A mechanism for memorizing the route that a mobile robot passes in search of a target object is considered. The basis of the proposed method is to memorize the path by visual reference points and fuzzy control. The benchmark is a compact group of objects that differ in color and size. A hierarchical procedure for recognizing landmarks and scenes is described. An algorithm for forming the route description is proposed. The rules for its interpretation include elements of spatial logic. The results of simulation obtained using the Kvorum modeling system are presented.
For a large variety of tasks autonomous robots require a robust visual data processing system. This paper presents a new human detection framework that combines rotation-invariant histogram of oriented gradients (RIHOG) features and binarized normed gradients (BING) pre-processing and skin segmentation. For experimental evaluation a new Human body dataset of over 60000 images was constructed using the Human-Parts dataset, the Simulated disaster victim dataset, and the Servosila Engineer robot dataset. Random, Liner SVM, Quadratic SVM, AdaBoost, and Random Forest approaches were compared using the Human body dataset. Experimental evaluation demonstrated an average precision of 90.4% for the Quadratic SVM model and showed the efficiency of RIHOG features as a descriptor for human detection tasks.
The paper considers the problem of controlling a robot using a voice interface with speech recognition and analysis of the resulting set of words. The proposed method of command recognition is based on a dictionary of commands and special modifier words that are used for sentiment analysis of the command phrase and determining the priority of the task execution.
The mobile robots control subsystems are presented.