Deep Local Trajectory Replanning and Control for Robot Navigation
| Autor: | Roberto Martín-Martín, Silvio Savarese, Patrick Goebel, Junwei Yang, Hans M. Ewald, Marynel Vázquez, Amir Sadeghian, Dorsa Sadigh, Vincent Chow, Ashwini Pokle, Zhenkai Wang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Structure (mathematical logic)
FOS: Computer and information sciences 0209 industrial biotechnology Computer science Computer Science - Artificial Intelligence Real-time computing Control (management) Navigation system 020207 software engineering 02 engineering and technology Plan (drawing) Motion (physics) Computer Science - Robotics 020901 industrial engineering & automation Artificial Intelligence (cs.AI) 0202 electrical engineering electronic engineering information engineering Trajectory Robot Robotics (cs.RO) |
| Popis: | We present a navigation system that combines ideas from hierarchical planning and machine learning. The system uses a traditional global planner to compute optimal paths towards a goal, and a deep local trajectory planner and velocity controller to compute motion commands. The latter components of the system adjust the behavior of the robot through attention mechanisms such that it moves towards the goal, avoids obstacles, and respects the space of nearby pedestrians. Both the structure of the proposed deep models and the use of attention mechanisms make the system's execution interpretable. Our simulation experiments suggest that the proposed architecture outperforms baselines that try to map global plan information and sensor data directly to velocity commands. In comparison to a hand-designed traditional navigation system, the proposed approach showed more consistent performance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|