Using Convolutional Neural Networks in Robots with Limited Computational Resources: Detecting NAO Robots While Playing Soccer

Abstract

The main goal of this paper is to analyze the general problem of using Convolutional Neural Networks (CNNs) in robots with limited computational capabilities, and to propose general design guidelines for their use. In addition, two different CNN based NAO robot detectors that are able to run in real-time while playing soccer are proposed. One of the detectors is based on the XNOR-Net and the other on the SqueezeNet. Each detector is able to process a robot object-proposal in ~1ms, with an average number of 1.5 proposals per frame obtained by the upper camera of the NAO. The obtained detection rate is ~97%.

Playing Soccer Without Colors in the SPL: A Convolutional Neural Network Approach

“…Finally, this also proves that the use of color information is not necessary to detect robots or balls when using expressive classifiers such as CNNs. In fact, the CNN used in the robot detector achieves a similar accuracy rate that the model proposed in [3], while being approximately 2.75 times faster.…”

“…In [3] we presented a robot detector based on CNNs, capable of operating in real time. The system was based on the classification of color-based robot proposals (generated by B-Human's robot perceptor [8]).…”

“…While these CNNs are used for different purposes, their architectures remain similar across all the developed modules and are based on the work presented in [3], with slight variations to achieve higher speeds while maintaining accuracy. The main component of these architectures is the extended Fire module, which was developed in [3] inspired on the original Fire module proposed in [9]. This module concatenates the outputs produced by filters of different sizes in order to achieve increased accuracy while being computationally inexpensive.…”

“…However, still most of the teams use color information to detect field features (e.g., lines and their intersections), other players and the ball. Very recently, Convolutional Neural Networks (CNNs) have been used for detecting the robots and/or the ball (e.g., [3,4,5,6]), but even in these cases, the CNN-based detectors require object proposals which are usually obtained using color information. Therefore, to the best of our knowledge, color-free vision systems have not been used in robotic soccer, at least not in the SPL.…”

xYOLO: A Model For Real-Time Object Detection In Humanoid Soccer On Low-End Hardware

“…Our team, Electric Sheep, competed in the 2019 world cup with our unique low-cost open-source humanoid robotics platform 2 . The purpose of designing and building a low-cost platform was to lower the boundary to entry, as larger robots have seen larger costs in recent years which could discourage new teams from entering the competition.…”