Autonomous Drone Racing: A Survey
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914860132990976 |
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| author | Hanover, Drew Loquercio, Antonio Bauersfeld, Leonard Romero, Angel Penicka, Robert Song, Yunlong Cioffi, Giovanni Kaufmann, Elia Scaramuzza, Davide |
| author_facet | Hanover, Drew Loquercio, Antonio Bauersfeld, Leonard Romero, Angel Penicka, Robert Song, Yunlong Cioffi, Giovanni Kaufmann, Elia Scaramuzza, Davide |
| contents | Over the last decade, the use of autonomous drone systems for surveying, search and rescue, or last-mile delivery has increased exponentially. With the rise of these applications comes the need for highly robust, safety-critical algorithms which can operate drones in complex and uncertain environments. Additionally, flying fast enables drones to cover more ground which in turn increases productivity and further strengthens their use case. One proxy for developing algorithms used in high-speed navigation is the task of autonomous drone racing, where researchers program drones to fly through a sequence of gates and avoid obstacles as quickly as possible using onboard sensors and limited computational power. Speeds and accelerations exceed over 80 kph and 4 g respectively, raising significant challenges across perception, planning, control, and state estimation. To achieve maximum performance, systems require real-time algorithms that are robust to motion blur, high dynamic range, model uncertainties, aerodynamic disturbances, and often unpredictable opponents. This survey covers the progression of autonomous drone racing across model-based and learning-based approaches. We provide an overview of the field, its evolution over the years, and conclude with the biggest challenges and open questions to be faced in the future. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_01755 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Autonomous Drone Racing: A Survey Hanover, Drew Loquercio, Antonio Bauersfeld, Leonard Romero, Angel Penicka, Robert Song, Yunlong Cioffi, Giovanni Kaufmann, Elia Scaramuzza, Davide Robotics Over the last decade, the use of autonomous drone systems for surveying, search and rescue, or last-mile delivery has increased exponentially. With the rise of these applications comes the need for highly robust, safety-critical algorithms which can operate drones in complex and uncertain environments. Additionally, flying fast enables drones to cover more ground which in turn increases productivity and further strengthens their use case. One proxy for developing algorithms used in high-speed navigation is the task of autonomous drone racing, where researchers program drones to fly through a sequence of gates and avoid obstacles as quickly as possible using onboard sensors and limited computational power. Speeds and accelerations exceed over 80 kph and 4 g respectively, raising significant challenges across perception, planning, control, and state estimation. To achieve maximum performance, systems require real-time algorithms that are robust to motion blur, high dynamic range, model uncertainties, aerodynamic disturbances, and often unpredictable opponents. This survey covers the progression of autonomous drone racing across model-based and learning-based approaches. We provide an overview of the field, its evolution over the years, and conclude with the biggest challenges and open questions to be faced in the future. |
| title | Autonomous Drone Racing: A Survey |
| topic | Robotics |
| url | https://arxiv.org/abs/2301.01755 |