Multi-agent active multi-target search with intermittent measurements

Abstract

Consider a multi-agent system that must find an unknown number of static targets at unknown locations as quickly as possible. To estimate the number and positions of targets from noisy and sometimes missing measurements, we use a customized particle-based probability hypothesis density filter. Novel methods are introduced that select waypoints for the agents in a decoupled manner from taking measurements, which allows optimizing over waypoints arbitrarily far in the environment while taking as many measurements as necessary along the way. Optimization involves control cost, target refinement, and exploration of the environment. Measurements are taken either periodically, or only when they are expected to improve target detection, in an event-triggered manner. All this is done in 2D and 3D environments, for a single agent as well as for multiple homogeneous or heterogeneous agents, leading to a comprehensive framework for (Multi-Agent) Active target Search with Intermittent measurements – (MA)ASI. In simulations and real-life experiments involving a Parrot Mambo drone and a TurtleBot3 ground robot, the novel framework works better than baselines including lawnmowers, mutual-information-based methods, active search methods, and our earlier exploration-based techniques.