Path Planning of Urban Manned eVTOL Based on the Improved Artificial Electric Field Algorithm

Guang-cai LIU , Song-peng JIN , Zhang-ping LI , Bai-geng LIU

Science Technology and Engineering ›› 2025, Vol. 25 ›› Issue (1) : 238 -244.

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Science Technology and Engineering ›› 2025, Vol. 25 ›› Issue (1) : 238-244 DOI: 10.12404/j.issn.1671-1815.2402679
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Path Planning of Urban Manned eVTOL Based on the Improved Artificial Electric Field Algorithm
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Abstract

The path planning problem of electric vertical takeoff and landing (eVTOL) aircraft in urban scenarios was studied. Firstly, a three-dimensional urban space model was constructed using the hazard grid method. Considering the selected model of eVTOL, with range, operational risk, and altitude variation as objective functions, a path planning model for manned eVTOL with multiple constraints was developed, taking into account the characteristics of the aircraft and environmental limitations. Subsequently, an improved artificial electric field algorithm (IAEFA) was proposed, which enhances the traditional artificial electric field algorithm(AEFA) by introducing an adaptive Coulomb parameter and incorporating a decreasing coefficient in the Coulomb constant calculation for simulation-based solution. Experimental results demonstrate that the constructed model achieves the expected outcomes. The solution effectiveness of path planning using the improved algorithm surpasses that of traditional particle swarm optimization and artificial electric field methods, resulting in shorter range, minimal altitude variation, and enhanced safety during operations. Finally, based on comparative experiments, the value of the decreasing coefficient was determined. The optimal solution effectiveness of the improved algorithm is achieved when the decreasing coefficient is set to 1.5.

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urban air mobility (UAM) / electric vertical takeoff and landing(eVTOL) / path planning / improved artificial electric field algorithm (IAEFA)

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Guang-cai LIU, Song-peng JIN, Zhang-ping LI, Bai-geng LIU. Path Planning of Urban Manned eVTOL Based on the Improved Artificial Electric Field Algorithm. Science Technology and Engineering, 2025, 25(1): 238-244 DOI:10.12404/j.issn.1671-1815.2402679

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