Visual Navigation for Biped Humanoid Robots Using Deep Reinforcement Learning

Lobos-Tsunekawa, Kenzo; Leiva, Francisco; Ruiz‐del‐Solar, Javier

doi:10.1109/lra.2018.2851148

Cited by 81 publications

(40 citation statements)

References 7 publications

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“…The recognition of basic basketball movements is of great value to further improve the training efficiency of athletes, and the division of basketball posture composition is shown in Figure 1 . In ordinary training, basic training movements mainly include dribbling, ball control, passing, catching, shooting, and pace adjustment (Lobos-Tsunekawa et al, 2018 ). In specific training, basketball posture can be divided into two states: static and moving based on the different states of the body.…”

Section: Methodsmentioning

confidence: 99%

Adoption of Machine Learning Algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention

Tang

2021

Front. Neurorobot.

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In order to effectively prevent sports injuries caused by collisions in basketball training, realize efficient shooting, and reduce collisions, the machine learning algorithm was applied to intelligent robot for path planning in this study. First of all, combined with the basketball motion trajectory model, the sport recognition in basketball training was analyzed. Second, the mathematical model of the basketball motion trajectory of the shooting motion was established, and the factors affecting the shooting were analyzed. Thirdly, on this basis, the machine learning-based improved Q-Learning algorithm was proposed, the path planning of the moving robot was realized, and the obstacle avoidance behavior was accomplished effectively. In the path planning, the principle of fuzzy controller was applied, and the obstacle ultrasonic signals acquired around the robot were taken as input to effectively avoid obstacles. Finally, the robot was able to approach the target point while avoiding obstacles. The results of simulation experiment show that the obstacle avoidance path obtained by the improved Q-Learning algorithm is flatter, indicating that the algorithm is more suitable for the obstacle avoidance of the robot. Besides, it only takes about 250 s for the robot to find the obstacle avoidance path to the target state for the first time, which is far lower than the 700 s of the previous original algorithm. As a result, the fuzzy controller applied to the basketball robot can effectively avoid the obstacles in the robot movement process, and the motion trajectory curve obtained is relatively smooth. Therefore, the proposed machine learning algorithm has favorable obstacle avoidance effect when it is applied to path planning in basketball training, and can effectively prevent sports injuries in basketball activities.

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Section: Methodsmentioning

confidence: 99%

Adoption of Machine Learning Algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention

Tang

2021

Front. Neurorobot.

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“…Therefore, Zhu et al proposed a DRL architecture which mapped RGB images to control policies directly [20]. Instead of using raw RGB images, segmented ones were adopted in [21] to control humanoid robots. Nevertheless, although RGB images are more informative, they suffer from the significant deviations between the simulated and realworld domains.…”

Section: Introductionmentioning

confidence: 99%

BND*-DDQN: Learn to Steer Autonomously Through Deep Reinforcement Learning

Wang

Esfahani

et al. 2021

IEEE Trans. Cogn. Dev. Syst.

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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…Changxi et al [28] has proposed using learning aids to guide self-sufficiency facilities. Kenzo et al [29] used DDPG-assisted learning calculations to design the motion of bipedal robots in football coordinates. Farad et al [30] has created a way to master proficiency in difficult conditions through the Enterunder Pundit Fortress learning model.…”

Section: Introductionmentioning

confidence: 99%