Research Papers - Dept of Information Technology

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Author: search.filters.author.Rajapaksha, SStart date: 2023

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Now showing 1 - 3 of 3
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    PublicationOpen Access
    Adding Common Sense to Robots Using Ontology
    (International Association of Computer Science and Information Technology, 2025-04-11) Ranathunga, R.A.A.L; Rajapaksha, S
    This work investigates how ontological frameworks might improve robots’ ability to reason using common sense. The goal of the project was to enhance robot decision-making in dynamic real-world situations by developing an ontology-based model retraining technique. The researchers wanted to incorporate organized commonsense knowledge into robotic systems, so they built extensive ontologies that captured knowledge about the physical world and human interactions. The research compared the performance of robots with conventional models (control group) to those with ontology-enhanced models (experimental group) across various measures. The results indicate that this strategy may be used to develop more competent and user-friendly robotic helpers for a variety of sectors, including industry, healthcare, and education. Although the study has limitations related to data quality and experimental design, it does demonstrate the promise of ontology-based techniques to advance autonomous systems and human-robot interactions. Extending ontology databases, multidisciplinary cooperation, and investigating applications in other sectors are some of the future research goals.
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    PublicationOpen Access
    Improved Path Planning for Multi-Robot Systems Using a Hybrid Probabilistic Roadmap and Genetic Algorithm Approach
    (Department of Agribusiness, Universitas Muhammadiyah Yogyakarta, 2025-03-24) Jathunga, T; Rajapaksha, S
    This study focuses on the development and application of an improved Probabilistic Roadmap (PRM) algorithm enhanced with Genetic Algorithms (GA) for multi-robot path planning in dynamic environments. Traditional PRM-based methods often struggle with optimizing path length and minimizing turns, particularly in complex, multi-agent scenarios. To address these limitations, we propose a hybrid PRM-GA approach that incorporates genetic operators to evolve optimal paths for multiple robots in real-time.The research contribution is an enhanced PRM-GA framework that improves efficiency in multi-robot navigation by integrating evolutionary techniques for dynamic obstacle handling and optimized path generation.The research methodology involves testing the algorithm in various environments, including varying robot numbers and environmental complexities, to evaluate its scalability and effectiveness. Our results demonstrate that the PRM-GA algorithm successfully reduces both path lengths and turn counts compared to standard PRM-based methods, ensuring collision-free and smooth paths. The algorithm showed robust performance across different scenarios, effectively handling dynamic obstacles and multi-agent coordination. However, in highly dynamic environments with rapidly changing obstacles and constraints, the algorithm may occasionally produce paths with turn counts and distances similar to or slightly higher than those of simpler approaches due to the need for frequent re-optimization. Future research can explore incorporating additional factors such as energy consumption and time optimization, alongside distance and turns, to further enhance the algorithm's efficiency in real-world applications. Overall, the PRM-GA approach advances the state of the art by offering a more adaptable and scalable solution for multi-robot path planning, with applications in logistics, industrial automation, and autonomous robotics.
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    PublicationEmbargo
    Cloud-based Salesman-Bot for Ontology-based Negotiation
    (IEEE, 2023-04-06) Fernando, A; Rahubedda, T; Jayasinghe, B; Mallikahewa, S; Hettiarachchi, O; Rajapaksha, S
    We have proposed a cloud-based ChatBot (Salesman-Bot) approach to handling multiple negotiation scenarios in a supermarket environment. The web application is a simple interface that can be implemented on a single standalone device or interacted with through a mobile phone. The Salesman-Bot responds both via text and speech. By introducing a Salesman-Bot, efficient negotiation, with quick preferences and suggestions can be provided. A new architecture proposed to operate the Salesman-Bot together with Google APIs and libraries such as Natural Language AI, Vision AI, Speech to Text API, Text to Speech API and Machine Learning using TensorFlow. The application also uses the Google Cloud Platform with related services such as Google App Engine. The goal is to make ChatBots more efficient in negotiating in different business scenarios. This paper presents the work carried out with ontology and machine learning in a cloud-based environment to handle multiple negotiation scenarios based on a negotiation hierarchy. It also proposes the opportunities and drawbacks of such a system.