This research develops scalable motion-planning algorithms that enable large teams of robots to work together safely and efficiently. By combining machine learning with search algorithms, the work delivers both speed and reliability, supporting applications from automated warehouses to disaster response, infrastructure repair, and future space exploration.

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.