Long-Horizon Active SLAM system for multi-agent coordinated exploration

Marie Ossenkopf¹, Gastón Castro², Facundo Pessacg², Kurt Geihs¹, and Pablo De Cristóforis^2
1Distributed Systems Group, University of Kassel, Germany
²University of Buenos Aires (UBA-CONICET-ICC), Argentina

Exploring efficiently an unknown environment with several autonomous agents is a challenging task. In this work we propose an multi-agent Active SLAM method that is able to evaluate a long planning horizon of actions and perform exploration while maintaining estimation uncertainties bounded. Candidate actions are generated using a variant of the Rapidly exploring Random Tree approach (RRT*) followed by a joint entropy minimization to select a path. Entropy estimation is performed in two stages, a short horizon evaluation is carried using exhaustive filter updates while entropy in long horizons is approximated considering reductions on predicted loop closures between robot trajectories. We pursue a fully decentralized exploration approach to cope with typical uncertainties in multi-agent coordination. We performed simulations for decentralized exploration planning, which is both dynamically adapting to new situations as well as concerning long horizon plans.