Zobrazeno 11 - 20
of 7 485
pro vyhledávání: '"Hsieh, M."'
Any strategy used to distribute a robot ensemble over a set of sequential tasks is subject to inaccuracy due to robot-level uncertainties and environmental influences on the robots' behavior. We approach the problem of inaccuracy during task allocati
Externí odkaz:
http://arxiv.org/abs/2212.09816
This paper focuses on the broadcast of information on robot networks with stochastic network interconnection topologies. Problematic communication networks are almost unavoidable in areas where we wish to deploy multi-robotic systems, usually due to
Externí odkaz:
http://arxiv.org/abs/2212.09808
Transfer operators offer linear representations and global, physically meaningful features of nonlinear dynamical systems. Discovering transfer operators, such as the Koopman operator, require careful crafted dictionaries of observables, acting on st
Externí odkaz:
http://arxiv.org/abs/2212.01503
Nonlinear model predictive control (MPC) is a flexible and increasingly popular framework used to synthesize feedback control strategies that can satisfy both state and control input constraints. In this framework, an optimization problem, subjected
Externí odkaz:
http://arxiv.org/abs/2211.13829
Autor:
Chee, Kong Yao, Hsieh, M. Ani
State estimation is an important aspect in many robotics applications. In this work, we consider the task of obtaining accurate state estimates for robotic systems by enhancing the dynamics model used in state estimation algorithms. Existing framewor
Externí odkaz:
http://arxiv.org/abs/2209.08185
In this paper, we present an online adaptive planning strategy for a team of robots with heterogeneous sensors to sample from a latent spatial field using a learned model for decision making. Current robotic sampling methods seek to gather informatio
Externí odkaz:
http://arxiv.org/abs/2208.06053
In this work, we consider the task of improving the accuracy of dynamic models for model predictive control (MPC) in an online setting. Although prediction models can be learned and applied to model-based controllers, these models are often learned o
Externí odkaz:
http://arxiv.org/abs/2207.09344
The multi-robot adaptive sampling problem aims at finding trajectories for a team of robots to efficiently sample the phenomenon of interest within a given endurance budget of the robots. In this paper, we propose a robust and scalable approach using
Externí odkaz:
http://arxiv.org/abs/2207.07751
We present a flow-based control strategy that enables resource-constrained marine robots to patrol gyre-like flow environments on an orbital trajectory with a periodicity in a given range. The controller does not require a detailed model of the flow
Externí odkaz:
http://arxiv.org/abs/2203.00796
Autor:
Kakarla, D. Chandrasekhar, Ku, Yuan-Han, Wu, H.C., Chen, C.C., Hsu, M.Y., Hu, T.R., Lin, J.-Y., Puri, Nidhi, Hsieh, M.-J., Wang, C.W., Li, W.-H., C, Dhanasekhar, Tiwari, A., Lu, C.H., You, K.J., Kuo, T.W., Fan, K.J., Chang, Y.C., Yang, H.D.
Publikováno v:
In Materials Today Physics August 2024 46