  This paper presents a decentralized and asynchronous systematic solution for
multi-robot autonomous navigation in unknown obstacle-rich scenes using merely
onboard resources. The planning system is formulated under gradient-based local
planning framework, where collision avoidance is achieved by formulating the
collision risk as a penalty of a nonlinear optimization problem. In order to
improve robustness and escape local minima, we incorporate a lightweight
topological trajectory generation method. Then agents generate safe, smooth,
and dynamically feasible trajectories in only several milliseconds using an
unreliable trajectory sharing network. Relative localization drift among agents
is corrected by using agent detection in depth images. Our method is
demonstrated in both simulation and real-world experiments. The source code is
released for the reference of the community.
