UAV path planning based on improved rapidly-exploring random tree

Sun, Quan; Li, Meng; Tianhe, Wang; Zhao, Chen-Peng

doi:10.1109/ccdc.2018.8408258

Cited by 23 publications

(9 citation statements)

References 1 publication

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“…In other terms, path planning/obstacle avoidance considers a feasible trajectory for the UAV to follow between start and end points, ensuring a smooth path while detecting and avoiding obstacles in the airspace. For the mentioned purpose, on the one hand, there exist passive and active sample-based algorithms, such as rapidly exploring random graphs [132], Probabilistic Road Maps (PRMs) [133], Rapidly exploring Random Trees (RRTs) [134], and Dynamic Domain RRT [135]. In addition, obstacles can be momentarily avoided (i.e., collision-free path) in collaboration with the RRT algorithm [136].…”

Section: Path Planning/obstacle Avoidancementioning

confidence: 99%

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

Osmani,

Schulz

2024

Sensors

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The evolving technologies regarding Unmanned Aerial Vehicles (UAVs) have led to their extended applicability in diverse domains, including surveillance, commerce, military, and smart electric grid monitoring. Modern UAV avionics enable precise aircraft operations through autonomous navigation, obstacle identification, and collision prevention. The structures of avionics are generally complex, and thorough hierarchies and intricate connections exist in between. For a comprehensive understanding of a UAV design, this paper aims to assess and critically review the purpose-classified electronics hardware inside UAVs, each with the corresponding performance metrics thoroughly analyzed. This review includes an exploration of different algorithms used for data processing, flight control, surveillance, navigation, protection, and communication. Consequently, this paper enriches the knowledge base of UAVs, offering an informative background on various UAV design processes, particularly those related to electric smart grid applications. As a future work recommendation, an actual relevant project is openly discussed.

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Section: Path Planning/obstacle Avoidancementioning

confidence: 99%

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

Osmani,

Schulz

2024

Sensors

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“…A comparison of A* and Dijkstra's was performed by [8] in an environment with static obstacles, where the performance of the A* algorithm was overall good in terms of obtaining optimal paths with respect to trajectory acquisition, path length and total travelling time. The Rapidly exploring Random Trees (RRT), known for producing optimal solutions in high-dimensional spaces, have been used with different variations in [13,16] for randomly building a space-filling tree in the navigation space and finding the best path each time. Evolutionary algorithms, such as the Particle Swarm Optimization (PSO) are generally recognized for solving path planning problems [7].…”

Section: Related Workmentioning

confidence: 99%

Path planning of autonomous UAVs using reinforcement learning

Chronis

Anagnostopoulos

Politi

et al. 2023

J. Phys.: Conf. Ser.

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Autonomous BVLOS Unmanned Aerial Vehicles (UAVs) are gradually gaining their share in the drone market. Together with the demand for extended levels of autonomy comes the necessity for high-performance obstacle avoidance and navigation algorithms that will allow autonomous drones to operate with minimum or no human intervention. Traditional AI algorithms have been extensively used in the literature for finding the shortest path in 2-D or 3-D environments and navigating the drones successfully through a known and stable environment. However, the situation can become much more complicated when the environment is changing or not known in advance. In this work, we explore the use of advanced artificial intelligence techniques, such as reinforcement learning, to successfully navigate a drone within unspecified environments. We compare our approach against traditional AI algoriths in a set of validation experiments on a simulation environment, and the results show that using only a couple of low-cost distance sensors it is possible to successfully navigate the drone beyond the obstacles.

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“…In nonconvex high‐dimensional areas, new developments have been done for the RRT algorithm. Many algorithms are proposed for the improvement like dynamic value, bidirectional RRT, and dynamic length 54,55 . Efficiency and high rate success can be achieved by the improved version of RRT.…”

Section: Routing Techniquesmentioning

confidence: 99%

Routing and collision avoidance techniques for unmanned aerial vehicles: Analysis, optimal solutions, and future directions

Sharma

Obaidat

Sharma

et al. 2020

Int J Communication

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SummaryRouting remains the most important problem to be explored, for discovering an ideal pathway in unmanned aerial vehicles (UAVs). Several problems for identifying and targeting location continue giving the immense motility of UAVs. In routing planning, optimum decisions have to be taken for several vital operations for solving the issues in UAVs. Many types of routing techniques of UAVs, which have been used for many years, have been analyzed in this paper. Routing techniques aim to provide striking‐free surroundings to the UAVs and not only to find an optimum path but also the smallest path. Routing techniques play a vital part in the development of a secured way in a minimal time to reach the ultimate destination and hence much needed. There are various routing techniques, real‐time applications of UAVs which are elaborated in this paper, namely, representative, cooperative, and noncooperative techniques. In UAV, collision avoidance is very important for the obstacle‐free environment. Collision avoidance techniques are discussed for a safe environment of UAV. Further, the UAVs network communication coverage and connectivity by these techniques are discussed. Finally, we discuss the security and connectivity and future directions in UAV.

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UAV path planning based on improved rapidly-exploring random tree

Cited by 23 publications

References 1 publication

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

Path planning of autonomous UAVs using reinforcement learning

Routing and collision avoidance techniques for unmanned aerial vehicles: Analysis, optimal solutions, and future directions

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