Zobrazeno 1 - 10
of 344
pro vyhledávání: '"Ren, Xiaoqiang"'
In this paper, we investigate the problem of estimating the 4-DOF (three-dimensional position and orientation) robot-robot relative frame transformation using odometers and distance measurements between robots. Firstly, we apply a two-step estimation
Externí odkaz:
http://arxiv.org/abs/2405.12577
This paper studies distributed nonconvex optimization problems with stochastic gradients for a multi-agent system, in which each agent aims to minimize the sum of all agents' cost functions by using local compressed information exchange. We propose a
Externí odkaz:
http://arxiv.org/abs/2403.01322
Deep neural networks (DNN) are increasingly being used to learn controllers due to their excellent approximation capabilities. However, their black-box nature poses significant challenges to closed-loop stability guarantees and performance analysis.
Externí odkaz:
http://arxiv.org/abs/2403.00381
Autor:
Li, Xinghan, Li, Haoying, Zeng, Guangyang, Zeng, Qingcheng, Ren, Xiaoqiang, Yang, Chao, Wu, Junfeng
A filter for inertial-based odometry is a recursive method used to estimate the pose from measurements of ego-motion and relative pose. Currently, there is no known filter that guarantees the computation of a globally optimal solution for the non-lin
Externí odkaz:
http://arxiv.org/abs/2402.05003
This paper studies the privacy-preserving distributed optimization problem under limited communication, where each agent aims to keep its cost function private while minimizing the sum of all agents' cost functions. To this end, we propose two differ
Externí odkaz:
http://arxiv.org/abs/2307.16656
This paper addresses the problem of differentially private distributed optimization under limited communication, where each agent aims to keep their cost function private while minimizing the sum of all agents' cost functions. In response, we propose
Externí odkaz:
http://arxiv.org/abs/2304.01779
Age-of-Information (AoI) is a critical metric for network applications. Existing works mostly address optimization with homogeneous AoI requirements, which is different from practice. In this work, we optimize uplink scheduling for an access point (A
Externí odkaz:
http://arxiv.org/abs/2212.06633
Both goal-agnostic and goal-oriented tasks have practical value for robotic grasping: goal-agnostic tasks target all objects in the workspace, while goal-oriented tasks aim at grasping pre-assigned goal objects. However, most current grasping methods
Externí odkaz:
http://arxiv.org/abs/2212.01763
Autor:
Jiang, Haodong, Wang, Wentao, Shen, Yuan, Li, Xinghan, Ren, Xiaoqiang, Mu, Biqiang, Wu, Junfeng
State estimation is an essential part of autonomous systems. Integrating the Ultra-Wideband(UWB) technique has been shown to correct the long-term estimation drift and bypass the complexity of loop closure detection. However, few works on robotics ad
Externí odkaz:
http://arxiv.org/abs/2209.06779
Deep reinforcement learning (DRL) has a great potential for solving complex decision-making problems in autonomous driving, especially in mixed-traffic scenarios where autonomous vehicles and human-driven vehicles (HDVs) drive together. Safety is a k
Externí odkaz:
http://arxiv.org/abs/2204.01024