  Different applications, such as environmental monitoring and military
operations, demand the observation of predefined target locations, and an
autonomous mobile robot can assist in these tasks. In this context, the
Orienteering Problem (OP) is a well-known routing problem, in which the goal is
to maximize the objective function by visiting the most rewarding locations,
however, respecting a limited travel budget (e.g., length, time, energy).
However, traditional formulations for routing problems generally neglect some
environment peculiarities, such as obstacles or threatening zones. In this
paper, we tackle the OP considering Dubins vehicles in the presence of a known
deployed sensor field. We propose a novel multi-objective formulation called
Minimal Exposure Dubins Orienteering Problem (MEDOP), whose main objectives
are: (i) maximize the collected reward, and (ii) minimize the exposure of the
agent, i.e., the probability of being detected. The solution is based on an
evolutionary algorithm that iteratively varies the subset and sequence of
locations to be visited, the orientations on each location, and the turning
radius used to determine the paths. Results show that our approach can
efficiently find a diverse set of solutions that simultaneously optimize both
objectives.
