What does this research mean for the field?

The Adaptive Cooperative Potential Field (ACPF) algorithm improves UAV formation maintenance, obstacle avoidance, and trajectory efficiency in complex urban environments compared to existing methods like APF, VS-DWA, and CLF-CBF-QP. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Develop and evaluate an Adaptive Cooperative Potential Field (ACPF) algorithm to maintain formation stability and achieve reliable obstacle avoidance for UAVs in urban environments.

August 22, 2026Journal of King Saud University - Computer and Information SciencesOpen Access

An Adaptive Cooperative Potential Field (ACPF) based UAV formation control algorithm for urban environments

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Authors

SWShanxing Wang HWHua Wu XWXiaoxia Wei

Discussion

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Overview

Simulation study demonstrates improved trajectory efficiency and obstacle avoidance in drone formations, indicating robust navigation for complex urban airspace.

Key Points

Develop and evaluate an Adaptive Cooperative Potential Field (ACPF) algorithm to maintain formation stability and achieve reliable obstacle avoidance for UAVs in urban environments.
Designed the ACPF framework integrating neighbor coordination forces, dynamic weight adjustment for attractive and repulsive forces, and a 3D obstacle avoidance strategy with altitude constraints.
Conducted comparative simulations against APF, VS-DWA, and CLF-CBF-QP algorithms across regular pentagon, rhombus, and V-shaped UAV formations.
Reduced total path length by an average of 5.4% and shortened convergence time by 63 s for regular pentagon formations.
Decreased total path length by 7.2% and 7.4%, while shortening convergence time by 53 s and 66 s, for rhombus and V-shaped formations, respectively.

Cite This Study

Wang et al. (2026) studied this question.

synapsesocial.com/papers/6a895eaeca7ade938187ccca https://doi.org/10.1007/s44443-026-01041-6

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August 22, 2026Journal of King Saud University - Computer and Information SciencesOpen Access

An Adaptive Cooperative Potential Field (ACPF) based UAV formation control algorithm for urban environments

View Full Paper

Ask AI

Bookmark

Authors

SWShanxing Wang HWHua Wu XWXiaoxia Wei

Discussion

Member takes

Overview

Simulation study demonstrates improved trajectory efficiency and obstacle avoidance in drone formations, indicating robust navigation for complex urban airspace.

Key Points

Develop and evaluate an Adaptive Cooperative Potential Field (ACPF) algorithm to maintain formation stability and achieve reliable obstacle avoidance for UAVs in urban environments.
Designed the ACPF framework integrating neighbor coordination forces, dynamic weight adjustment for attractive and repulsive forces, and a 3D obstacle avoidance strategy with altitude constraints.
Conducted comparative simulations against APF, VS-DWA, and CLF-CBF-QP algorithms across regular pentagon, rhombus, and V-shaped UAV formations.
Reduced total path length by an average of 5.4% and shortened convergence time by 63 s for regular pentagon formations.
Decreased total path length by 7.2% and 7.4%, while shortening convergence time by 53 s and 66 s, for rhombus and V-shaped formations, respectively.

Cite This Study

Wang et al. (2026) studied this question.

synapsesocial.com/papers/6a895eaeca7ade938187ccca https://doi.org/10.1007/s44443-026-01041-6

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