Scalable Task Cleanup Assignment for Multi-agents

This paper describes a group of robots for cleaning a simulated environment and proposes an ecient algorithm for navigation based on Path nding A *. No need for vision sensors. As a result it was observed that the robots can work cooperatively to clear the ground and that the navigation algorithm is e ective in cleaning. In order to test its eciency it was compared the combination of the Path nding A* algorithm and the decision algorithm proposed in this paper with Path nding A* and Euclidean distance, resulted in an improvement in time and distance traveled

Navigation of quadruped multirobots by gesture recognition using restricted boltzmann machines

This article discusses a method that performs gesture recognition, with the objective of extracting characteristics of the segmented hand, from dynamic images captured from a webcam and identifying signal patterns. With this method it is possible to manipulate simulated multirobots that perform specific movements. The method consists of the Continuously Adaptive Mean-SHIFT algorithm, followed by the Threshold segmentation algorithm and Deep Learning through Boltzmann restricted machines. As a result, an accuracy of 82.2%

Scalable Task Cleanup Assignment for Multi-agents

This paper describes a group of robots for cleaning a simulated environment and proposes an ecient algorithm for navigation based on Path nding A *. No need for vision sensors. As a result it was observed that the robots can work cooperatively to clear the ground and that the navigation algorithm is e ective in cleaning. In order to test its eciency it was compared the combination of the Path nding A* algorithm and the decision algorithm proposed in this paper with Path nding A* and Euclidean distance, resulted in an improvement in time and distance traveled

Navigation of quadruped multirobots by gesture recognition using restricted boltzmann machines

This article discusses a method that performs gesture recognition, with the objective of extracting characteristics of the segmented hand, from dynamic images captured from a webcam and identifying signal patterns. With this method it is possible to manipulate simulated multirobots that perform specific movements. The method consists of the Continuously Adaptive Mean-SHIFT algorithm, followed by the Threshold segmentation algorithm and Deep Learning through Boltzmann restricted machines. As a result, an accuracy of 82.2%