  Autonomous navigation in dynamic environments is a complex but essential task
for autonomous robots, with recent deep reinforcement learning approaches
showing promising results. However, the complexity of the real world makes it
infeasible to train agents in every possible scenario configuration. Moreover,
existing methods typically overlook factors such as robot kinodynamic
constraints, or assume perfect knowledge of the environment. In this work, we
present RUMOR, a novel planner for differential-drive robots that uses deep
reinforcement learning to navigate in highly dynamic environments. Unlike other
end-to-end DRL planners, it uses a descriptive robocentric velocity space model
to extract the dynamic environment information, enhancing training
effectiveness and scenario interpretation. Additionally, we propose an action
space that inherently considers robot kinodynamics and train it in a simulator
that reproduces the real world problematic aspects, reducing the gap between
the reality and simulation. We extensively compare RUMOR with other
state-of-the-art approaches, demonstrating a better performance, and provide a
detailed analysis of the results. Finally, we validate RUMOR's performance in
real-world settings by deploying it on a ground robot. Our experiments,
conducted in crowded scenarios and unseen environments, confirm the algorithm's
robustness and transferability.
