Rapid A*: a robust path planning scheme for UAVs

Kumar, Pawan; Pal, Kunwar; Govil, Mahesh Chandra; Choudhary, Anita

doi:10.1007/s41315-023-00294-y

Cited by 4 publications

(1 citation statement)

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“…For autonomous decision-making as well as control of UAVs, numerous route planning and navigation algorithms have been recommended. Some studies have reviewed specific algorithms applied in path planning for UAVs such as a deep reinforcement learning (DRL) approach [4][5][6], biologically inspired (bio-inspired) algorithms [7], motion planning algorithms [8], A * [9], Grey wolf [10], etc. In addition, a thorough analysis of UAV formation trajectory planning techniques was presented in the study of [11].…”

Section: Introductionmentioning

confidence: 99%

Hybrid algorithms in path planning for autonomous navigation of unmanned aerial vehicle: a comprehensive review

Tuyet Minh,

Ba Dung

2024

Meas. Sci. Technol.

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Path planning for Unmanned Aerial Vehicle (UAV) is the process of determining the path that travels through each location of interest within a particular area. There are numerous algorithms proposed and described in the publications to address UAV path planning problems. However, in order to handle the complex and dynamic environment with different obstacles, it is critical to utilize the proper fusion algorithms in planning the UAV path. This paper reviews some hybrid algorithms used in finding the optimal route of UAVs that developed in the last ten years as well as their advantages and disadvantages. The UAV path planning methods were classified into categories of hybrid algorithms based on traditional, heuristic, machine learning approaches. Criteria used to evaluate algorithms include execution time, total cost, energy consumption, robustness, data, computation, obstacle avoidance, and environment. The results of this study provide reference resources for researchers in finding the path for UAVs.

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