Path Planning of Low Altitude UAV for Urban Combat

Addressing the challenge of unmanned aerial vehicle (UAV) path planning in urban combat, we establish a three-dimensional urban space model using the grid mapping method and “0-1” assignment approach. We simplify this complex three-dimensional environment into a two-dimensional representation through the application of the tangent projection technique. An objective function encompassing altitude, turning angle, pitch angle, and flight distance is introduced, providing a comprehensive evaluation framework. Based on this model, an enhanced A-Star algorithm is presented to augment planning efficacy. This algorithm incorporates measures to enhance the safe distance around obstacles, dynamically adjusts the heuristic function's convergence mode, and smooths the planned path to align with the UAV's flight capabilities. Simulation results demonstrate that the improved A-Star algorithm achieves a superior balance between safety and minimal path length, while significantly enhancing the rapidity and smoothness of the planning process.

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Publication Details

Published
2024-10-18
DOI
https://doi.org/10.1109/icus61736.2024.10839966
Primary Topic
Robotic Path Planning Algorithms
Type
article
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article

Path Planning of Low Altitude UAV for Urban Combat

An Liu, Xingyu He, Shuyi Wang, Jiacheng Ma et al.
Robotic Path Planning Algorithms
article

Path Planning of Low Altitude UAV for Urban Combat

An Liu, Xingyu He, Shuyi Wang, Jiacheng Ma, Junhua Hu, Wang Jin-cai
article en

Abstract

Addressing the challenge of unmanned aerial vehicle (UAV) path planning in urban combat, we establish a three-dimensional urban space model using the grid mapping method and “0-1” assignment approach. We simplify this complex three-dimensional environment into a two-dimensional representation through the application of the tangent projection technique. An objective function encompassing altitude, turning angle, pitch angle, and flight distance is introduced, providing a comprehensive evaluation framework. Based on this model, an enhanced A-Star algorithm is presented to augment planning efficacy. This algorithm incorporates measures to enhance the safe distance around obstacles, dynamically adjusts the heuristic function's convergence mode, and smooths the planned path to align with the UAV's flight capabilities. Simulation results demonstrate that the improved A-Star algorithm achieves a superior balance between safety and minimal path length, while significantly enhancing the rapidity and smoothness of the planning process.

Weinan Normal University (CN), Air Force Engineering University (CN)
Sustainable cities and communities
Openalex Percentile: Top 74%
Robotic Path Planning Algorithms
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Path Planning of Low Altitude UAV for Urban Combat — An Liu, Xingyu He, et al. · (2024) | TGRS Research Map | TGRS