Auto altitude holding of quadrotor UAVs with Kalman filter based vertical velocity estimation

Liu, H.; Liu, M.; Wei, Xiaolin; Song, Quanjun; Ge, Yaojun; Wang, F. L.

doi:10.1109/wcica.2014.7053520

Cited by 7 publications

(5 citation statements)

References 24 publications

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“…Commercial off the shelf UAVs such as the 3DR IRIS+ that we used for building our autonomous system employs a barometer to determine height. We observed in flight tests that the height measurement is unreliable, fluctuates over time, and often depends on weather conditions, as also observed in Liu et al () and Szafranski, Czyba, Janusz, and Blotnicki (). Thus, we leave it for future work to address the problem of accurately estimating the altitude of a UAV.…”

Section: Discussionsupporting

confidence: 82%

“…Thus, we leave it for future work to address the problem of accurately estimating the altitude of a UAV. Two approaches that can be considered include (a) filtering the barometer sensor data using, for example, a Kalman filter (Liu et al, ), and (b) the use of a LiDAR sensor or a radar‐based sensor for more accurate height above ground estimations (Schartel et al, ). Alternatively, employing the existing implementation on all topographical conditions requires a UAV capability to maintain a fixed relative altitude above ground.…”

Section: Discussionmentioning

confidence: 99%

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TrackerBots: Autonomous unmanned aerial vehicle for real‐time localization and tracking of multiple radio‐tagged animals

Nguyen

Chesser

Koh

et al. 2019

Journal of Field Robotics

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Autonomous aerial robots provide new possibilities to study the habitats and behaviors of endangered species through the efficient gathering of location information at temporal and spatial granularities not possible with traditional manual survey methods. We present a novel autonomous aerial vehicle system-TrackerBotsto track and localize multiple radio-tagged animals. The simplicity of measuring the received signal strength indicator (RSSI) values of very high frequency (VHF) radiocollars commonly used in the field is exploited to realize a low-cost and lightweight tracking platform suitable for integration with unmanned aerial vehicles (UAVs). Due to uncertainty and the nonlinearity of the system based on RSSI measurements, our tracking and planning approaches integrate a particle filter for tracking and localizing and a partially observable Markov decision process for dynamic path planning. This approach allows autonomous navigation of a UAV in a direction of maximum information gain to locate multiple mobile animals and reduce exploration time and, consequently, conserve on-board battery power. We also employ the concept of search termination criteria to maximize the number of located animals within power constraints of the aerial system. We validated our real-time and online approach through both extensive simulations and field experiments with five VHF radio-tags on a grassland plain.

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Section: Discussionsupporting

confidence: 82%

Section: Discussionmentioning

confidence: 99%

TrackerBots: Autonomous unmanned aerial vehicle for real‐time localization and tracking of multiple radio‐tagged animals

Nguyen

Chesser

Koh

et al. 2019

Journal of Field Robotics

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“…Since both vertical and level velocities are not directly available, and to deduce them from differencing position signal and integrating acceleration signal are both unfeasible resulting in large noise and drift, Kanlman filter provides a good solution which fuses the acceleration and altitude signals for velocity estimation. Applying the control law (8) to the third component of (5) the altitude holding has been presented in [16].…”

Section: Implementation Issuesmentioning

confidence: 99%

“…The autopilot is implemented on a 16 MHz Mega2560 processor board using C/C++. Also included are an on-board data logger, an xBee wireless communication unit and a GUI based ground station [16].…”

Section: A Our Platformmentioning

confidence: 99%

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Autonomous position holding and point to point flying control of quadrotor UAVs

Liu

Wei

2014

Proceeding of the 11th World Congress on Intelligent Control and Automation

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Position holding, also called hovering, is a key specification of rotor based aircraft. Its control problem for small quadrotor UAVs (unmanned aerial vehicles) is addressed in this paper. Given the flight dynamics of quadrotors, the P+D+DD based position holding control laws are presented. For the resultant two loop overall configuration, the outer loop controls the throttle magnitude required to reach the target position and generates the reference attitude command for the inner loop, while the inner attitude loop ensures that aircraft's thrust is orientated to the target. The control algorithm is validated via a serial of field tests on a platform developed, including position holding and point to point navigation, showing the effectiveness of the approaches. Some implementation issues, such as translation velocity estimation, its Kalman filter design and multiple sensor data fusion are also addressed.

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