Developing intelligent decision-making algorithms that enable autonomous robots to plan, coordinate, and adapt in uncertain and dynamic environments using reinforcement learning, probabilistic planning, and multi-agent autonomy.
Advancing robotic perception through simultaneous localisation and mapping (SLAM), sensor fusion, and vision-based localisation to enable reliable operation in complex and GPS-denied environments.
Developing Bayesian estimation and Random Finite Set (RFS) based algorithms for robust multi-object tracking, state estimation, and situational awareness under uncertainty.
Designing and deploying autonomous aerial, ground, and marine robotic systems that integrate perception, estimation, and decision-making for real-world field applications.
Applying autonomous robotics and artificial intelligence to solve real-world challenges in environmental monitoring, marine science, industrial automation, infrastructure inspection, and defence.
A probabilistic 3D Visual SLAM framework based on Random Finite Set theory for robust localisation and mapping in GPS-denied environments.
A probabilistic collaborative LiDAR SLAM framework enabling multiple autonomous robots to jointly localise and construct a shared map using Random Finite Set theory.
Joint probabilistic estimation of robot trajectories, static maps, and moving objects using Random Finite Set theory.
Design and collection of real-world datasets for validating probabilistic localisation and mapping algorithms in marine environments.
A ROS2-based localisation framework using AprilTags for cooperative localisation of autonomous aerial robots.
A decentralized reinforcement learning framework for cooperative pursuit of evasive targets using multiple autonomous robots, funded by Central Queensland University (CQU).
Developing decentralised probabilistic perception algorithms that enable teams of autonomous robots to collaboratively understand complex environments.
Published in IEEE OCEANS, Sydney, Australia, 2010
A collaborative localisation and mapping framework for multiple vehicles operating in marine environments.
Recommended citation: M.D.P. Moratuwage, W.S. Wijesoma, B. Kalyan, J.F. Dong, P.G.C.N. Senarathne, F.S. Hover, N.M. Patrikalakis (2010). "Collaborative multi-vehicle localization and mapping in marine environments." IEEE OCEANS, Sydney, Australia.
Published in IEEE OCEANS, Sydney, Australia, 2010
MarineSIM, a robot simulation environment designed for marine robotics research and development.
Recommended citation: P.G.C.N. Senarathne, W.S. Wijesoma, K.W. Lee, B. Kalyan, M.D.P. Moratuwage (2010). "MarineSIM: Robot Simulation for Marine Environments." IEEE OCEANS, Sydney, Australia.
Published in IEEE International Conference on Systems Man and Cybernetics (SMC), Turkey, 2010
Application of random finite set theory to detection and tracking using 3D LIDAR sensors in dynamic environments.
Recommended citation: B. Kalyan, K.W. Lee, S. Wijesoma, D. Moratuwage, N.M. Patrikalakis (2010). "A random finite set based detection and tracking using 3D LIDAR in dynamic environments." IEEE SMC, Turkey.
Published in IEEE International Conference on Control Automation Robotics & Vision (ICARCV), Singapore, 2010
Collaborative localisation and mapping in high clutter density environments using multiple vehicles.
Recommended citation: M.D.P. Moratuwage, W.S. Wijesoma, B. Kalyan, N.M. Patrikalakis, P. Moghadam (2010). "Collaborative multi-vehicle localization and mapping in high clutter environments." IEEE ICARCV, Singapore.
Published in IEEE International Conference on Control Automation Robotics & Vision (ICARCV), Singapore, 2010
Progress towards a fully autonomous vision-based navigation system for vehicles in unstructured outdoor environments.
Recommended citation: P. Moghadam, W.S. Wijesoma, M.D.P. Moratuwage (2010). "Towards fully autonomous vision-based vehicle navigation system in outdoor environments." IEEE ICARCV, Singapore.
Published in Information Fusion (FUSION), Singapore, 2012
A hierarchical approach to solving the multi-vehicle SLAM problem, improving scalability and consistency across multiple agents.
Recommended citation: D. Moratuwage, B.N. Vo and W. Danwei (2012). "A Hierarchical Approach to the Multi-Vehicle SLAM problem." FUSION, Singapore.
Published in IEEE International Conference on Control Automation Robotics & Vision (ICARCV), Guangzhou, China, 2012
Extension of the Bayesian random finite set SLAM framework to the multi-vehicle setting for collaborative localisation and mapping.
Recommended citation: D. Moratuwage, B.N. Vo and W. Danwei (2012). "Extending Bayesian RFS SLAM to Multi-Vehicle SLAM." IEEE ICARCV, Guangzhou, China.
Published in IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany, 2013
A collaborative SLAM framework for multiple vehicles that simultaneously tracks moving objects using random finite set theory.
Recommended citation: D. Moratuwage, B.N. Vo and W. Danwei (2013). "Collaborative Multi-Vehicle SLAM with Moving Object Tracking." IEEE ICRA, Karlsruhe, Germany.
Published in IEEE Robotics and Automation Magazine, 2014
This paper presents a random finite set based collaborative SLAM framework for dynamic, high-clutter environments using multiple vehicles.
Recommended citation: D. Moratuwage, D. Wang, R. Akshay, P. Senarathne, H. Wang (2014). "RFS Collaborative SLAM: SLAM in Dynamic High-Clutter Environments." IEEE Robotics and Automation Magazine, 21(2), 53-59.
Published in IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2016
A hybrid approach combining probabilistic methods and point set registration for fusing 3D occupancy grid maps in multi-robot mapping.
Recommended citation: Y. Yue, D. Wang, P.G.C.N. Senarathne, D. Moratuwage (2016). "A hybrid probabilistic and point set registration approach for fusion of 3D occupancy grid maps." IEEE SMC.
