Real-time motion planning for aerial videography with dynamic obstacle avoidance and viewpoint optimization

Journal Article (2017)
Author(s)

Tobias Nägeli (ETH Zürich)

Javier Alonso-Mora (TU Delft - Learning & Autonomous Control, Massachusetts Institute of Technology)

Alexander Domahidi (Embotech GmbH)

Daniela Rus (Massachusetts Institute of Technology)

Otmar Hilliges (ETH Zürich)

Research Group
Learning & Autonomous Control
DOI related publication
https://doi.org/10.1109/LRA.2017.2665693
More Info
expand_more
Publication Year
2017
Language
English
Research Group
Learning & Autonomous Control
Issue number
3
Volume number
2
Pages (from-to)
1696-1703

Abstract

We propose a method for real-time trajectory generation with applications in aerial videography. Taking framing objectives, such as position of targets in the image plane, as input, our method solves for robot trajectories and gimbal controls automatically and adapts plans in real time due to changes in the environment. We contribute a real-time receding horizon planner that autonomously records scenes with moving targets, while optimizing for visibility under occlusion and ensuring collision-free trajectories. A modular cost function, based on the reprojection error of targets, is proposed that allows for flexibility and artistic freedom and is well behaved under numerical optimization. We formulate the minimization problem under constraints as a finite horizon optimal control problem that fulfills aesthetic objectives, adheres to nonlinear model constraints of the filming robot and collision constraints with static and dynamic obstacles and can be solved in real time. We demonstrate the robustness and efficiency of the method with a number of challenging shots filmed in dynamic environments including those with moving obstacles and shots with multiple targets to be filmed simultaneously.

No files available

Metadata only record. There are no files for this record.