Autonomous ballistic airdrop of objects from a small fixed-wing unmanned aerial vehicle

Mathisen, Siri Gulaker; Leira, Frederik Stendahl; Helgesen, Håkon Hagen; Gryte, Kristoffer; Johansen, Tor Arne

doi:10.1007/s10514-020-09902-3

Cited by 16 publications

(9 citation statements)

References 30 publications

(36 reference statements)

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“…As the initial trajectory T T from equation (11) plans stop-tostop motion, we need to ensure the required velocity at the launch point. Moving backwards along the initial trajectory, we can replace a portion of the initial trajectory with a spline trajectory that satisfies the launch velocity.…”

Section: ) Launch Trajectorymentioning

confidence: 99%

“…Researchers in [10] are carefully calculating the approach for the ballistic airdrop, while considering the wind speed. Later on, they extended their approach to an autonomous ballistic airdrop relying on the wind field and air resistance models in [11]. The work in [12] revolves around the precise ballistic airdrop based on the Global Positioning System (GPS).…”

Section: Introductionmentioning

confidence: 99%

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Parabolic Airdrop Trajectory Planning for Multirotor Unmanned Aerial Vehicles

Ivanović

Orsag

2022

IEEE Access

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In this paper we present a motion planning method for the parabolic airdrop using multirotor UAVs. The first step is to determine a set of parabolic trajectories that can reach a desired target. Next, we plan a path to the payload launch point, and based on that path we plan a collision-free trajectory that safely executes both launch and stopping motions. The described motion planning method is extensively tested in the simulation environment: by executing many trajectories through repeatability analysis; planning in a large city-like environment; and planning in a dense office environment. Furthermore, the method is tested through real-world experiments in indoor and outdoor environments. In both environments, we performed the repeatability analysis and the obstacle avoidance experiments while delivering the payload to the target. The video demonstrating our simulation and experimental analyses is available at: https://youtu.be/HJCH2vJEuuo INDEX TERMS Unmanned aerial vehicles, motion planning, unmanned autonomous systems.

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Section: ) Launch Trajectorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parabolic Airdrop Trajectory Planning for Multirotor Unmanned Aerial Vehicles

Ivanović

Orsag

2022

IEEE Access

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“…The error of the controlled UAV must be taken into account for stable flying performance. As a result, in a UAV control system, a closed-loop controller is recommended [16]. The most common closed-loop control system is a (proportional, integral, and derivative) controller [17].…”

Section: Autopilot Controller Designmentioning

confidence: 99%

Smart aerosonde UAV longitudinal flight control system based on genetic algorithm

2021

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Synthesis of a flight control system for such an aircraft that achieves stable and acceptable performance across a specified flying envelope in the presence of uncertainties represents an attractive and challenging design problem. This study uses the genetic self-tuning PID algorithm to develop an intelligent flight control system for the aerosonde UAV model. To improve the system's transient responses, the gains of the PID controller are improved using a genetic algorithm (GA). Simulink/MATLAB software is used to model and simulate the proposed system. The proposed PID controller integrated with the GA is compared with the classical one. Three simulation scenarios are carried out. In the first scenario, and at normal conditions, the proposed controller performance is better than the classical one. While in the second scenario, identical results are achieved from both controllers. Finally, in the third scenario, the PID controller with GA shows the robustness and durability of the system compared with the classical PID in presence of external wind disturbance. The simulation results prove the system parameters optimization.

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“…Since the thermal camera's frame rate is 7.5 frames/s (0.13 s between each image frame), running the object detection, recognition, and tracking module onboard/online is expected to yield identical results as the findings presented below. For demonstrations of real‐time and online use of variations of the presented framework the reader is referred to Leira et al (2017) and Mathisen, Leira, Helgesen, Gryte, and Johansen (2020).…”

Section: Uav and Field Testmentioning

confidence: 99%

Object detection, recognition, and tracking from UAVs using a thermal camera

Leira

Helgesen

Johansen

et al. 2020

Journal of Field Robotics

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In this paper a multiple object detection, recognition, and tracking system for unmanned aerial vehicles (UAVs) has been studied. The system can be implemented on any UAVs platform, with the main requirement being that the UAV has a suitable onboard computational unit and a camera. It is intended to be used in a maritime object tracking system framework for UAVs, which enables a UAV to perform multiobject tracking and situational awareness of the sea surface, in real time, during a UAV operation. Using machine vision to automatically detect objects in the camera's image stream combined with the UAV's navigation data, the onboard computer is able to georeference each object detection to measure the location of the detected objects in a local North‐East (NE) coordinate frame. A tracking algorithm which uses a Kalman filter and a constant velocity motion model utilizes an object's position measurements, automatically found using the object detection algorithm, to track and estimate an object's position and velocity. Furthermore, a global‐nearest‐neighbor algorithm is applied for data association. This is achieved using a measure of distance that is based not only on the physical distance between an object's estimated position and the measured position, but also how similar the objects appear in the camera image. Four field tests were conducted at sea to verify the object detection and tracking system. One of the flight tests was a two‐object tracking scenario, which is also used in three scenarios with an additional two simulated objects. The tracking results demonstrate the effectiveness of using visual recognition for data association to avoid interchanging the two estimated object trajectories. Furthermore, real‐time computations performed on the gathered data show that the system is able to automatically detect and track the position and velocity of a boat. Given that the system had at least 100 georeferenced measurements of the boat's position, the position was estimated and tracked with an accuracy of 5–15 m from 400 m altitude while the boat was in the camera's field of view (FOV). The estimated speed and course would also converge to the object's true trajectories (measured by Global Positioning System, GPS) for the tested scenarios. This enables the system to track boats while they are outside the FOV of the camera for extended periods of time, with tracking results showing a drift in the boat's position estimate down to 1–5 m/min outside of the FOV of the camera.

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Autonomous ballistic airdrop of objects from a small fixed-wing unmanned aerial vehicle

Cited by 16 publications

References 30 publications

Parabolic Airdrop Trajectory Planning for Multirotor Unmanned Aerial Vehicles

Parabolic Airdrop Trajectory Planning for Multirotor Unmanned Aerial Vehicles

Smart aerosonde UAV longitudinal flight control system based on genetic algorithm

Object detection, recognition, and tracking from UAVs using a thermal camera

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