6D Localization and Kicking for Humanoid Robotic Soccer

Robotic soccer simulation is a challenging area, where the development of new techniques is paramount to remain competitive. Robotic skill evolution has accelerated with recent developments in deep learning algorithms, leading to improvements in behavior number and complexity. Shooting a ball toward...

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Bibliographic Details
Published in:Journal of intelligent & robotic systems 2021-06, Vol.102 (2), Article 30
Main Authors: Abreu, Miguel, Silva, Tiago, Teixeira, Henrique, Reis, Luís Paulo, Lau, Nuno
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Complexity
Control
Data mining
Electrical Engineering
Engineering
Humanoid
Localization
Machine learning
Mechanical Engineering
Mechatronics
Optimization
Regular Paper
Robotics
Robots
Soccer
Topical collection on ICARSC’20
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Summary:Robotic soccer simulation is a challenging area, where the development of new techniques is paramount to remain competitive. Robotic skill evolution has accelerated with recent developments in deep learning algorithms, leading to improvements in behavior number and complexity. Shooting a ball towards a defined target is one of the most basic yet indispensable skills in soccer. However, fast and accurate kicks pose several challenges. In order to reach that target, the skill is highly dependent on the ability of the agent to self-locate and self-orient, in order to better position itself before the kick. To tackle these issues, a 6D localization technique was devised. To optimize the kick behavior, two scenarios were proposed. In the first, the robot walks to the ball, stops, and then kicks. In the second, it kicks the ball while moving. We used state-of-the-art algorithms — Proximal Policy Optimization and Soft Actor Critic — to solve these complex problems and show their applicability in the context of RoboCup. Obtained results have shown very significant improvements over previously used behaviors by FC Portugal 3D team. The new kick in motion executes 5 times faster than the previous kick, and the new 6D pose estimator has an average error of just 6.3mm, a reduction of more than 97 % .
ISSN:0921-0296
1573-0409
DOI:10.1007/s10846-021-01385-3