RETRACTED: Design of small humanoid fighting robot based on target recognition algorithm

Abstract

In the past, robots can only do simple walking, but now robots can perform complex actions such as running, attacking and automatically standing up after falling to the ground. The humanoid robot competition is very interesting and has great significance for the study. The speed and accuracy of robots in domestic competitions still need to be improved. In this article, a design scheme of a small humanoid fighting robot based on the target recognition algorithm is proposed. In this article, a design scheme of a small humanoid fighting robot based on the target recognition algorithm is proposed, for the case where the mobile robot has a low recognition rate in a complicated working environment. In this article, based on the matching of single colour features, the matching of shapes is added to more accurately identify the target. In addition, visual information added to achieve information fusion, the algorithm is simpler and can achieve positioning more quickly and effectively. The humanoid robot is to detect different states of the target, and needs to measure the target, trajectory of the target and analyse the target motion. In the era of popular 5G networks, it is hoped that in the future, robots that use 5G networks for communication can be designed, so that robots can be remotely controlled anytime and anywhere. There will be more kinds of robots entering our lives, which will promote the construction and application of smart homes and smart cities.

Target Recognition of Industrial Robots Using Machine Vision in 5G Environment

“…The NYU Depth V2 database is introduced (Chen et al, 2019) with the leave-one-out evaluation method adopted; that is, the dataset with a sample space of 1100 is divided into subsets of 1000 and 100, with the subset of 1000 used as the training set and subset of 100 as the test set. The constructed system model is compared with advanced CNNs (AlexNet, GoogleNet, LeNet, ZF-Net, and ResNet) (Wang et al, 2017;Fadlullah et al, 2018;Luo et al, 2019;Hosny et al, 2020;Wang and Jia, 2020). The following equation shows the accuracy.…”