Zobrazeno 1 - 10
of 576
pro vyhledávání: '"Naré, A."'
Autor:
Karapetyan, Nare, Asghar, Ahmad Bilal, Bhaskar, Amisha, Shi, Guangyao, Manocha, Dinesh, Tokekar, Pratap
In this paper, we present an approach for coverage path planning for a team of an energy-constrained Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV). Both the UAV and the UGV have predefined areas that they have to cover. The goal
Externí odkaz:
http://arxiv.org/abs/2310.07621
Decision-Oriented Learning Using Differentiable Submodular Maximization for Multi-Robot Coordination
We present a differentiable, decision-oriented learning framework for cost prediction in a class of multi-robot decision-making problems, in which the robots need to trade off the task performance with the costs of taking actions when they select act
Externí odkaz:
http://arxiv.org/abs/2310.01519
Autor:
Lin, Xiaomin, Karapetyan, Nare, Joshi, Kaustubh, Liu, Tianchen, Chopra, Nikhil, Yu, Miao, Tokekar, Pratap, Aloimonos, Yiannis
Autonomous navigation in the underwater environment is challenging due to limited visibility, dynamic changes, and the lack of a cost-efficient accurate localization system. We introduce UIVNav, a novel end-to-end underwater navigation solution desig
Externí odkaz:
http://arxiv.org/abs/2309.08806
Publikováno v:
OCEANS 2023 - MTS/IEEE U.S. Gulf Coast
With a number of marine populations in rapid decline, collecting and analyzing data about marine populations has become increasingly important to develop effective conservation policies for a wide range of marine animals, including whales. Modern com
Externí odkaz:
http://arxiv.org/abs/2308.07766
Autor:
Palnitkar, Aadi, Kapu, Rashmi, Lin, Xiaomin, Liu, Cheng, Karapetyan, Nare, Aloimonos, Yiannis
Publikováno v:
OCEANS 2023 - MTS/IEEE U.S. Gulf Coast
Robots are becoming an essential part of many operations including marine exploration or environmental monitoring. However, the underwater environment presents many challenges, including high pressure, limited visibility, and harsh conditions that ca
Externí odkaz:
http://arxiv.org/abs/2308.04029
Autor:
Adama Sawadogo, Gaia Severgnini, Moussa Bazongo, Lassina Konaté, Farid Pingwindé Belem, Youssouf Naré, Alain Sanou, Silvia Perlangeli, Lorena Fénoglio, Alberto Pilozzi, Maurizio Roberto, Marco Zanobini
Publikováno v:
Journal of Cardiothoracic Surgery, Vol 19, Iss 1, Pp 1-3 (2024)
Abstract The case presents a traumatic ventricular perforation of a girl, accidentally felt on a sharp instrument. The uniqueness of the case presented is due to the very high infrequency of injuries with this type of sharp object. The 7-year-old gir
Externí odkaz:
https://doaj.org/article/59138c1b978e49089d69c87452d254a7
Publikováno v:
OCEANS 2022, Hampton Roads, 2022, pp. 1-6
Oysters are the living vacuum cleaners of the oceans. There is an exponential decline in the oyster population due to over-harvesting. With the current development of the automation and AI, robots are becoming an integral part of the environmental mo
Externí odkaz:
http://arxiv.org/abs/2209.09395
Publikováno v:
2023 IEEE International Conference on Robotics and Automation (ICRA)
Oysters play a pivotal role in the bay living ecosystem and are considered the living filters for the ocean. In recent years, oyster reefs have undergone major devastation caused by commercial over-harvesting, requiring preservation to maintain ecolo
Externí odkaz:
http://arxiv.org/abs/2209.08176
Autor:
Asghar, Ahmad Bilal, Shi, Guangyao, Karapetyan, Nare, Humann, James, Reddinger, Jean-Paul, Dotterweich, James, Tokekar, Pratap
We study a resource allocation problem for the cooperative aerial-ground vehicle routing application, in which multiple Unmanned Aerial Vehicles (UAVs) with limited battery capacity and multiple Unmanned Ground Vehicles (UGVs) that can also act as a
Externí odkaz:
http://arxiv.org/abs/2209.06308
HTRON:Efficient Outdoor Navigation with Sparse Rewards via Heavy Tailed Adaptive Reinforce Algorithm
Autor:
Weerakoon, Kasun, Chakraborty, Souradip, Karapetyan, Nare, Sathyamoorthy, Adarsh Jagan, Bedi, Amrit Singh, Manocha, Dinesh
We present a novel approach to improve the performance of deep reinforcement learning (DRL) based outdoor robot navigation systems. Most, existing DRL methods are based on carefully designed dense reward functions that learn the efficient behavior in
Externí odkaz:
http://arxiv.org/abs/2207.03694