Robots are often employed in hazardous or inaccessible environments, such as disaster sites, extraterrestrial terrains, agricultural fields, and ocean floors. Autonomous operation is crucial in these scenarios to reduce reliance on human operators and enable real-time decision-making. However, robots must balance multiple, often conflicting, objectives. These objectives are subject to change based on new data or evolving conditions. This paper presents a novel approach to dynamic multi-objective trajectory planning. The proposed method leverages the boundary intersection decomposition technique to adaptively plan trajectories that balance multiple evolving objectives. Our approach ensures efficient and effective exploration by continuously optimizing the trade-offs between changing objectives. We show that our method performs on average 34 % better in terms of solution quality on the dynamic multi-objective trajectory planning problem as compared to prior work.

Robotic explorers are essential tools for gathering information about regions that are inaccessible to humans. For applications like planetary exploration or search and rescue, robots use prior knowledge about the area to guide their search. Ergodic search methods find trajectories that effectively balance exploring unknown regions and exploiting prior information. In many search based problems, the robot must take into account multiple factors such as scientific information gain, risk, and energy, and update its belief about these dynamic objectives as they evolve over time. However, existing ergodic search methods either consider multiple static objectives or consider a single dynamic objective, but not multiple dynamic objectives. We address this gap in existing methods by presenting an algorithm called Dynamic Multi-Objective Ergodic Search (D-MO-ES) that efficiently plans an ergodic trajectory on multiple changing objectives. Our experiments show that our method requires up to nine times less compute time than a naïve approach with comparable coverage of each objective.