Zobrazeno 1 - 10
of 299
pro vyhledávání: '"GREGORY, JASON"'
Navigation safety is critical for many autonomous systems such as self-driving vehicles in an urban environment. It requires an explicit consideration of boundary constraints that describe the borders of any infeasible, non-navigable, or unsafe regio
Externí odkaz:
http://arxiv.org/abs/2403.14956
Autor:
Aich, Shubhra, Wang, Wenshan, Maheshwari, Parv, Sivaprakasam, Matthew, Triest, Samuel, Ho, Cherie, Gregory, Jason M., Rogers III, John G., Scherer, Sebastian
The limited sensing resolution of resource-constrained off-road vehicles poses significant challenges towards reliable off-road autonomy. To overcome this limitation, we propose a general framework based on fusing the future information (i.e. future
Externí odkaz:
http://arxiv.org/abs/2403.11876
Autor:
Maheshwari, Parv, Wang, Wenshan, Triest, Samuel, Sivaprakasam, Matthew, Aich, Shubhra, Rogers III, John G., Gregory, Jason M., Scherer, Sebastian
Modeling the precise dynamics of off-road vehicles is a complex yet essential task due to the challenging terrain they encounter and the need for optimal performance and safety. Recently, there has been a focus on integrating nominal physics-based mo
Externí odkaz:
http://arxiv.org/abs/2311.00815
Mapless navigation has emerged as a promising approach for enabling autonomous robots to navigate in environments where pre-existing maps may be inaccurate, outdated, or unavailable. In this work, we propose an image-based local representation of the
Externí odkaz:
http://arxiv.org/abs/2310.14065
Traversability prediction is a fundamental perception capability for autonomous navigation. Deep neural networks (DNNs) have been widely used to predict traversability during the last decade. The performance of DNNs is significantly boosted by exploi
Externí odkaz:
http://arxiv.org/abs/2306.14370
Mixup-based data augmentation has been validated to be a critical stage in the self-training framework for unsupervised domain adaptive semantic segmentation (UDA-SS), which aims to transfer knowledge from a well-annotated (source) domain to an unlab
Externí odkaz:
http://arxiv.org/abs/2303.02741
Robot data collected in complex real-world scenarios are often biased due to safety concerns, human preferences, and mission or platform constraints. Consequently, robot learning from such observational data poses great challenges for accurate parame
Externí odkaz:
http://arxiv.org/abs/2210.08679
Experimental design in field robotics is an adaptive human-in-the-loop decision-making process in which an experimenter learns about system performance and limitations through interactions with a robot in the form of constructed experiments. This can
Externí odkaz:
http://arxiv.org/abs/2210.08397