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Autonomous navigation in dynamic environments is a complex but essential taskfor autonomous robots. Recent deep reinforcement learning approaches showpromising results to solve the problem, but it is not solved yet, as theytypically assume no robot kinodynamic restrictions, holonomic movement orperfect environment knowledge. Moreover, most algorithms fail in the real worlddue to the inability to generate real-world training data for the hugevariability of possible scenarios. In this work, we present a novel planner,DQN-DOVS, that uses deep reinforcement learning on a descriptive robocentricvelocity space model to navigate in highly dynamic environments. It is trainedusing a smart curriculum learning approach on a simulator that faithfullyreproduces the real world, reducing the gap between the reality and simulation.We test the resulting algorithm in scenarios with different number of obstaclesand compare it with many state-of-the-art approaches, obtaining a betterperformance. Finally, we try the algorithm in a ground robot, using the samesetup as in the simulation experiments.

Deep reinforcement learning oriented for real world dynamic scenarios