  In this paper, we study the application of DRL algorithms in the context of
local navigation problems, in which a robot moves towards a goal location in
unknown and cluttered workspaces equipped only with limited-range exteroceptive
sensors, such as LiDAR. Collision avoidance policies based on DRL present some
advantages, but they are quite susceptible to local minima, once their capacity
to learn suitable actions is limited to the sensor range. Since most robots
perform tasks in unstructured environments, it is of great interest to seek
generalized local navigation policies capable of avoiding local minima,
especially in untrained scenarios. To do so, we propose a novel reward function
that incorporates map information gained in the training stage, increasing the
agent's capacity to deliberate about the best course of action. Also, we use
the SAC algorithm for training our ANN, which shows to be more effective than
others in the state-of-the-art literature. A set of sim-to-sim and sim-to-real
experiments illustrate that our proposed reward combined with the SAC
outperforms the compared methods in terms of local minima and collision
avoidance.
