Zobrazeno 1 - 10
of 191
pro vyhledávání: '"Petillot, Yvan"'
Autor:
Adetunji, Favour O., Ellis, Niamh, Koskinopoulou, Maria, Carlucho, Ignacio, Petillot, Yvan R.
Subsea exploration, inspection, and intervention operations heavily rely on remotely operated vehicles (ROVs). However, the inherent complexity of the underwater environment presents significant challenges to the operators of these vehicles. This pap
Externí odkaz:
http://arxiv.org/abs/2402.07556
Autonomous underwater vehicles (AUVs) play a crucial role in surveying marine environments, carrying out underwater inspection tasks, and ocean exploration. However, in order to ensure that the AUV is able to carry out its mission successfully, a con
Externí odkaz:
http://arxiv.org/abs/2308.05547
Publikováno v:
EPTCS 362, 2022, pp. 37-53
In real-world applications, the ability to reason about incomplete knowledge, sensing, temporal notions, and numeric constraints is vital. While several AI planners are capable of dealing with some of these requirements, they are mostly limited to pr
Externí odkaz:
http://arxiv.org/abs/2207.09709
Autor:
Willners, Jonatan Scharff, Carlucho, Ignacio, Łuczyński, Tomasz, Katagiri, Sean, Lemoine, Chandler, Roe, Joshua, Stephens, Dylan, Xu, Shida, Carreno, Yaniel, Pairet, Èric, Barbalata, Corina, Petillot, Yvan, Wang, Sen
Autonomous Underwater Vehicles (AUVs) are becoming increasingly important for different types of industrial applications. The generally high cost of (AUVs) restricts the access to them and therefore advances in research and technological development.
Externí odkaz:
http://arxiv.org/abs/2108.05792
Autor:
Luczynski, Tomasz, Willners, Jonatan Scharff, Vargas, Elizabeth, Roe, Joshua, Xu, Shida, Cao, Yu, Petillot, Yvan, Wang, Sen
This paper presents a novel dataset for the development of visual navigation and simultaneous localisation and mapping (SLAM) algorithms as well as for underwater intervention tasks. It differs from existing datasets as it contains ground truth for t
Externí odkaz:
http://arxiv.org/abs/2107.13628
A Simultaneous Localization and Mapping (SLAM) system must be robust to support long-term mobile vehicle and robot applications. However, camera and LiDAR based SLAM systems can be fragile when facing challenging illumination or weather conditions wh
Externí odkaz:
http://arxiv.org/abs/2104.05347
Robotic systems may frequently come across similar manipulation planning problems that result in similar motion plans. Instead of planning each problem from scratch, it is preferable to leverage previously computed motion plans, i.e., experiences, to
Externí odkaz:
http://arxiv.org/abs/2103.00448
Autor:
Wang, Cong, Zhang, Qifeng, Tian, Qiyan, Li, Shuo, Wang, Xiaohui, Lane, David, Petillot, Yvan, Hong, Ziyang, Wang, Sen
Agile control of mobile manipulator is challenging because of the high complexity coupled by the robotic system and the unstructured working environment. Tracking and grasping a dynamic object with a random trajectory is even harder. In this paper, a
Externí odkaz:
http://arxiv.org/abs/2006.04271
Deep convolutional neural networks generally perform well in underwater object recognition tasks on both optical and sonar images. Many such methods require hundreds, if not thousands, of images per class to generalize well to unseen examples. Howeve
Externí odkaz:
http://arxiv.org/abs/2005.04621
Numerous Simultaneous Localization and Mapping (SLAM) algorithms have been presented in last decade using different sensor modalities. However, robust SLAM in extreme weather conditions is still an open research problem. In this paper, RadarSLAM, a f
Externí odkaz:
http://arxiv.org/abs/2005.02198