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In this work, our goal is to extend the existing search and rescue paradigm by allowing teams of autonomous unmanned aerial vehicles (UAVs) to collaborate effectively with human searchers on the ground. We derive a framework that includes a simulated lost person behavior model, as well as a human searcher behavior model that is informed by data collected from past search tasks. These models are used together to create a probabilistic heatmap of the lost persons position and anticipated searcher trajectories. We then use Gaussian processes with a Gibbs kernel to accurately model a limited field-of-view (FOV) sensor, e.g., thermal cameras, from which we derive a risk metric that drives UAV path optimization. Our framework finally computes a set of search paths for a team of UAVs to autonomously complement human searchers efforts.
Autonomous or teleoperated robots have been playing increasingly important roles in civil applications in recent years. Across the different civil domains where robots can support human operators, one of the areas where they can have more impact is i
Sampling-based algorithms solve the path planning problem by generating random samples in the search-space and incrementally growing a connectivity graph or a tree. Conventionally, the sampling strategy used in these algorithms is biased towards expl
Regular irradiation of indoor environments with ultraviolet C (UVC) light has become a regular task for many indoor settings as a result of COVID-19, but current robotic systems attempting to automate it suffer from high costs and inefficient irradia
This paper presents a human-robot trust integrated task allocation and motion planning framework for multi-robot systems (MRS) in performing a set of tasks concurrently. A set of task specifications in parallel are conjuncted with MRS to synthesize a
For large-scale tasks, coverage path planning (CPP) can benefit greatly from multiple robots. In this paper, we present an efficient algorithm MSTC* for multi-robot coverage path planning (mCPP) based on spiral spanning tree coverage (Spiral-STC). Ou