S
Sina Sharif Mansouri
Researcher at Luleå University of Technology
Publications - 65
Citations - 716
Sina Sharif Mansouri is an academic researcher from Luleå University of Technology. The author has contributed to research in topics: Computer science & Model predictive control. The author has an hindex of 12, co-authored 60 publications receiving 371 citations. Previous affiliations of Sina Sharif Mansouri include University College of Engineering & University of Tehran.
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Journal ArticleDOI
Cooperative coverage path planning for visual inspection
Sina Sharif Mansouri,Christoforos Kanellakis,Emil Fresk,Dariusz Kominiak,George Nikolakopoulos +4 more
TL;DR: The main novelty of the proposed Collaborative-Coverage Path Planning (C-CPP) stems from the establishment of a theoretical framework capable of providing a path for accomplishing a full coverage of the infrastructure, without any further simplifications.
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Nonlinear MPC for Collision Avoidance and Control of UAVs With Dynamic Obstacles
TL;DR: This letter proposes a Novel Nonlinear Model Predictive Control (NMPC) for navigation and obstacle avoidance of an Unmanned Aerial Vehicle (UAV) and applies a classification scheme to differentiate between different kinds of trajectories to predict future obstacle positions.
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Remaining Useful Battery Life Prediction for UAVs based on Machine Learning
TL;DR: The problem of Remaining Useful Life estimation of a battery, under different flight conditions, is tackled using four machine learning techniques: a linear sparse model, a variant of support vector regression, a multilayer perceptron and an advanced tree based algorithm.
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Nonlinear MPC for Collision Avoidance and Controlof UAVs With Dynamic Obstacles
TL;DR: In this paper, a novel nonlinear model predictive control (NMPC) for navigation and obstacle avoidance of an Unmanned Aerial Vehicle (UAV) is proposed, which allows for a fully parametric obstacle trajectory, while in this article we apply a classification scheme to differentiate between different kinds of trajectories to predict future obstacle positions.
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Deploying MAVs for autonomous navigation in dark underground mine environments
TL;DR: This work proposes a novel baseline approach for the navigation of resource constrained robots, introducing the aerial underground scout, with the main goal to rapidly explore unknown areas and provide a feedback to the operator.