Published in International Conference on Advanced Robotics (ICAR), Hong Kong, China, 2017
A method for detecting 3D feature points on sparse and non-uniform point clouds for robust feature-based SLAM.
Recommended citation: P. Siritanawan, D. Moratuwage, D. Wang (2017). "3D feature points detection on sparse and non-uniform pointcloud for SLAM." ICAR, Hong Kong, China.
Published in International Conference on Control, Automation and Information Sciences (ICCAIS), Hangzhou, China, 2018
Conference paper introducing the delta-GLMB SLAM framework for simultaneous localisation and mapping.
Recommended citation: D. Moratuwage, M. Adams, F. Inostroza (2018). "Delta-Generalized Labelled Multi-Bernoulli Simultaneous Localization and Mapping." ICCAIS, Hangzhou, China.
Published in Sensors, 2019
This paper presents a delta-GLMB approach to SLAM incorporating optimal kernel-based particle filtering for improved trajectory and map estimation.
Recommended citation: D. Moratuwage, M. Adams, F. Inostroza (2019). "Delta-Generalized Labelled Multi-Bernoulli Simultaneous Localization and Mapping with the Optimal Kernel based Particle Filtering Approach." Sensors, 19(10).
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Published in Advanced Maui Optical and Space Surveillance Technologies (AMOS) Conference, Maui, Hawaii, 2019
Application of a Jump Markov System based delta-GLMB filter to space object tracking for space situational awareness.
Recommended citation: D. Moratuwage, M. Adams, L. Cament (2019). "Space Object Tracking using a Jump Markov System based Delta-GLMB filter for Space Situational Awareness." AMOS Conference, Maui, Hawaii.
Published in IEEE Transactions on Signal Processing, 2022
This paper addresses multi-object state estimation using multiple sensors and multiple scans, leveraging the labeled multi-Bernoulli framework.
Recommended citation: D. Moratuwage, B.N. Vo, B.T. Vo and C. Shim (2022). "Multi-Scan Multi-Sensor Multi-Object State Estimation." IEEE Transactions on Signal Processing, 70, 5429-5442.
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Published in IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2022
This paper presents an audio-visual approach to online multi-source separation, combining visual and audio cues for improved performance.
Recommended citation: J. Ong, B.T. Vo, S. Nordholm, B.N. Vo, D. Moratuwage and C. Shim (2022). "Audio-Visual Based Online Multi-Source Separation." IEEE/ACM Transactions on Audio, Speech, and Language Processing, 30, 1219-1234.
Published in Elsevier Signal Processing, 2022
This paper proposes an adaptive GLMB filter for multi-object tracking, improving adaptability across varying clutter and detection scenarios.
Recommended citation: C.T. Do, T.T.D. Nguyen, D. Moratuwage, C. Shim and Y.D. Chung (2022). "Multi-Object Tracking with an adaptive generalized labelled multi-Bernoulli filter." Elsevier Signal Processing, 196.
Published in Conference on Control, Automation and Information Sciences (ICCAIS), Hanoi, Vietnam, 2022
A moving window approach to multi-scan multi-target tracking for improved computational efficiency in real-time applications.
Recommended citation: D. Moratuwage, C. Shim, and Y. Punchihewa (2022). "A Moving Window Based Approach to Multi-scan Multi-target Tracking." ICCAIS, Hanoi, Vietnam.
Published in Conference on Control, Automation and Information Sciences (ICCAIS), Hanoi, Vietnam, 2022
A computationally efficient implementation of the multi-sensor GLMB filter for scalable multi-object tracking.
Recommended citation: D. Moratuwage, Y. Punchihewa, J.Y. Lee (2022). "A Faster Implementation of Multi-sensor Generalized Labeled Multi-Bernoulli Filter." ICCAIS, Hanoi, Vietnam.
Published in Conference on Control, Automation and Information Sciences (ICCAIS), Hanoi, Vietnam, 2022
A generalized label grouping strategy to improve scalability of trajectory estimation in multi-object tracking.
Recommended citation: C. Shim, J.Y. Lee, D. Moratuwage, D.Y. Kim, Y.D. Chung (2022). "Generalized Label Grouping for Scalable Trajectory Estimation." ICCAIS, Hanoi, Vietnam.
Published in IEEE Transactions on Signal Processing, 2023
This paper presents a linear complexity Gibbs sampling approach for GLMB filtering, significantly reducing the computational burden of multi-object state estimation.
Recommended citation: C. Shim, B.T. Vo, B.N. Vo, J. Ong, and D. Moratuwage (2023). "Linear Complexity Gibbs Sampling for Generalized Labeled Multi-Bernoulli Filtering." IEEE Transactions on Signal Processing, 71, 1981-1994.
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Undergraduate course, Central Queensland University, School of Engineering and Technology, 2024
Unit Coordinator for Industrial Control and Automation (ENEX13001) at CQUniversity. (HE Term 2, 2024)
Undergraduate course, Central Queensland University, School of Engineering and Technology, 2025
Unit Coordinator for Embedded System Design (ENEX20001) at CQUniversity. (HE Term 1, 2025)
Undergraduate course, Central Queensland University, School of Engineering and Technology, 2025
Unit Coordinator for Embedded System Design (ENEX20001) at CQUniversity. (HE Term 2, 2025)
Undergraduate course, Central Queensland University, School of Engineering and Technology, 2025
Unit Coordinator for Industrial Control and Automation (ENEX13001) at CQUniversity. (HE Term 2, 2025)
Undergraduate course, Central Queensland University, School of Engineering and Technology, 2026
Unit Coordinator, Lecturer, Tutor, and Campus Marker for Mechatronics Systems Design (ENEX14001) at CQUniversity. (HE Term 1, 2026)