Zobrazeno 1 - 10
of 7 454
pro vyhledávání: '"Hsieh, M."'
Autor:
Cladera, Fernando, Miller, Ian D., Ravichandran, Zachary, Murali, Varun, Hughes, Jason, Hsieh, M. Ani, Taylor, C. J., Kumar, Vijay
One common and desirable application of robots is exploring potentially hazardous and unstructured environments. Air-ground collaboration offers a synergistic approach to addressing such exploration challenges. In this paper, we demonstrate a system
Externí odkaz:
http://arxiv.org/abs/2405.07169
Autor:
Li, Alice K., Mao, Yue, Manjanna, Sandeep, Liu, Sixuan, Dhanoa, Jasleen, Mehta, Bharg, Edwards, Victoria M., Ojeda, Fernando Cladera, Men, Maël Le, Sigg, Eric, Ulloa, Hugo N., Jerolmack, Douglas J., Hsieh, M. Ani
Climate change has increased the frequency and severity of extreme weather events such as hurricanes and winter storms. The complex interplay of floods with tides, runoff, and sediment creates additional hazards -- including erosion and the undermini
Externí odkaz:
http://arxiv.org/abs/2312.14248
Autor:
Cladera, Fernando, Ravichandran, Zachary, Miller, Ian D., Hsieh, M. Ani, Taylor, C. J., Kumar, Vijay
Multi-robot collaboration in large-scale environments with limited-sized teams and without external infrastructure is challenging, since the software framework required to support complex tasks must be robust to unreliable and intermittent communicat
Externí odkaz:
http://arxiv.org/abs/2309.15975
Planning time-optimal trajectories for quadrotors in cluttered environments is a challenging, non-convex problem. This paper addresses minimizing the traversal time of a given collision-free geometric path without violating bounds on individual motor
Externí odkaz:
http://arxiv.org/abs/2309.11637
With the increase in data availability, it has been widely demonstrated that neural networks (NN) can capture complex system dynamics precisely in a data-driven manner. However, the architectural complexity and nonlinearity of the NNs make it challen
Externí odkaz:
http://arxiv.org/abs/2308.08086
The recent increase in data availability and reliability has led to a surge in the development of learning-based model predictive control (MPC) frameworks for robot systems. Despite attaining substantial performance improvements over their non-learni
Externí odkaz:
http://arxiv.org/abs/2308.00570
Autor:
Wang, Ziyun, Ojeda, Fernando Cladera, Bisulco, Anthony, Lee, Daewon, Taylor, Camillo J., Daniilidis, Kostas, Hsieh, M. Ani, Lee, Daniel D., Isler, Volkan
Event-based sensors have recently drawn increasing interest in robotic perception due to their lower latency, higher dynamic range, and lower bandwidth requirements compared to standard CMOS-based imagers. These properties make them ideal tools for r
Externí odkaz:
http://arxiv.org/abs/2304.07200
Persistent monitoring of a spatiotemporal fluid process requires data sampling and predictive modeling of the process being monitored. In this paper we present PASST algorithm: Predictive-model based Adaptive Sampling of a Spatio-Temporal process. PA
Externí odkaz:
http://arxiv.org/abs/2304.00732
We seek methods to model, control, and analyze robot teams performing environmental monitoring tasks. During environmental monitoring, the goal is to have teams of robots collect various data throughout a fixed region for extended periods of time. St
Externí odkaz:
http://arxiv.org/abs/2212.11447