Resumen: This work addresses a new technique of motion planning and navigation for differential-drive robots in dynamic environments. Static and dynamic objects are represented directly on the control space of the robot, where decisions on the best motion are made. A new model representing the dynamism and the prediction of the future behavior of the environment is defined, the dynamic object velocity space (DOVS). A formal definition of this model is provided, establishing the properties for its characterization. An analysis of its complexity, compared with other methods, is performed. The model contains information about the future behavior of obstacles, mapped on the robot control space. It allows planning of near-time-optimal safe motions within the visibility space horizon, not only for the current sampling period. Navigation strategies are developed based on the identification of situations in the model. The planned strategy is applied and updated for each sampling time, adapting to changes occurring in the scenario. The technique is evaluated in randomly generated simulated scenarios, based on metrics defined using safety and time-to-goal criteria. An evaluation in real-world experiments is also presented. Idioma: Inglés DOI: 10.1177/0278364918775520 Año: 2018 Publicado en: International Journal of Robotics Research 37, 8 (2018), 867-889 ISSN: 0278-3649 Factor impacto JCR: 6.134 (2018) Categ. JCR: ROBOTICS rank: 4 / 26 = 0.154 (2018) - Q1 - T1 Factor impacto SCIMAGO: 2.189 - Applied Mathematics (Q1) - Artificial Intelligence (Q1) - Software (Q1) - Mechanical Engineering (Q1) - Modeling and Simulation (Q1) - Electrical and Electronic Engineering (Q1)