Daniel Angley

RMIT University, University of Melbourne

Papers

Total Citations

H-Index

About

Daniel Angley’s research lies at the intersection of autonomous robotics, probabilistic estimation, and multi-agent systems, with a particular focus on solving real-world sensing and navigation challenges. His most impactful work, “Autonomous Multi-Robot Search for a Hazardous Source in a Turbulent Environment” (43 citations), introduces a cognitive infotaxis algorithm that enables teams of robots to efficiently locate toxic atmospheric releases—a critical capability for national security and disaster response. In “A random finite set approach to occupancy-grid SLAM” (6 citations), Angley advanced simultaneous localisation and mapping under severe sensor uncertainty, developing a robust framework that handles false and missed detections from low-cost sonar or radar. His work on “Dynamic Target Driven Trajectory Planning using RRT” (5 citations) addresses the practical challenge of autonomous underwater vehicle recovery, planning trajectories for AUVs returning to moving vessels using only passive angle-only sensors. Across these contributions, Angley demonstrates a consistent ability to bridge theoretical Bayesian methods with deployable robotic systems, producing algorithms that function reliably in the unpredictable, sensor-limited conditions of real-world environments. His research is particularly notable for its direct applicability to safety-critical autonomous operations.