Automatica (Journal of IFAC)
Efficient algorithms for geometric optimization
ACM Computing Surveys (CSUR)
Gradient Convergence in Gradient methods with Errors
SIAM Journal on Optimization
Brief paper: An adaptive optimization scheme with satisfactory transient performance
Automatica (Journal of IFAC)
Parallel Tracking and Mapping for Small AR Workspaces
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
Large scale nonlinear control system fine-tuning through learning
IEEE Transactions on Neural Networks
Optimal coverage for multiple hovering robots with downward facing cameras
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Intuitive 3D Maps for MAV Terrain Exploration and Obstacle Avoidance
Journal of Intelligent and Robotic Systems
A passivity-based decentralized strategy for generalized connectivity maintenance
International Journal of Robotics Research
Distributed multi-robot patrol: A scalable and fault-tolerant framework
Robotics and Autonomous Systems
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The problem of deploying a team of flying robots to perform surveillance coverage missions over an unknown terrain of complex and non-convex morphology is presented. In such a mission, the robots attempt to maximize the part of the terrain that is visible while keeping the distance between each point in the terrain and the closest team member as small as possible. A trade-off between these two objectives should be fulfilled given the physical constraints and limitations imposed at the particular application. As the terrain's morphology is unknown and it can be quite complex and non-convex, standard algorithms are not applicable to the particular problem treated in this paper. To overcome this, a new approach based on the Cognitive-based Adaptive Optimization (CAO) algorithm is proposed and evaluated. A fundamental property of this approach is that it shares the same convergence characteristics as those of constrained gradient-descent algorithms (which require perfect knowledge of the terrain's morphology and optimize surveillance coverage subject to the constraints the team has to satisfy). Rigorous mathematical arguments and extensive simulations establish that the proposed approach provides a scalable and efficient methodology that incorporates any particular physical constraints and limitations used to navigate the robots into an arrangement that (locally) optimizes surveillance coverage.