  Cooperative autonomous approaches to avoiding collisions among small Unmanned
Aerial Vehicles (UAVs) is central to safe integration of UAVs within the
civilian airspace. One potential online cooperative approach is the concept of
reciprocal actions, where both UAVs take pre-trained mutually coherent actions
that do not require active online coordination (thereby avoiding the
computational burden and risk associated with it). This paper presents a
learning based approach to train such reciprocal maneuvers. Neuroevolution,
which uses evolutionary algorithms to simultaneously optimize the topology and
weights of neural networks, is used as the learning method -- which operates
over a set of sample approach scenarios. Unlike most existing work (that
minimize travel distance, energy or risk), the training process here focuses on
the objective of minimizing the required detection range; this has important
practical implications w.r.t. alleviating the dependency on sophisticated
sensing and their reliability under various environments. A specialized design
of experiments and line search is used to identify the minimum detection range
for each sample scenarios. In order to allow an efficient training process, a
classifier is used to discard actions (without simulating them) where the
controller would fail. The model obtained via neuroevolution is observed to
generalize well to (i.e., successful collision avoidance over) unseen approach
scenarios.
