Efficiently Range-Coupled Position Estimation for an Aerial Swarm With the Confidence Evaluation of Onboard Sensing

This work proposes an efficient range-coupled localization approach for aerial swarms considering the variance of onboard sensors in cluttered environments. First, a reciprocal bond estimation algorithm is proposed to estimate the position; it constructs an optimization framework based on the range-...

Full description

Saved in:
Bibliographic Details
Published in:IEEE/ASME transactions on mechatronics 2023-08, Vol.28 (4), p.2178-2188
Main Authors: Li, Yuzhu, Zhang, Peihan, Mei, Zheyuan, Dong, Wei
Format: Article
Language:English
Subjects:
Adaptive state estimation
aerial swarm
Algorithms
Computational efficiency
Computing time
Efficiency
Estimation
Fuses
gradient-aware Levenberg–Marquardt
Least squares method
Location awareness
Observability
Optimization
range-coupled
Sensors
Swirling
Visualization
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This work proposes an efficient range-coupled localization approach for aerial swarms considering the variance of onboard sensors in cluttered environments. First, a reciprocal bond estimation algorithm is proposed to estimate the position; it constructs an optimization framework based on the range-coupled observability analysis. Subsequently, considering the variable performance characteristics of onboard sensors in cluttered environments, a moving confidence evaluation algorithm is proposed to improve the resilience of the position estimation system by assessing the concurrent reliability of all sensors. Mathematically, the aforementioned two algorithms are integrally formulated using a nonlinear least-squares equation. Notably, solving this nonlinear problem is typically a low-efficiency process that deteriorates with the scale of the swarm. To overcome this issue, a gradient-aware Levenberg-Marquardt algorithm is herein proposed to enhance the computational efficiency of this system to solve this nonlinear least-squares problem. Finally, swirling experiments involving four drones are carried out to verify the performance of the proposed methods. The results demonstrate that the time cost of optimization in each servo period is only approximately 7.15 ms, and the computational efficiency exhibits at least a 2.78 times improvement over the existing methods. Meanwhile, even in a smoky environment, the estimation precision can be as great as 8 cm, which is comparable to results obtained using state-of-the-art methods.
ISSN:1083-4435
1941-014X
DOI:10.1109/TMECH.2022.3232796