Department Publications

Intelligent control of RF transceivers adapting to dynamic operational conditions is essential in the modern and future communication systems. We propose a multi-agent neurosymbolic AI system, where AI agents are assigned for circuit components. Agents have an internal model and a corresponding control algorithm as its constituents. Modeling of the IF amplifier shows promising results, where the same approach can be extended to all the components, thus creating a fully intelligent RF system.

Vision-Language Navigation (VLN) enables robots to navigate through environments using natural language instructions, making human-robot interaction intuitive. Traditional VLN models often rely on navigation graphs, 360-degree views, and perfect localization which pose significant challenges when adapting these models to real-world settings. This work addresses these limitations by performing a simulation-to-real domain shift of a VLN approach that operates in continuous environments without requiring navigation graphs or panoramic views. The proposed system integrates vision-language models that align visual inputs and linguistic instructions within a shared embedding space, facilitating natural language-driven navigation. We employ a Cross-Modal Attention (CMA) based architecture trained on an existing dataset in a simulated environment and fine-tune it using real-world data collected from a …

Trajectory planning strongly influences tracking accuracy, actuator demand, and overall execution behavior in robotic manipulators. Classical planners such as cubic, quintic, and trapezoidal profiles are widely used for their simplicity and smoothness, yet they remain purely kinematic and ignore system dynamics and control effort during trajectory generation. As a result, nominally smooth trajectories can lead to inefficient nonlinear execution and increased corrective control action. This paper presents a control-aware optimal trajectory planning framework that explicitly incorporates manipulator dynamics and actuator effort within a finite-horizon formulation. A midpoint linearization strategy is introduced to improve approximation accuracy for large point-to-point motions. In contrast to prior comparisons, the proposed approach enables fair, isolated evaluation of trajectory generation effects under identical closed-loop nonlinear execution conditions. To this end, a unified evaluation framework is developed in which all planners are executed under identical nonlinear dynamics, controller structure, and actuator constraints. Simulations on a nonlinear simplified UR5 manipulator show that the proposed approach consistently reduces tracking error, corrective torque, and closed-loop execution cost compared to classical methods, achieving substantial reductions in actuator effort and execution cost across all evaluated scenarios, demonstrating that kinematic smoothness alone does not ensure dynamically efficient execution.

Traffic signal design in developing countries frequently underperforms due to the direct application of classical models—Webster's delay formula, HCM, ARR123, and ORN13—developed for homogeneous, lane-disciplined traffic conditions. Sri Lankan intersections exhibit fundamentally different characteristics: heterogeneous vehicle streams comprising motorcycles, three-wheelers, non-motorized vehicles, and pedestrians operating without strict lane discipline under geometric constraints. This systematic review synthesizes empirical evidence explaining why conventional signal design methods fail in such contexts. Following PRISMA principles, 138 records were screened, yielding 41 high-quality references spanning foundational theory, classical assumptions, regional adaptations, and Sri Lankan empirical data. The review demonstrates that local intersections systematically violate foundational assumptions regarding lane usage, headway uniformity, start-up behavior, and pedestrian compliance. Observed saturation flows reach 2574 pcu/h/lane—substantially exceeding HCM benchmarks—while behaviors such as motorcycle seepage, lateral filtering, initial surge, and dynamic clustering create discharge patterns that static PCU values cannot capture. A mathematical framework is presented mapping these deviations to quantifiable variables: lateral efficiency, surge discharge dynamics, dynamic vehicle equivalence, headway expansion, and geometric constraints affecting intersection capacity. The paper concludes by proposing context-sensitive design pathways incorporating dynamic PCU factors, behavioral modeling, and simulation …

Humanoid robots have become increasingly popular in applications such as social interaction, education, and service roles, which drives the need for more natural and efficient human-robot interactions. However, currently available humanoid heads often face limitations, including high costs, mechanical complexity, and limited adaptability across diverse environments. To address these challenges, we present an articulated humanoid robot head designed for a receptionist role, integrating a mechanical structure with 21 degrees of freedom (DoF), including mechanisms for the mouth, eyes, eyebrows, and neck, and covered with realistic silicone skin to achieve a human-like appearance and expression. The system integrates a model-based architecture that combines SCRFD, ArcFace, and ByTetrack for face recognition and Llama and Whisper for natural language processing, with hardware support enabling real …

This paper presents a multi agent neurosymbolic artificial intelligence architecture to dynamically adapt operating parameters of radio frequency (RF) transceivers. The proposed dual branch architecture combines deep learning feature extraction with deterministic symbolic reasoning, leveraging sensed physical parameters such as signal power and spectrum occupancy, to ensure self-adapting and robust hardware safety. Component specific control agents are trained using digital twins and integrated with physical hardware via shared memory space and socket-based inter-process communication for low latency. Experimental results demonstrate accurate modeling and rapid parameter adaptation for error vector magnitude optimization, providing a scalable foundation for intelligent self-adapting RF transceivers.