Robotic explorers play a crucial role in acquiring data from areas that are difficult or impossible for humans to reach. Whether for planetary exploration, search and rescue missions, agriculture, or other scientific exploration tasks, these robots can utilize pre-existing knowledge of the terrain to navigate effectively. In these scenarios, robots must consider various factors including scientific data acquisition, risk assessment, and energy consumption. Importantly, they must also adapt their strategies as these objectives evolve over time. This thesis addresses the unique challenge of autonomously planning paths for time- and information-evolving objectives.

We present planning methods that efficiently generate paths that honor a desired trade-off among objectives even in changing conditions. We introduce two approaches based on ergodic search, one leveraging prior data to predict how to best balance objectives, and another employing decomposition-based optimization to maintain a consistent trade-off even as objectives change. We further extend these ideas to graph search, developing methods that adapt traditional algorithms like A* and Monte Carlo Tree Search for dynamic multi-objective planning.

Our methods are validated using terrestrial and lunar terrain data, demonstrating improved computational efficiency while maintaining solution quality compared to state-of-the-art methods. Additionally, we present local trajectory planning strategies for real-time hazard avoidance, which can be integrated with our global planning methods for robust end‑to‑end autonomy. In all, these contributions advance the efficiency and adaptability of multi-objective planning for planetary and terrestrial exploration robots.