Collision Detection and Recovery Control of Drones Using Onboard Inertial Measurement Unit

This paper presents a strategy for collision detection and recovery control of drones using an onboard Inertial Measurement Unit (IMU). The collision detection algorithm compares the expected response of the drone with the measurements from the IMU to identify and characterize collisions. The recovery controller implements a gain scheduling approach, adjusting its parameters based on the characteristics of the collision and the drone’s attitude. Simulations were conducted to compare the proposed collision detection strategy with a popular detection method with fixed thresholds, and the simulation results showed that the proposed approach outperformed the existing method in terms of detection accuracy. Furthermore, the proposed collision detection and recovery control approaches were tested with physical experiments using a custom-built drone. The experimental results confirmed that the proposed collision detection algorithm was able to distinguish between actual collisions and aggressive flight maneuvers, and the recovery controller can recover the drone within 0.8 s.

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Publication Details

Journal
Drones
Published
2025-05-18
DOI
https://doi.org/10.3390/drones9050380
Citations
1
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
1.28

Funders

Controls
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article

Collision Detection and Recovery Control of Drones Using Onboard Inertial Measurement Unit

Guangjun Liu, X S Huang, Yugang Liu
1 citations
Drones
Robotic Path Planning Algorithms
1.28
article

Collision Detection and Recovery Control of Drones Using Onboard Inertial Measurement Unit

Guangjun Liu, X S Huang, Yugang Liu
article en
1 citations

Abstract

This paper presents a strategy for collision detection and recovery control of drones using an onboard Inertial Measurement Unit (IMU). The collision detection algorithm compares the expected response of the drone with the measurements from the IMU to identify and characterize collisions. The recovery controller implements a gain scheduling approach, adjusting its parameters based on the characteristics of the collision and the drone’s attitude. Simulations were conducted to compare the proposed collision detection strategy with a popular detection method with fixed thresholds, and the simulation results showed that the proposed approach outperformed the existing method in terms of detection accuracy. Furthermore, the proposed collision detection and recovery control approaches were tested with physical experiments using a custom-built drone. The experimental results confirmed that the proposed collision detection algorithm was able to distinguish between actual collisions and aggressive flight maneuvers, and the recovery controller can recover the drone within 0.8 s.

DronesVol. 9(5)
Royal Military College of Canada (CA), Toronto Metropolitan University (CA)
Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 20%
Robotic Path Planning Algorithms
1.28
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