Aspects of unmanned aerial vehicles path planning: Overview and applications

Khan, Muhammad Toaha Raza; Saad, Malik Muhammad; Ru, Yang; Seo, Jung Hun; Kim, Dongkyun

doi:10.1002/dac.4827

Cited by 31 publications

(16 citation statements)

References 41 publications

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“…Predicting when a collision might happen closely relates to path planning schemes, and research has been carried out in this field [ 30 , 31 , 32 , 33 , 34 ]. However, unlike path planning approaches, collision avoidance usually refers to the ability of the vehicle to recognize dangers and act simultaneously.…”

Section: Related Workmentioning

confidence: 99%

Collision Detection and Avoidance for Underwater Vehicles Using Omnidirectional Vision

Ochoa

Gracias

Istenič

et al. 2022

Sensors

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Exploration of marine habitats is one of the key pillars of underwater science, which often involves collecting images at close range. As acquiring imagery close to the seabed involves multiple hazards, the safety of underwater vehicles, such as remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs), is often compromised. Common applications for obstacle avoidance in underwater environments are often conducted with acoustic sensors, which cannot be used reliably at very short distances, thus requiring a high level of attention from the operator to avoid damaging the robot. Therefore, developing capabilities such as advanced assisted mapping, spatial awareness and safety, and user immersion in confined environments is an important research area for human-operated underwater robotics. In this paper, we present a novel approach that provides an ROV with capabilities for navigation in complex environments. By leveraging the ability of omnidirectional multi-camera systems to provide a comprehensive view of the environment, we create a 360° real-time point cloud of nearby objects or structures within a visual SLAM framework. We also develop a strategy to assess the risk of obstacles in the vicinity. We show that the system can use the risk information to generate warnings that the robot can use to perform evasive maneuvers when approaching dangerous obstacles in real-world scenarios. This system is a first step towards a comprehensive pilot assistance system that will enable inexperienced pilots to operate vehicles in complex and cluttered environments.

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Section: Related Workmentioning

confidence: 99%

Collision Detection and Avoidance for Underwater Vehicles Using Omnidirectional Vision

Ochoa

Gracias

Istenič

et al. 2022

Sensors

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“…The recent survey on various UAV path planning and collision avoidance algorithms can be seen in (Basiri et al 2022 ; Khan et al 2021a , b ). In order to solve the high dimensional problems with good accuracy and avoid sudden changes in its path for UAV applications, the aforementioned algorithm may not be an efficient choice.…”

Section: State-of-the Artmentioning

confidence: 99%

“…An efficient path planning strategy is much needed to avoid the obstacles and find the shortest and the safest path to achieve the desired mission. Many pioneering works dealt with a 2D environment (Khan et al 2021a , b ) and very few works focused on path planning in a 3D environment by avoiding the obstacles (Vashisth et al 2021 ; Zhang et al 2021 ). In the event of low-altitude flight of UAVs, many static obstacles obstruct the UAV and an efficient path planning algorithm is very much needed.…”

Section: Introductionmentioning

confidence: 99%

Optimal energy efficient path planning of UAV using hybrid MACO-MEA* algorithm: theoretical and experimental approach

Esakki

Elangovan

Tamilarasan

et al. 2022

J Ambient Intell Human Comput

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Autonomous mission capabilities with optimal path are stringent requirements for Unmanned Aerial Vehicle (UAV) navigation in diverse applications. The proposed research framework is to identify an energy-efficient optimal path to achieve the designated missions for the navigation of UAVs in various constrained and denser obstacle prone regions. Hence, the present work is aimed to develop an optimal energy-efficient path planning algorithm through combining well known modified ant colony optimization algorithm (MACO) and a variant of A*, namely the memory-efficient A* algorithm (MEA*) for avoiding the obstacles in three dimensional (3D) environment and arrive at an optimal path with minimal energy consumption. The novelty of the proposed method relies on integrating the above two efficient algorithms to optimize the UAV path planning task. The basic design of this study is, that by utilizing an improved version of the pheromone strategy in MACO, the local trap and premature convergence are minimized, and also an optimal path is found by means of reward and penalty mechanism. The sole notion of integrating the MEA* algorithm arises from the fact that it is essential to overcome the stringent memory requirement of conventional A* algorithm and to resolve the issue of tracking only the edges of the grids. Combining the competencies of MACO and MEA*, a hybrid algorithm is proposed to avoid obstacles and find an efficient path. Simulation studies are performed by varying the number of obstacles in a 3D domain. The real-time flight trials are conducted experimentally using a UAV by implementing the attained optimal path. A comparison of the total energy consumption of UAV with theoretical analysis is accomplished. The significant finding of this study is that, the MACO-MEA* algorithm achieved 21% less energy consumption and 55% shorter execution time than the MACO-A*. moreover, the path traversed in both simulation and experimental methods is 99% coherent with each other. it confirms that the developed hybrid MACO-MEA* energy-efficient algorithm is a viable solution for UAV navigation in 3D obstacles prone regions.

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“…It is set that V g = 0.2865 m/s in a 32 × 70 map. The path has the HAUV start at the grid (2,31) and travel to the grid (66,11). In the 21 × 47 map, V g = 0.3921 m/s.…”

Section: Comparison Of Traditional A* Algorithm and Improved A* Algor...mentioning

confidence: 99%

“…Path planning algorithms are now widely used in the civilian and military fields. The civil applications of path planning algorithms include robotic transportation and distribution [1], the automatic driving of civilian UAVs [2,3], intelligent robotic obstacle avoidance planning [4], etc. In addition, the path planning method has also been used in military-related projects, including military UAV formation planning, missile trajectory planning [5], underwater vehicle trajectory tracking [6], etc.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Time-Optimal Path Planning of a Hybrid Autonomous Underwater Vehicle Based on Ocean Current Neural Point Grid

Hua

Yuan

et al. 2022

JMSE

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Path planning is the precondition for Hybrid Autonomous Underwater Vehicles (HAUV) to enter the submerged area to undertake a mission. The influence of ocean currents on HAUV should be further investigated to obtain a time-optimal path. The improved A* algorithm and the neural network model are employed in this paper to plan a time-optimal path for the vehicle. The HAUV in glider mode is capable of traveling forward mainly through the zigzag motion in vertical plane. Since the vehicle can only receive the command orders when it surfaces from the water, the path is expected to include a series of discrete waypoints in the water surface. At the same time, the presence of submerged riverbeds is also taken into account to avoid hazards for HAUVs when it navigates in the water. It can be demonstrated that ocean currents can be used to decrease the operating time. The comparison results of the two methods verify that the size of the map affects the calculation time. In addition, the neural node represented method surpasses the modified A* method, especially when the map is too large.

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Aspects of unmanned aerial vehicles path planning: Overview and applications

Cited by 31 publications

References 41 publications

Collision Detection and Avoidance for Underwater Vehicles Using Omnidirectional Vision

Collision Detection and Avoidance for Underwater Vehicles Using Omnidirectional Vision

Optimal energy efficient path planning of UAV using hybrid MACO-MEA* algorithm: theoretical and experimental approach

Time-Optimal Path Planning of a Hybrid Autonomous Underwater Vehicle Based on Ocean Current Neural Point Grid

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