2 research outputs found
Exponential stabilization by delay feedback control for highly nonlinear hybrid stochastic functional differential equations with infinite delay
Given an unstable hybrid stochastic functional differential equation, how to design a delay feedback controller to make it stable? Some results have been obtained for hybrid systems with finite delay. However, the state of many stochastic differential equations are related to the whole history of the system, so it is necessary to discuss the feedback control of stochastic functional differential equations with infinite delay. On the other hand, in many practical stochastic models, the coefficients of these systems do not satisfy the linear growth condition, but are highly nonlinear. In this paper, the delay feedback controls are designed for a class of infinite delay stochastic systems with highly nonlinear and the influence of switching state
Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey
Autonomous systems possess the features of inferring their own state,
understanding their surroundings, and performing autonomous navigation. With
the applications of learning systems, like deep learning and reinforcement
learning, the visual-based self-state estimation, environment perception and
navigation capabilities of autonomous systems have been efficiently addressed,
and many new learning-based algorithms have surfaced with respect to autonomous
visual perception and navigation. In this review, we focus on the applications
of learning-based monocular approaches in ego-motion perception, environment
perception and navigation in autonomous systems, which is different from
previous reviews that discussed traditional methods. First, we delineate the
shortcomings of existing classical visual simultaneous localization and mapping
(vSLAM) solutions, which demonstrate the necessity to integrate deep learning
techniques. Second, we review the visual-based environmental perception and
understanding methods based on deep learning, including deep learning-based
monocular depth estimation, monocular ego-motion prediction, image enhancement,
object detection, semantic segmentation, and their combinations with
traditional vSLAM frameworks. Then, we focus on the visual navigation based on
learning systems, mainly including reinforcement learning and deep
reinforcement learning. Finally, we examine several challenges and promising
directions discussed and concluded in related research of learning systems in
the era of computer science and robotics.Comment: This paper has been accepted by IEEE TNNL