Situational awareness and state estimation tools for search and localize missions with stationary targets in formation

Abstract

Multi-agent hybrid dynamical systems are a natural model for collaborative missions in which several steps and behaviors are required to achieve the goal of the mission. Missions are tasks featuring interacting subtasks, such as the decision of where to search, how to search, and when to transition from a search behavior to a rescue behavior. While the discrete nature of mission actions (which subtask to accomplish) and the continuous nature of realworld physical state spaces make hybrid systems a good model, control in such systems is poorly understood. Theoretical results on state reachability rely on restrictive assumptions which hinder formal verification and optimization of such systems. Despite this, we find the formalism to have significant value and develop hierarchical state estimation tools to control agents in a hybrid framework and execute missions. In past work, we developed hierarchical dynamic target modeling to estimate the progress of search and track scenarios with moving targets. In this work, we consider the related problem of searching for stationary targets that appear in formation. While this may seem easier than searching for moving targets (e.g. because a preplanned search is guaranteed to find all targets), executing the search efficiently and gaining situational awareness while doing so presents unique challenges. We develop a generative hierarchical model for target locations that relies on stochastic clustering techniques and ideas from object Simultaneous Location and Mapping (SLAM) to address these challenges and demonstrate their efficacy in single-and multi-agent scenarios.