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Naveena Crasta

Researcher at University of Lisbon

Publications -  27
Citations -  464

Naveena Crasta is an academic researcher from University of Lisbon. The author has contributed to research in topics: Observability & Motion planning. The author has an hindex of 10, co-authored 26 publications receiving 350 citations. Previous affiliations of Naveena Crasta include Technische Universität Ilmenau & Technical University of Lisbon.

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Environmental Monitoring using Autonomous Vehicles: A Survey of Recent Searching Techniques

TL;DR: Recent advances in searching techniques that are at the core of environmental monitoring strategies using autonomous vehicles are surveyed.
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Range-Based Underwater Vehicle Localization in the Presence of Unknown Ocean Currents: Theory and Experiments

TL;DR: This paper addresses the problem of range-based autonomous underwater vehicle (AUV) localization in the presence of unknown ocean currents by deriving conditions under which it is possible to reconstruct the initial condition of the system under study and proposing a novel observer for simultaneous AUV and beacon localization.
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Multiple autonomous surface vehicle motion planning for cooperative range-based underwater target localization

TL;DR: This paper proposes a general methodology for tracker motion planning that results from maximizing the determinant of an appropriately defined Fisher information matrix (FIM) subject to inter-vehicle collision avoidance and vehicle maneuvering constraints and provides a family of analytical solutions for the optimal tracker trajectories.
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Observability analysis of 3D AUV trimming trajectories in the presence of ocean currents using range and depth measurements

TL;DR: The observability properties of the kinematic model of an autonomous underwater vehicle (AUV) moving in 3D, under the influence of ocean currents, are analyzed using range and depth measurements to shed light into the types of trajectories that an AUV may be requested to undergo in order to ensure observability.
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Integrated Motion Planning, Control, and Estimation for Range-Based Marine Vehicle Positioning and Target Localization

TL;DR: In this paper, the authors address the problem of range-based marine vehicle positioning and target localization by maximizing the determinant of a suitably defined Fisher information matrix (FIM), subject to inter-vehicle collision avoidance and vehicle maneuvering constraints.