Development of Obstacle Avoidance Controller for MAVs: Testing in Hardware-In-Loop Simulation∗∗This work was sponsored by NP-MICAV Project No: DARO/081102072/M/I/CN-04.

Gade, Murali Mohan; Hangal, Swaroop; Krishnan, Dileep; Arya, Hemendra

doi:10.1016/j.ifacol.2016.03.089

Cited by 6 publications

(2 citation statements)

References 3 publications

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“…A Pixhawk flight controller was used for the system. The Pixhawk flight controller is capable of acquiring data from various sensors and controlling different devices such as servo actuators [37]. Servos are commonly used as camera shutters or fixed winged model aircraft controls [38].…”

Section: Fabricated Water Capturing Mechanismmentioning

confidence: 99%

Evaluation of a UAV-Assisted Autonomous Water Sampling

Koparan

Koc

Privette

et al. 2018

Water

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Water quality assessment programs for the management of water resources require the collection of water samples for physical, chemical, and biological analyses. Lack of personnel, accessibility of water bodies, and time constraints, especially after natural disasters and emergencies, are some of the challenges of water sampling. To overcome these challenges, a custom-made thief-style water sampling mechanism was developed and mounted on a multirotor unmanned aerial vehicle (UAV) for autonomous water sampling. The payload capacity and endurance of the UAV were determined using an indoor test station. The UAV was equipped with floatation, and electronic components were coated to prevent water damage in the event of a forced landing or for sample collection. Water samples from a 1.1 ha pond were collected with the developed UAV-assisted water sampling system and traditional manual methods. Dissolved oxygen (DO), electrical conductivity (EC), pH, temperature and chloride measurements were made on samples collected with both UAV-assisted and manual methods and compared. Percent differences between the two sampling methods for DO, EC, pH, and temperature were minimal except for chloride level. Percent differences between the two sampling methods for DO, EC, pH, and temperature measurements were 3.6%, 2.3%, 0.76%, and 0.03%, respectively. Measured chloride levels for the manual and UAV-assisted sampling methods were 3.97 and 5.46 mg/L. UAV-assisted water sampling may prove faster and safer than manual water sampling from large surface waters and from difficult to access water bodies.

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Section: Fabricated Water Capturing Mechanismmentioning

confidence: 99%

Evaluation of a UAV-Assisted Autonomous Water Sampling

Koparan

Koc

Privette

et al. 2018

Water

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“…An example of this type of research is the article [2]. The authors propose a HiL simulation for the verification of an obstacle avoidance controller intended for Miniature Aerial Vehicles (MAVs).…”

Section: Related Workmentioning

confidence: 99%

Hardware-in-the-loop simulation of a UAV autonomous landing algorithm implemented in SoC FPGA

Szolc¹,

Kryjak²

2022

Preprint

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This paper presents a system for hardware-inthe-loop (HiL) simulation of unmanned aerial vehicle (UAV) control algorithms implemented on a heterogeneous SoC FPGA computing platforms. The AirSim simulator running on a PC and an Arty Z7 development board with a Zynq SoC chip from AMD Xilinx were used. Communication was carried out via a serial USB link. An application for autonomous landing on a specially marked landing strip was selected as a case study. A landing site detection algorithm was implemented on the Zynq SoC platform. This allowed processing a 1280 × 720 @ 60 fps video stream in real time. Performed tests showed that the system works correctly and there are no delays that could negatively affect the stability of the control. The proposed concept is characterised by relative simplicity and low implementation cost. At the same time, it can be applied to test various types of high-level perception and control algorithms for UAV implemented on embedded platforms. We provide the code developed on GitHub, which includes both Python scripts running on the PC and C code running on Arty Z7.

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