RRT*-APF Path Planning and MA-AADRC-SMC Control for Cooperative 3-D Obstacle Avoidance in Multi-UAV Formations

To enable safe cooperative flight of multi-UAV formations in urban 3-D airspace with wind-field disturbances, we develop an integrated planning-control framework.The planning layer uses an APF-guided RRT* with continuous collision prediction and explicit velocity/acceleration limits, and compensates wind online.The control layer adopts a dual-loop MA-AADRC-SMC design. An adaptive ESO estimates disturbances for feed-forward cancellation, and an SMC term improves robustness and tracking accuracy. By coupling the planned trajectory with speed-weighted repulsive fields, the framework coordinates path and attitude in closed loop, enabling collision-free and overshoot-free formation flight in wind and clutter. Simulations show higher tracking accuracy and better formation stability than ADRC, PID and SMC. A Lyapunov analysis proves uniform boundedness and asymptotic stability. The framework is scalable to applications such as disaster assessment and urban air transport.

Authors

Institutions

Publication Details

Journal
Drones
Published
2025-08-29
DOI
https://doi.org/10.3390/drones9090611
Citations
3
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
3.83

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

RRT*-APF Path Planning and MA-AADRC-SMC Control for Cooperative 3-D Obstacle Avoidance in Multi-UAV Formations

Yuehao Yan, Songlin Liu, Rui Hao
3 citations
Drones
Robotic Path Planning Algorithms
3.83
article

RRT*-APF Path Planning and MA-AADRC-SMC Control for Cooperative 3-D Obstacle Avoidance in Multi-UAV Formations

Yuehao Yan, Songlin Liu, Rui Hao
article en
3 citations

Abstract

To enable safe cooperative flight of multi-UAV formations in urban 3-D airspace with wind-field disturbances, we develop an integrated planning-control framework.The planning layer uses an APF-guided RRT* with continuous collision prediction and explicit velocity/acceleration limits, and compensates wind online.The control layer adopts a dual-loop MA-AADRC-SMC design. An adaptive ESO estimates disturbances for feed-forward cancellation, and an SMC term improves robustness and tracking accuracy. By coupling the planned trajectory with speed-weighted repulsive fields, the framework coordinates path and attitude in closed loop, enabling collision-free and overshoot-free formation flight in wind and clutter. Simulations show higher tracking accuracy and better formation stability than ADRC, PID and SMC. A Lyapunov analysis proves uniform boundedness and asymptotic stability. The framework is scalable to applications such as disaster assessment and urban air transport.

DronesVol. 9(9)
Civil Aviation Flight University of China (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 6%
Robotic Path Planning Algorithms
3.83
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

RRT*-APF Path Planning and MA-AADRC-SMC Control for Cooperative 3-D Obstacle Avoidance in Multi-UAV Formations — Yuehao Yan, Songlin Liu, et al. · Drones (2025) | TGRS Research Map | TGRS