Multirobot Field Exploration in HexDecomposed Environments for Dubins Vehicles Multirobot systems can be useful in applications such as map building, surveillance, and search and rescue. To be efficient in practice, the robotic team should cooperatively explore a region of interest. However, when the environment is unknown, it is challenging to plan collisionfree paths in realtime under both nonholonomic mobility constraints and energy constraints. We propose the Multirobot Hex Decomposition Exploration (MHDE) method for multiple Dubins vehicles to explore unknown environments. MHDE ensures robots return to the initial (departure) position before energy runs out. Furthermore, when available energy level allow, MHDE can achieve complete exploration of the environment. The proposed approach generates smooth, continuous paths for Dubins vehicles to follow at constant velocities, and offers geometric closedform solutions for both team formation and paths. The performance is evaluated with a team of Turtlebots in simulated environments with obstacles in Gazebo.

Ignorance is Not Bliss: An Analysis of CentralPlace Foraging Algorithms Centralplace foraging (CPF) is a canonical task in collective robotics with applications to planetary exploration, automated mining, warehousing, and search and rescue operations. We compare the performance of three CentralPlace Foraging Algorithms (CPFAs), variants of which have been shown to work well in real robots: spiralbased, rotatingspoke, and randomballistic. To understand the difference in performance between these CPFAs, we define the price of ignorance and show how this metric explains our previously published empirical results. We obtain upperbounds for expected complete collection times for each algorithm and evaluate their performance in simulation. We show that sitefidelity (i.e. returning to the location of the last found target) and avoiding search redundancy are keyfactors that determine the efficiency of CPFAs. Our formal analysis suggests the following efficiency ranking from best to worst: spiral, spoke, and the stochastic ballistic algorithm.