  Robots in a swarm take advantage of a motion capture system or GPS sensors to
obtain their global position. However, motion capture systems are
environment-dependent and GPS sensors are not reliable in occluded
environments. For a reliable and versatile operation in a swarm, robots must
sense each other and interact locally. Motivated by this requirement, here we
propose an on-board localization framework for multi-robot systems. Our
framework consists of an anchor robot with three ultrawideband (UWB) sensors
and a tag robot with a single UWB sensor. The anchor robot utilizes the three
UWB sensors as a localization infrastructure and estimates the tag robot's
location by using its on-board sensing and computational capabilities solely,
without explicit inter-robot communication. We utilize a dual Monte-Carlo
localization approach to capture the agile maneuvers of the tag robot with an
acceptable precision. We validate the effectiveness of our algorithm with
simulations and indoor and outdoor experiments on a two-drone setup. The
proposed dual MCL algorithm yields highly accurate estimates for various speed
profiles of the tag robot and demonstrates a superior performance over the
standard particle filter and the extended Kalman Filter.
