Evolution Algorithm and Online Learning for Racing Drone

Sangyun Shin, Yongwon Kang, Yong-Guk Kim
Proceedings of the NeurIPS 2019 Competition and Demonstration Track, PMLR 123:100-109, 2020.

Abstract

Drone racing has become one of the challenging topics in robotics and machine learning because such a drone requires to equip with high performing modules that carry out demanding tasks, such as obstacle avoidance, mapping, and planning. In addition, one of the most crucial aspects of the racing drone is its speed. However, this is the somewhat less studied area compared to conventional topics such as obstacle avoidance and path-finding, probably because designing a loss function for the speed optimization with the gradient-based method is difficult. In this paper, we propose an evolutionary scheme for optimizing the speed-related parameters for shortening the travel time rather than using the gradient-based loss for them. For the planning part, we use an online learning method with the racing parameter optimization. Therefore, our approach is to combine evolutionary algorithms for speed optimization and gradient-based online learning, achieving first place in Tier 2 and Tier 3 in Game of Drones competition at NeurIPS 2019.

Cite this Paper

BibTeX
@InProceedings{pmlr-v123-shin20a, title = {Evolution Algorithm and Online Learning for Racing Drone}, author = {Shin, Sangyun and Kang, Yongwon and Kim, Yong-Guk}, booktitle = {Proceedings of the NeurIPS 2019 Competition and Demonstration Track}, pages = {100--109}, year = {2020}, editor = {Escalante, Hugo Jair and Hadsell, Raia}, volume = {123}, series = {Proceedings of Machine Learning Research}, month = {08--14 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v123/shin20a/shin20a.pdf}, url = {https://proceedings.mlr.press/v123/shin20a.html}, abstract = {Drone racing has become one of the challenging topics in robotics and machine learning because such a drone requires to equip with high performing modules that carry out demanding tasks, such as obstacle avoidance, mapping, and planning. In addition, one of the most crucial aspects of the racing drone is its speed. However, this is the somewhat less studied area compared to conventional topics such as obstacle avoidance and path-finding, probably because designing a loss function for the speed optimization with the gradient-based method is difficult. In this paper, we propose an evolutionary scheme for optimizing the speed-related parameters for shortening the travel time rather than using the gradient-based loss for them. For the planning part, we use an online learning method with the racing parameter optimization. Therefore, our approach is to combine evolutionary algorithms for speed optimization and gradient-based online learning, achieving first place in Tier 2 and Tier 3 in Game of Drones competition at NeurIPS 2019. } }
Endnote
%0 Conference Paper %T Evolution Algorithm and Online Learning for Racing Drone %A Sangyun Shin %A Yongwon Kang %A Yong-Guk Kim %B Proceedings of the NeurIPS 2019 Competition and Demonstration Track %C Proceedings of Machine Learning Research %D 2020 %E Hugo Jair Escalante %E Raia Hadsell %F pmlr-v123-shin20a %I PMLR %P 100--109 %U https://proceedings.mlr.press/v123/shin20a.html %V 123 %X Drone racing has become one of the challenging topics in robotics and machine learning because such a drone requires to equip with high performing modules that carry out demanding tasks, such as obstacle avoidance, mapping, and planning. In addition, one of the most crucial aspects of the racing drone is its speed. However, this is the somewhat less studied area compared to conventional topics such as obstacle avoidance and path-finding, probably because designing a loss function for the speed optimization with the gradient-based method is difficult. In this paper, we propose an evolutionary scheme for optimizing the speed-related parameters for shortening the travel time rather than using the gradient-based loss for them. For the planning part, we use an online learning method with the racing parameter optimization. Therefore, our approach is to combine evolutionary algorithms for speed optimization and gradient-based online learning, achieving first place in Tier 2 and Tier 3 in Game of Drones competition at NeurIPS 2019.
APA
Shin, S., Kang, Y. & Kim, Y.. (2020). Evolution Algorithm and Online Learning for Racing Drone. Proceedings of the NeurIPS 2019 Competition and Demonstration Track, in Proceedings of Machine Learning Research 123:100-109 Available from https://proceedings.mlr.press/v123/shin20a.html.

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