This research investigates near-wall turbulence, the chaotic fluid motion responsible for much of aerodynamic drag in transportation systems. Using high-resolution computational simulations and predictive modelling, the work aims to better understand turbulence near surfaces, enabling more efficient aerospace designs, reduced fuel consumption, and potentially major reductions in greenhouse gas emissions.

This thesis develops autonomous robotic systems for data gathering in complex real-world environments. It advances three areas: informative path planning, disturbance-aware planning, and predictive world modelling. The research introduces adaptive sampling-based planners for long-horizon information gathering, methods for estimating wind fields and planning safe, energy-aware UAV trajectories, and predictive mapping systems for indoor exploration. It also compares robot exploration strategies with human decision-making and uses those insights to build a reinforcement learning planner that performs at near-human level. Overall, the work shows that integrating long-horizon reasoning, environmental dynamics, and richer belief models can make autonomous robotic information gathering more effective, reliable, and scalable.