Drivocopter: A concept Hybrid Aerial/Ground vehicle for long-endurance mobility

This work aimed to develop an autonomous system for unmanned aerial vehicles (UAVs) to land on moving platforms such as an automobile or a marine vessel, providing a promising solution for a long-endurance flight operation, a large mission coverage range, and a convenient recharging ground station. Unlike most state-of-the-art UAV landing frameworks that rely on UAV onboard computers and sensors, the proposed system fully depends on the computation unit situated on the ground vehicle/marine vessel to serve as a landing guidance system. Such a novel configuration can therefore lighten the burden of the UAV, and the computation power of the ground vehicle/marine vessel can be enhanced. In particular, we exploit a sensor fusion-based algorithm for the guidance system to perform UAV localization, whilst a control method based upon trajectory optimization is integrated. Indoor and outdoor experiments are conducted, and the results show that precise autonomous landing on a 43 cm × 43 cm platform can be performed.

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Proactive Guidance for Accurate UAV Landing on a Dynamic Platform: A Visual-inertial Approach

10.20944/preprints202112.0012.v1 ◽

2021 ◽

Author(s):

Ching-Wei Chang ◽

Li-Yu Lo ◽

Hiu Ching Cheung ◽

Yurong Feng ◽

An-Shik Yang ◽

...

Keyword(s):

Trajectory Optimization ◽

Control Method ◽

Guidance System ◽

Autonomous Landing ◽

Ground Vehicle ◽

Ground Station ◽

Aerial Vehicle ◽

Promising Solution ◽

Marine Vessels ◽

Long Endurance

This work aims to develop an autonomous system for the unmanned aerial vehicle (UAV) to land on a moving platform such as the automobile or marine vessels, providing a promising solution for a long-endurance flight operation, a large mission coverage range, and a convenient recharging ground station. Different from most state-of-the-art UAV landing frameworks which rely on UAV’s onboard computers and sensors, the proposed system fully depends on the computation unit situated on the ground vehicle/marine vessel to serve as a landing guidance system. Such novel configuration can therefore lighten the burden of the UAV and computation power on the ground vehicle/marine vessel could be enhanced. In particular, we exploit a sensor fusion-based algorithm for the guidance system to perform UAV localization, whilst a control method based upon trajectory optimization is integrated. Indoor and outdoor experiments are conducted and the result shows that a precise autonomous landing on a 43 X 43 cm platform could be performed.

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Fast Attitude Estimation System for Unmanned Ground Vehicle Based on Vision/Inertial Fusion

Machines ◽

10.3390/machines9100241 ◽

2021 ◽

Vol 9 (10) ◽

pp. 241

Author(s):

Zhenhui Fan ◽

Pengxiang Yang ◽

Chunbo Mei ◽

Qiju Zhu ◽

Xiao Luo

Keyword(s):

Attitude Estimation ◽

Inertial Fusion ◽

Test Results ◽

Ground Vehicles ◽

Ground Vehicle ◽

The Road ◽

Continuous Vision ◽

Estimation System ◽

Core Idea ◽

Long Endurance

The attitude estimation system based on vision/inertial fusion is of vital importance and great urgency for unmanned ground vehicles (UGVs) in GNSS-challenged/denied environments. This paper aims to develop a fast vision/inertial fusion system to estimate attitude; which can provide attitude estimation for UGVs during long endurance. The core idea in this paper is to integrate the attitude estimated by continuous vision with the inertial pre-integration results based on optimization. Considering that the time-consuming nature of the classical methods comes from the optimization and maintenance of 3D feature points in the back-end optimization thread, the continuous vision section calculates the attitude by image matching without reconstructing the environment. To tackle the cumulative error of the continuous vision and inertial pre-integration, the prior attitude information is introduced for correction, which is measured and labeled by an off-line fusion of multi-sensors. Experiments with the open-source datasets and in road environments have been carried out, and the results show that the average attitude errors are 1.11° and 1.96°, respectively. The road test results demonstrate that the processing time per frame is 24 ms, which shows that the proposed system improves the computational efficiency.

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