Map-Predictive Motion Planning in Unknown Environments
| Autor: | Amine Elhafsi, Lucas Janson, Boris Ivanovic, Marco Pavone |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Computer science Context (language use) 010103 numerical & computational mathematics 02 engineering and technology Systems and Control (eess.SY) Machine learning computer.software_genre 01 natural sciences Electrical Engineering and Systems Science - Systems and Control Computer Science::Robotics Computer Science - Robotics 020901 industrial engineering & automation FOS: Electrical engineering electronic engineering information engineering Motion planning 0101 mathematics Selection (genetic algorithm) Heuristic business.industry Trajectory Robot Artificial intelligence Heuristics business computer Robotics (cs.RO) |
| Zdroj: | ICRA |
| Popis: | Algorithms for motion planning in unknown environments are generally limited in their ability to reason about the structure of the unobserved environment. As such, current methods generally navigate unknown environments by relying on heuristic methods to choose intermediate objectives along frontiers. We present a unified method that combines map prediction and motion planning for safe, time-efficient au-tonomous navigation of unknown environments by dynamically-constrained robots. We propose a data-driven method for predicting the map of the unobserved environment, using the robot’s observations of its surroundings as context. These map predictions are then used to plan trajectories from the robot’s position to the goal without requiring frontier selection. We applied this map-predictive motion planning strategy to randomly generated winding hallway environments, yielding substantial improvement in trajectory duration over a naive frontier pursuit method. We also experimentally demonstrate similar performance to methods using more sophisticated fron-tier selection heuristics while significantly reducing computation time. |
| Databáze: | OpenAIRE |
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