Optimal Trajectory Planning for Autonomous Drone Cinematography

Sabetghadam, Bahareh; Alcántara, Alfonso; Capitán, Jesús; Cunha, Rita; Ollero, A.; Pascoal, António

doi:10.1109/ecmr.2019.8870950

Cited by 20 publications

(13 citation statements)

References 15 publications

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“…real-time aerial videography in dynamic scenes: Recently, several works address the generation of quadrotor camera trajectories in real-time to record dynamic scenes. Rousseau et al, 2018 andSabetghadam et al, 2019 propose Model Predictive Control (MPC) approaches to follow a target and Poiesi and Cavallaro, 2015 introduced a particle swarm method that captures a moving target with multiple quadrotors. However, these works do not optimize a target's image space position.…”

Section: Related Workmentioning

confidence: 99%

Optimal Control to Support High-Level User Goals in Human-Computer Interaction

Gebhardt

Hilliges

2021

Human–Computer Interaction Series

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Section: Related Workmentioning

confidence: 99%

Optimal Control to Support High-Level User Goals in Human-Computer Interaction

Gebhardt

Hilliges

2021

Human–Computer Interaction Series

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“…Moreover, field tests have been performed using this architecture with UAL integrated. In the study by Sabetghadam et al, 22 for instance, some results for autonomous aerial cinematography through UAL are demonstrated.…”

Section: Multiple Drones For Media Productionmentioning

confidence: 99%

Unmanned aerial vehicle abstraction layer: An abstraction layer to operate unmanned aerial vehicles

Real

Torres-González

Soria

et al. 2020

International Journal of Advanced Robotic Systems

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This article presents a software layer to abstract users of unmanned aerial vehicles from the specific hardware of the platform and the autopilot interfaces. The main objective of our unmanned aerial vehicle abstraction layer (UAL) is to simplify the development and testing of higher-level algorithms in aerial robotics by trying to standardize and simplify the interfaces with the unmanned aerial vehicles. Unmanned aerial vehicle abstraction layer supports operation with PX4 and DJI autopilots (among others), which are current leading manufacturers. Besides, unmanned aerial vehicle abstraction layer can work seamlessly with simulated or real platforms and it provides calls to issue standard commands such as taking off, landing or pose, and velocity controls. Even though unmanned aerial vehicle abstraction layer is under continuous development, a stable version is available for public use. We showcase the use of unmanned aerial vehicle abstraction layer with a set of applications coming from several European research projects, where different academic and industrial entities have adopted unmanned aerial vehicle abstraction layer as a common development framework.

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“…Finally, it is important to remark that our architecture allows us to use alternative solutions for the Shot Executor, as long as they address drone and gimbal control and implement the RT and ST concepts that we defined. Indeed, we also developed and tested within our architecture another algorithm for shot execution [41]. The algorithm plans optimal trajectories for the drone as it takes the shot, considering aesthetic aspects (e.g., generating smooth trajectories) and collision constraints.…”

Section: B Shot Executormentioning

confidence: 99%

Autonomous Execution of Cinematographic Shots with Multiple Drones

Alcántara,

Capitán,

Torres-González

et al. 2020

Preprint

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This paper presents a system for the execution of autonomous cinematography missions with a team of drones. The system allows media directors to design missions involving different types of shots with one or multiple cameras, running sequentially or concurrently. We introduce the complete architecture, which includes components for mission design, planning and execution. Then, we focus on the components related to autonomous mission execution. First, we propose a novel parametric description for shots, considering different types of camera motion and tracked targets; and we use it to implement a set of canonical shots. Second, for multi-drone shot execution, we propose distributed schedulers that activate different shot controllers on board the drones. Moreover, an event-based mechanism is used to synchronize shot execution among the drones and to account for inaccuracies during shot planning. Finally, we showcase the system with field experiments filming sport activities, including a real regatta event. We report on system integration and lessons learnt during our experimental campaigns.INDEX TERMS Autonomous cinematography, Multi-robot system, Unmanned aerial vehicles.

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Optimal Trajectory Planning for Autonomous Drone Cinematography

Cited by 20 publications

References 15 publications

Optimal Control to Support High-Level User Goals in Human-Computer Interaction

Optimal Control to Support High-Level User Goals in Human-Computer Interaction

Unmanned aerial vehicle abstraction layer: An abstraction layer to operate unmanned aerial vehicles

Autonomous Execution of Cinematographic Shots with Multiple Drones

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