148 research outputs found
Optimization-Based Motion Planning for Autonomous Parking Considering Dynamic Obstacle: A Hierarchical Framework
We present a hierarchical framework based on graph search and model
predictive control (MPC) for electric autonomous vehicle (EAV) parking
maneuvers in a tight environment. At high-level, only static obstacles are
considered, and the scenario-based hybrid A* (SHA*), which is faster than the
traditional hybrid A*, is designed to provide an initial guess (also known as a
global path) for the parking task. To extract the velocity and acceleration
profile from an initial guess, an optimal control problem (OCP) is built. At
the low level, an NMPC-based strategy is used to avoid dynamic obstacles (also
known as local planning). The efficacy of SHA* is evaluated through 148
different simulation schemes and the proposed hierarchical parking framework is
demonstrated through a real-time parallel parking simulation
Deep Domain Adversarial Adaptation for Photon-efficient Imaging
Photon-efficient imaging with the single-photon light detection and ranging
(LiDAR) captures the three-dimensional (3D) structure of a scene by only a few
detected signal photons per pixel. However, the existing computational methods
for photon-efficient imaging are pre-tuned on a restricted scenario or trained
on simulated datasets. When applied to realistic scenarios whose
signal-to-background ratios (SBR) and other hardware-specific properties differ
from those of the original task, the model performance often significantly
deteriorates. In this paper, we present a domain adversarial adaptation design
to alleviate this domain shift problem by exploiting unlabeled real-world data,
with significant resource savings. This method demonstrates superior
performance on simulated and real-world experiments using our home-built
up-conversion single-photon imaging system, which provides an efficient
approach to bypass the lack of ground-truth depth information in implementing
computational imaging algorithms for realistic applications
Optimal estimation and control for lossy network: stability, convergence, and performance
In this paper, we study the problems of optimal estimation and control, i.e., the linear quadratic Gaussian (LQG) control, for systems with packet losses but without acknowledgment. Such acknowledgment is a signal sent by the actuator to inform the estimator of the incidence of control packet losses. For such system, which is usually called as a user datagram protocol (UDP)-like system, the optimal estimation is nonlinear and its calculation is time-consuming, making its corresponding optimal LQG problem complicated. We first propose two conditions: 1) the sensor has some computation abilities; and 2) the control command, exerted to the plant, is known to the sensor. For a UDP-like system satisfying these two conditions, we derive the optimal estimation. By constructing the finite and infinite product probability measure spaces for the estimation error covariances (EEC), we give the stability condition for the expected EEC, and show the existence of a measurable function to which the EEC converges in distribution, and propose some practical methods to evaluate the estimation performance. Finally, the LQG controllers are derived, and the conditions for the mean square stability of the closed-loop system are established
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