  This paper presents a novel decentralized control strategy for a multi-robot
system that enables parallel multi-target exploration while ensuring a
time-varying connected topology in cluttered 3D environments. Flexible
continuous connectivity is guaranteed by building upon a recent connectivity
maintenance method, in which limited range, line-of-sight visibility, and
collision avoidance are taken into account at the same time. Completeness of
the decentralized multi-target exploration algorithm is guaranteed by
dynamically assigning the robots with different motion behaviors during the
exploration task. One major group is subject to a suitable downscaling of the
main traveling force based on the traveling efficiency of the current leader
and the direction alignment between traveling and connectivity force. This
supports the leader in always reaching its current target and, on a larger time
horizon, that the whole team realizes the overall task in finite time.
Extensive Monte~Carlo simulations with a group of several quadrotor UAVs show
the scalability and effectiveness of the proposed method and experiments
validate its practicability.
