave velocity, which requires tremendous labor cost, and effort. Unmanned vehicle technologies provide greater efficiency and accuracy to execute these functions. They are even capable of cage detection and illegal fishing surveillance when equipped with sensors and other technologies. Additionally, to provide a more large-scale scope, this document explores the capacity of unmanned vehicles as a communication gateway to facilitate offshore cages equipped with robust, low-cost sensors capable of underwater and in-air wireless connectivity. The capabilities of existing commercial systems, the Internet of Things, and artificial intelligence combined with drones are also presented to provide a precise aquaculture framework.

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“…The authors compared the developed algorithm with another GWO algorithm, showing its statistical superiority. In [24] the chosen algorithms have been the A* and Dijkstra algorithms. Real time tests have been performed on a UAV and the two path length algorithms show the same path length.…”

Section: Multi-fusion Based Algorithmsmentioning

confidence: 99%

Fixed-Wing UAV Energy Efficient 3D Path Planning in Cluttered Environments

Aiello

Valavanis

Rizzo

2022

J Intell Robot Syst

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UAV path planning in 3D cluttered and uncertain environments centers on finding an optimal / sub-optimal collision-free path, considering in parallel geometric, physical and temporal constraints, fox example, obstacles, infrastructure, physical or artificial landmarks, etc. This paper introduces a novel node-based algorithm, called Energy Efficient A* (EEA*), which is based on the A* search algorithm, but overcomes some of its key limitations. The EEA* deals with 3D environments, it is robust converging fast to the solution, it is energy efficient and it is real-time implementable and executable. In addition to the EEA*, a local path planner is also derived to cope with unknown dynamic threats within the working environment. The EEA* and the local path planner are first implemented and evaluated via simulated experiments using a fixed-wing UAV operating in mountain-like 3D environments, and in the presence of unknown dynamic obstacles. This is followed by evaluating a set up where three UAVs are commanded to follow their respective paths in a safe way. The energy efficiency of EEA* is also tested and compared with the conventional A* algorithm.

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Path Planning Algorithms for Unmanned Aerial Vehicles

Cited by 18 publications

References 9 publications

A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture

A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture

A Review of Unmanned System Technologies with Its Application to Aquaculture Farm Monitoring and Management

Fixed-Wing UAV Energy Efficient 3D Path Planning in Cluttered Environments

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