Current methods of deploying robots that operate in dynamic, uncertain environments, such as Uncrewed Aerial Systems in search & rescue missions, require nearly continuous human supervision for vehicle guidance and operation. These methods do not consider high-level mission context resulting in cumbersome manual operation or inefficient exhaustive search patterns. We present a human-centered autonomous frame-work that infers geospatial mission context through dynamic feature sets, which then guides a probabilistic target search planner. Operators provide a set of diverse inputs, including priority definition, spatial semantic information about ad-hoc geographical areas, and reference waypoints, which are probabilistically fused with geographical database information and condensed into a geospatial distribution representing an operator’s preferences over an area. An online, POMDP-based planner, optimized for target searching, is augmented with this reward map to generate an operator-constrained policy. Our results, simulated based on input from five professional rescuers, display effective task mental model alignment, 18% more victim finds, and 15 times more efficient guidance plans then current operational methods.

The ability of autonomous robots to model, communicate, and act on semantic “soft data” remains challenging. The human-assisted robotic planning and sensing (HARPS) framework is presented for active semantic sensing and planning in human–robot teams to address these gaps by formally combining the benefits of online sampling-based partially observable Markov decision process policies, multimodal human–robot interaction, and Bayesian data fusion. HARPS lets humans impose model structure and extend the range of soft data by sketching and labeling new semantic features in uncertain environments. Dynamic model updating lets robotic agents actively query humans for novel and relevant semantic data, thereby improving model and state beliefs for improved online planning. Simulations of a unmanned aerial vehicle-enabled target search in a large-scale partially structured environment show significant improvements in time and beliefs required for interception versus conventional planning with robot-only sensing. A human subject study in the same environment shows an average doubling in dynamic target capture rate compared to the lone robot case and highlights the robustness of HARPS over a range of user characteristics and interaction modalities.