Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Manjanna, Sandeep"'
In multi-robot informative path planning the problem is to find a route for each robot in a team to visit a set of locations that can provide the most useful data to reconstruct an unknown scalar field. In the budgeted version, each robot is subject
Externí odkaz:
http://arxiv.org/abs/2407.18545
Autor:
Mateen, Syed Abdul, Malvia, Niharika, Khader, Syed Abdul, Wang, Danny, Srinivasan, Deepti, Yang, Chi-Fu Jeffrey, Schumacher, Lana, Manjanna, Sandeep
Publikováno v:
ICRA-RAMI Workshop, May 2024, Japan
This paper presents an approach for surgical phase recognition using video data, aiming to provide a comprehensive understanding of surgical procedures for automated workflow analysis. The advent of robotic surgery, digitized operating rooms, and the
Externí odkaz:
http://arxiv.org/abs/2406.09185
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
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
Active target sensing is the task of discovering and classifying an unknown number of targets in an environment and is critical in search-and-rescue missions. This paper develops a deep reinforcement learning approach to plan informative trajectories
Externí odkaz:
http://arxiv.org/abs/2212.08214
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
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
Autor:
Huang, Yuying, Yao, Yiming, Hansen, Johanna, Mallette, Jeremy, Manjanna, Sandeep, Dudek, Gregory, Meger, David
This paper presents the portable autonomous probing system (APS), a low-cost robotic design for collecting water quality measurements at targeted depths from an autonomous surface vehicle (ASV). This system fills an important but often overlooked nic
Externí odkaz:
http://arxiv.org/abs/2110.14738
This paper presents a distributed scalable multi-robot planning algorithm for informed sampling of quasistatic spatial fields. We address the problem of efficient data collection using multiple autonomous vehicles and consider the effects of communic
Externí odkaz:
http://arxiv.org/abs/2105.10018