Object detection on robosoccer environment using convolution neural network

Abstract

Robots with autonomous capabilities depend on vision capabilities to detect and interact with objects and their environment. In the field of robotic research, one of the focus areas is the robosoccer platform that is being used to implement and test new ideas and findings on computer vision and decision making. In this article, an efficient real-time object detection algorithm is employed in a robosoccer simulation environment by deploying a convolution neural network and Kalman filter based tracking algorithms. This study's objective is to classify nao, ball, and the goalpost as well as to validate nao and ball tracking without human intervention from initial frame to last frame. In comparison with the existing methods, the proposed method is robust and fast in identifying three classes namely nao, ball, and goalpost with a speed of 1.67 FPS and a mAP of 95.18%. By implementing this approach, soccer playing robots can make appropriate decisions during game play.

Bayesian probabilistic modeling in robosoccer environment for robot path planning

“…Proposed system architecture Though the proposed system in this paper finds the shortest path to help the robot to reach the ball, the overall flow of the system shown in Figure 1 includes the object detection approach to find the ball in the image. To locate the ball in the robosoccer environment, the algorithm in [23] is employed before planning the best path without collision. It is a three-class classification problem using a convolution neural network [24] and Kalman filter as tracking algorithms in a robosoccer simulation environment.…”