Faculty of Computing

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Subject: search.filters.subject.Robot

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    PublicationEmbargo
    Moving a Robot In Unknown Areas Without Collision Using Robot Operating System
    (IEEE, 2022-02-23) Gayashani, K. K. P; Rajapaksha, S; Jayawardena, C
    Nowadays, robots have become a most crucial role. With technology development, we can do so many things using robotic technology. There are lots of projects in which robots move in a known area. This study proposes a mechanism to move a robot in an unknown area. We can use this kind of robot in hazardous environments, and we can use this robot in several ways. The proposed system is based on the Robotic Operation System (ROS) and the simulator Gazebo. The obstacle avoidance part is done using a laser sensor. After that, there should be a direction-changing mechanism in the developing algorithm. That implemented using loops. Because after the robot changes direction, it again needs to check whether another object is there in the navigated location. The proposed algorithm was developed with the autonomous navigation mechanism. Map generation is another functionality of this project. It is done using Simultaneous Localization And Mapping (SLAM). Map visualization was done using the Rviz application. With the robot’s movement, the robot’s current position is calculated using x, y, and z coordinates. Also, this project has included reverse navigation functionality. Reverse navigation is a novel section in this research work. The objective of this study and the outcome is to move the robot without having any crashes. Also, we can use this to evaluate dangerous areas. Experimental results of the direction and velocity changes have been mentioned in the results and discussion section.
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    PublicationOpen Access
    Voice Communication in Performing a Cooperative Task with a Robot
    (Springer, Tokyo, 2002) Pulasinghe, K; Watanabe, K; Kiguchi, K; Izumi, K
    This paper investigates the credibility of voice (especially natural language commands) as a communication medium in sharing advanced sensory capacity and knowledge of the human with a robot to perform a cooperative task. Identification of the machine sensitive words in the unconstrained speech signal and interpretation of the imprecise natural language commands for the machine has been considered. The system constituents include a hidden Markov model (HMM) based continuous automatie speech recognizer (ASR) to identify the lexical content of the user's speech signal, a fuzzy neural network (FNN) to comprehend the natural language (NL) contained in identified lexical content, an artificial neural network (ANN) to activate the desired functional ability, and contral modules to generate output signals to the actuators of the machine. The characteristic features have been tested experimentally by utilizing them to navigate a Khepera® in real time using the user's visual information transferred by speech signals