Vision-Based Multirotor Following Using Synthetic Learning Techniques.

ABSTRACT: Deep- and reinforcement-learning techniques have increasingly required large sets of real data to achieve stable convergence and generalization, in the context of image-recognition, object-detection or motion-control strategies. On this subject, the research community lacks robust approaches to overcome unavailable real-world extensive data by means of realistic synthetic-information and domain-adaptation techniques. In this work, synthetic-learning strategies have been used for the vision-based autonomous following of a noncooperative multirotor. The complete maneuver was learned with synthetic images and high-dimensional low-level continuous robot states, with deep- and reinforcement-learning techniques for object detection and motion control, respectively. A novel motion-control strateg

SUBMITTER: Rodriguez-Ramos A 

PROVIDER: S-EPMC6864684 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature