6D Localization and Kicking for Humanoid Robotic Soccer

• Tipo: Artigo em Revista Científica Internacional
• Data: 2021

Autores

Miguel Abreu (Autor) Outra; Tiago Silva (Autor) Outra; Henrique Teixeira (Autor) Outra; Luís Paulo Reis (Autor) FEUP; Nuno Lau (Autor) Outra

Revista

Título: Journal of Intelligent and Robotic Systems: Theory and Applications

Vol. 102

Páginas: 1-30

ISSN: 0921-0296

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 5 Citações

Scopus - 8 Citações

Outras Informações

ID Authenticus: P-00T-WNG

DOI: 10.1007/s10846-021-01385-3

Abstract (EN): 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%.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 25