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Build a flight planner system which computes the trajectory for the drone to fly and capture photos efficiently as per user specification.
Capturing high quality image datasets is a rapidly expanding field with a wide range of applications: mapping, survey, digital 3D models, and datasets for AI. Drones are transforming the industry by providing a safe, cheap, and accessible platform which can navigate difficult terrain and capture datasets with remarkable precision and efficiency. In this project, you will wear the hat of a Roboticist and a Software Developer and develop a path planning system which can capture the image datasets in an efficient manner.
Under the guidance of an industry expert, you’ll develop data models in Python for user requirements and constraints, develop modular APIs to calculate the position and the speed of the drones, and build visualization tools to analyze the computed trajectories. You’ll learn about camera systems and the image capture process, mapping and photogrammetry concepts like overlap, and industry conventions to represent the path of robots. Your project leader will provide the mathematical concepts required for the project and simulate an environment of a real robotics software development team.
Get to know the project leader and each other. Learn about the overview of the project, schedule, and the deliverables expected. Finally, get your computers set up to start coding.
Learn how cameras work, and create Python functions to model the mathematics of a simplified camera system.
Learn various specifications of the image datasets, and model them in Python. Understand the effect of various parameters on the compliant drone paths.
Build upon the concepts covered in workshop 2 and 3 to develop Python code to compute the distance between photos which satisfy the user specifications.
Learn about motion blur: why it occurs and how it affects photos. Write a python function to compute the maximum speed of travel to limit blur.
Use the python functions developed for distance and speed computation to create the flight plan for the full scan area.
Develop visualization tools to view the flight plans in the Jupyter notebook. Leverage the visualizations to demonstrate the impact of various parameters of the user specifications on the flight plans.
Use the Jupyter notebook used in the project to present the concepts and visualizations to the project leader and peer group. Reflect on the journey and learnings in the project.
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Ayush is a Software Development Fellow, based out of Bay Area, California. Ayush works as an Autonomy Engineer at Skydio, working on inspection and mapping software for drones. Ayush has a Masters in Computer Science from Georgia Tech, with specialization in Machine Learning. Ayush likes to play the piano, read, and be outdoors in his free time.