Towards DJI Phantom 4 Realistic Simulation with Gimbal and RC Controller in ROS/Gazebo Environment

Sagitov, Artur; Gerasimov, Yuriy

doi:10.1109/dese.2017.40

Cited by 14 publications

(6 citation statements)

References 23 publications

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“…In Table 3, we compared our proposed simulator with popular drone simulators such as Gazebo [47], Hector [20,48], RotorS [49] and FlightGoogles [50] across several criteria: visual quality (which is related to the rendering Engine), provided sensors and sensor noise models, interface to configure multiple vehicle models (the number of vehicles), support for virtual reality (VR) headset, and embedded aerodynamic drag force models. It is seen from the table that, compared to other simulators, Airsim stands out as a notable drone simulation tool in all listed criteria.…”

Section: Comparison With Other Drone Simulatorsmentioning

confidence: 99%

High-Fidelity Drone Simulation with Depth Camera Noise and Improved Air Drag Force Models

Kim,

Luong,

et al. 2023

Applied Sciences

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Drone simulations offer a safe environment for collecting data and testing algorithms. However, the depth camera sensor in the simulation provides exact depth values without error, which can result in variations in algorithm behavior, especially in the case of SLAM, when transitioning to real-world environments. The aerodynamic model in the simulation also differs from reality, leading to larger errors in drag force calculations at high speeds. This disparity between simulation and real-world conditions poses challenges when attempting to transfer high-speed drone algorithms developed in the simulated environment to actual operational settings. In this paper, we propose a more realistic simulation by implementing a novel depth camera noise model and an improved aerodynamic drag force model. Through experimental validation, we demonstrate the suitability of our models for simulating real-depth cameras and air drag forces. Our depth camera noise model can replicate the values of a real depth camera sensor with a coefficient of determination (R2) value of 0.62, and our air drag force model improves accuracy by 51% compared to the Airsim simulation air drag force model in outdoor flying experiments at 10 m/s.

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Section: Comparison With Other Drone Simulatorsmentioning

confidence: 99%

High-Fidelity Drone Simulation with Depth Camera Noise and Improved Air Drag Force Models

Kim,

Luong,

et al. 2023

Applied Sciences

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“…The MEMS mirror response time is can be less than 10 ms or even less than 1 ms. The servo motor of the camera stabilizer has a bandwidth width less than 30 Hz because they are bulky and have heavy load [28,37]. This results in a response time higher than 10 ms. Motion compensation in displays and robotics: Motioncompensated MEMS mirror scanner has been applied for projection, [13], where hand-shake is an issue.…”

Section: Related Workmentioning

confidence: 99%

Design of an Adaptive Lightweight LiDAR to Decouple Robot-Camera Geometry

Chen¹,

Wang²,

Thomas³

et al. 2023

Preprint

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A fundamental challenge in robot perception is the coupling of the sensor pose and robot pose. This has led to research in active vision where robot pose is changed to reorient the sensor to areas of interest for perception. Further, egomotion such as jitter, and external effects such as wind and others affect perception requiring additional effort in software such as image stabilization. This effect is particularly pronounced in micro-air vehicles and micro-robots who typically are lighter and subject to larger jitter but do not have the computational capability to perform stabilization in real-time. We present a novel microelectromechanical (MEMS) mirror LiDAR system to change the field of view of the LiDAR independent of the robot motion. Our design has the potential for use on small, low-power systems where the expensive components of the LiDAR can be placed external to the small robot. We show the utility of our approach in simulation and on prototype hardware mounted on a UAV. We believe that this LiDAR and its compact movable scanning design provide mechanisms to decouple robot and sensor geometry allowing us to simplify robot perception. We also demonstrate examples of motion compensation using IMU and external odometry feedback in hardware.

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“…The DJI Phantom has a top speed of 54 km•h -1 and a battery life of about 25 min. The user can simply preprogram the flight height and waypoints, and the UAV will automatically take off, land, document, and respond (Hovhannisyan et al 2018;Sagitov and Gerasimov 2017).…”

Section: Unmanned Aerial Vehicle Models and Drone Simulator Broadly U...mentioning

confidence: 99%

A Literature Survey of Unmanned Aerial Vehicle Usage for Civil Applications

Sivakumar

Tyj

2021

J. Aerosp. Technol. Manag.

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Unmanned vehicles/systems (UVs/USs) technology has exploded in recent years. Unmanned vehicles are operated in the air, on the ground, or on/in the water. Unmanned vehicles play a more significant role in many civil application domains, such as remote sensing, surveillance, precision agriculture and rescue operations rather than manned systems. Unmanned vehicles outperform manned systems in terms of mission safety and operational costs. Unmanned aerial vehicles (UAVs) are widely utilized in the civil infrastructure because of their low maintenance costs, ease of deployment, hovering capability, and excellent mobility. The UAVs can gather photographs faster and more accurately than satellite imagery, allowing for more prompt assessment. This study provides a comprehensive overview of UAV civil applications, including classification and requirements. Also encompassed with research trends, critical civil challenges, and future insights on how UAVs with artificial intelligence (smart AI). Furthermore, this paper discusses the specifications of several drone models and simulators. According to the literature review, precision agriculture is one of the civil applications of smart UAVs. Unmanned aerial vehicles aid in the detection of weeds, crop management, and the identification of plant diseases, among other issues, paving the path for researchers to create drone applications in the future.

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Towards DJI Phantom 4 Realistic Simulation with Gimbal and RC Controller in ROS/Gazebo Environment

Cited by 14 publications

References 23 publications

High-Fidelity Drone Simulation with Depth Camera Noise and Improved Air Drag Force Models

High-Fidelity Drone Simulation with Depth Camera Noise and Improved Air Drag Force Models

Design of an Adaptive Lightweight LiDAR to Decouple Robot-Camera Geometry

A Literature Survey of Unmanned Aerial Vehicle Usage for Civil Applications

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