Faculty of Computing

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    Deep Q-Network-Based Path Planning in a Simulated Warehouse Environment with SLAM Map Integration and Dynamic Obstacles
    (Department of Agribusiness, Universitas Muhammadiyah Yogyakarta, 2025-09-19) Medagangoda, H; Jayawickrama, N; de Silva, R; Samantha K.R.U.U; Abeygunawardhana, P.K.W
    With the rise of e-Commerce and the evolution of robotic technologies, the focus on autonomous navigation within warehouse environments has increased. This study presents a simulation-based framework for path planning using Deep Q-Networks (DQN) in a warehouse environment modeled with moving obstacles. The proposed solution integrates a prebuilt map of the environment generated using Simultaneous Localization and Mapping (SLAM), which provides prior spatial knowledge of static obstacles. The reinforcement learning model is formulated with a state space derived from grayscale images that combine the static map generated by SLAM and dynamic obstacles in real time. The action space consists of four discrete movements for the agent. A reward shaping strategy includes a distance-based reward and penalty for collisions to encourage goal-reaching and discourage collisions. An epsilon-greedy policy with exponential decay is used to balance exploration and exploitation. This system was implemented in the Robot Operating System (ROS) and Gazebo simulation environment. The agent was trained over 1000 episodes and metrics such as the number of actions executed to reach the goal and the cumulative reward per episode were analyzed to evaluate the convergence of the proposed solution. The results across two goal locations show that incorporating the SLAM map enhances learning stability, with the agent reaching a goal approximately 150 times, nearly double the success rate compared to the baseline without map information, which achieved only 80 successful episodes over the same number of episodes. This indicates faster convergence and reduced exploration overhead due to improved spatial awareness.
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    Development of an Elephant Detection and Repellent System based on Efficient Det-Lite Models
    (IEEE, 2023-04-03) Pemasinghe, S; Abeygunawardhana, P.K.W
    Human-elephant conflict (HEC) has become a major concern in Sri Lanka that results in many unfortunate human and elephant deaths. Methods that are currently in place to mitigate HEC, such as electrical fences have undesirable consequences resulting in both human and elephant casualties. In this paper, we have proposed a method based on computer vision and deep learning that has a promising potential for detecting and repelling elephants without endangering the lives of elephants or humans. We have used EfficientDet-Lite models that provide a good compromise between accuracy and performance in order to be usable with a resource-constrained device like a Raspberry Pi.
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    Tuning of Optoelectronic Properties of Chalcohalides by Tailoring Pnictogen Composition for Sustainable Photovoltaics
    (John Wiley and Sons Inc, 2025-08) Hu, D; Abeygunawardhana, P.K.W; Asha, G
    This study investigates Sb1-xBixSeI pnictogen chalcohalides as lead-free materials for photovoltaic and optoelectronic applications using density functional theory (DFT) calculations. Increasing Bi content from 0.5 to 0.6 reduces the bandgap from 1.60 to 1.43 eV, enhancing the light absorption and aligning with the optimal range for solar energy conversions. Structural analysis reveals that higher Bi substitution expands the lattice, reduces the hole effective mass, and improves the hole mobility, while the electron mobility decreases slightly. Sb0.4Bi0.6SeI demonstrates quasi-direct bandgap characteristics attributed to Bi-induced lattice distortion and strong spin–orbit coupling (SOC), which reduces the conduction band minimum and facilitate direct-like electronic transitions. Enhanced absorption near the band edge and localized states contribute to higher sub-bandgap absorption, broadening the spectral response. Reduced bandgap falls within the optimal range for single-junction solar cells, increasing photocurrent generation. While defect-induced recombination poses challenges, passivation and compositional tuning can optimize its performance. This study identifies the potential of Sb0.4Bi0.6SeI as a versatile absorber material in emerging solar cell architectures. The findings provide a pathway toward designing cost-effective and sustainable materials with tailored properties for next-generation photovoltaic and optoelectronic technologies.