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

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%.Comment: Accepted in the RoboCup Symposium 2017. Final version will be published at Springe

Evaluación de desempeño de redes convolucionales sobre arquitecturas heterogéneas para aplicaciones en robótica autónoma

Humanoid robots find application in human-robot interaction tasks. However, despite their capabilities, their sequential computing system limits the execution of computationally expensive algorithms such as convolutional neural networks, which have demonstrated good performance in recognition tasks. As an alternative to sequential computing units, Field-Programmable Gate Arrays and Graphics Processing Units have a high degree of parallelism and low power consumption. This study aims to improve the visual perception of a humanoid robot called NAO using these embedded systems running a convolutional neural network. The methodology adopted here is based on image acquisition and transmission using simulation software: Webots and Choreographe. In each embedded system, an object recognition stage is performed using commercial convolutional neural network acceleration frameworks. Xilinx® Ultra96™, Intel® Cyclone® V-SoC and NVIDIA® Jetson™ TX2 cards were used, and Tinier-YOLO, AlexNet, Inception-V1 and Inception V3 transfer-learning networks were executed. Real-time metrics were obtained when Inception V1, Inception V3 transfer-learning and AlexNet were run on the Ultra96 and Jetson TX2 cards, with frame rates between 28 and 30 frames per second. The results demonstrated that the use of these embedded systems and convolutional neural networks can provide humanoid robots such as NAO with greater visual recognition in tasks that require high accuracy and autonomy.Los robots humanoides encuentran aplicación en tareas de interacción humano-robot. A pesar de sus capacidades, su sistema de computación secuencial limita la ejecución de algoritmos computacionalmente costosos, como las redes neuronales convolucionales, que han demostrado buen rendimiento en tareas de reconocimiento. Como alternativa a unidades de cómputo secuencial se encuentran los Field Programmable Gate Arrays y las Graphics Processing Unit, que tienen un alto grado de paralelismo y bajo consumo de energía. Este trabajo tuvo como objetivo mejorar la percepción visual del robot humanoide NAO utilizando estos sistemas embebidos que ejecutan una red neuronal convolucional. El trabajo se basó en la adquisición y transmisión de la imagen usando herramientas de simulación como Webots y Choreographe. Posteriormente, en cada sistema embebido, se realizó una etapa de reconocimiento del objeto utilizando frameworks de aceleración comerciales de redes neuronales convolucionales. Luego se utilizaron las tarjetas Xilinx Ultra96, Intel Cyclone V-SoC y Nvidia Jetson TX2; después fueron ejecutadas las redes Tinier-Yolo, Alexnet, Inception V1 y Inception V3 transfer-learning. Se obtuvieron métricas en tiempo real cuando Inception V1, Inception V3 transfer-learning y AlexNet fueron ejecutadas sobre la Ultra96 y Jetson TX2, teniendo como intervalo entre 28 y 30 cuadros por segundo. Los resultados demostraron que el uso de estos sistemas embebidos y redes neuronales convolucionales puede otorgarles a robots humanoides, como NAO, mayor reconocimiento visual en tareas que requieren alta precisión y autonomía. &nbsp

Real-Time Object Recognition using a Multi-Framed Temporal Approach

Computer Vision involves the extraction of data from images that are analyzed in order to provide information crucial to many modern technologies. Object recognition has proven to be a difficult task and programming reliable object recognition remains elusive. Image processing is computationally intensive and this issue is amplified on mobile platforms with processor restrictions. The real-time constraints demanded by robotic soccer in RoboCup competition serve as an ideal format to test programming that seeks to overcome these challenges. This paper presents a method for ball recognition by analyzing the movement of the ball. Major findings include enhanced ball discrimination by replacing the analysis of static images with absolute change in brightness in conjunction with the classification of apparent motion change