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On Cooperative Surveillance, Online Trajectory Planning and Observer Based ControlPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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PrimeFaces.cw("AccordionPanel","widget_formSmash_responsibleOrgs",{id:"formSmash:responsibleOrgs",widgetVar:"widget_formSmash_responsibleOrgs",multiple:true}); 2009 (English)Doctoral thesis, comprehensive summary (Other academic)
##### Abstract [en]

##### Place, publisher, year, edition, pages

Stockholm: KTH , 2009. , xii, 53 p.
##### Series

Trita-MAT. OS, ISSN 1401-2294 ; 2009:03
##### Keyword [en]

Surveillance Missions, Minimum-Time Surveillance, Unmanned Ground Vehicles, Connectivity Constraints, Combinatorial Optimization, Computational Optimal Control, Receding Horizon Control, Mission Uncertainty, Safety, Task Completion, Adaptive Grid Methods, Missile Guidance, Nonlinear Observer Design, Active Observers, Non--uniformly Observable Systems, Mobile Robotic Systems, Intrinsic Observers, Differential Geometric Methods, Euler-Lagrange Systems, Contraction Analysis.
##### National Category

Computational Mathematics Computational Mathematics
##### Identifiers

URN: urn:nbn:se:kth:diva-9990ISBN: 978-91-7415-246-3OAI: oai:DiVA.org:kth-9990DiVA: diva2:202389
##### Public defence

2009-04-01, E2, Linstedtsvägen 3, KTH, 10:00 (English)
##### Opponent

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt455",{id:"formSmash:j_idt455",widgetVar:"widget_formSmash_j_idt455",multiple:true});
##### Supervisors

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt461",{id:"formSmash:j_idt461",widgetVar:"widget_formSmash_j_idt461",multiple:true});
#####

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt467",{id:"formSmash:j_idt467",widgetVar:"widget_formSmash_j_idt467",multiple:true});
##### Projects

TAIS, AURES
##### Note

QC 20100622Available from: 2009-03-12 Created: 2009-02-25 Last updated: 2010-07-19Bibliographically approved
##### List of papers

The main body of this thesis consists of six appended papers. In the first two, different cooperative surveillance problems are considered. The second two consider different aspects of the trajectory planning problem, while the last two deal with observer design for mobile robotic and Euler-Lagrange systems respectively.In Papers A and B, a combinatorial optimization based framework to cooperative surveillance missions using multiple Unmanned Ground Vehicles (UGVs) is proposed. In particular, Paper A considers the the Minimum Time UGV Surveillance Problem (MTUSP) while Paper B treats the Connectivity Constrained UGV Surveillance Problem (CUSP). The minimum time formulation is the following. Given a set of surveillance UGVs and a polyhedral area, find waypoint-paths for all UGVs such that every point of the area is visible from a point on a waypoint-path and such that the time for executing the search in parallel is minimized. The connectivity constrained formulation extends the MTUSP by additionally requiring the induced information graph to be kept recurrently connected at the time instants when the UGVs perform the surveillance mission. In these two papers, the NP-hardness of both these problems are shown and decomposition techniques are proposed that allow us to find an approximative solution efficiently in an algorithmic manner.Paper C addresses the problem of designing a real time, high performance trajectory planner for an aerial vehicle that uses information about terrain and enemy threats, to fly low and avoid radar exposure on the way to a given target. The high-level framework augments Receding Horizon Control (RHC) with a graph based terminal cost that captures the global characteristics of the environment. An important issue with RHC is to make sure that the greedy, short term optimization does not lead to long term problems, which in our case boils down to two things: not getting into situations where a collision is unavoidable, and making sure that the destination is actually reached. Hence, the main contribution of this paper is to present a trajectory planner with provable safety and task completion properties. Direct methods for trajectory optimization are traditionally based on a priori temporal discretization and collocation methods. In Paper D, the problem of adaptive node distribution is formulated as a constrained optimization problem, which is to be included in the underlying nonlinear mathematical programming problem. The benefits of utilizing the suggested method for online trajectory optimization are illustrated by a missile guidance example.In Paper E, the problem of active observer design for an important class of non-uniformly observable systems, namely mobile robotic systems, is considered. The set of feasible configurations and the set of output flow equivalent states are defined. It is shown that the inter-relation between these two sets may serve as the basis for design of active observers. The proposed observer design methodology is illustrated by considering a unicycle robot model, equipped with a set of range-measuring sensors. Finally, in Paper F, a geometrically intrinsic observer for Euler-Lagrange systems is defined and analyzed. This observer is a generalization of the observer proposed by Aghannan and Rouchon. Their contractivity result is reproduced and complemented by a proof that the region of contraction is infinitely thin. Moreover, assuming a priori bounds on the velocities, convergence of the observer is shown by means of Lyapunov's direct method in the case of configuration manifolds with constant curvature.

1. Adaptive Node Distribution for Online Trajectory Planning$(function(){PrimeFaces.cw("OverlayPanel","overlay10935",{id:"formSmash:j_idt503:0:j_idt507",widgetVar:"overlay10935",target:"formSmash:j_idt503:0:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

2. Riemannian Observers for Euler-Lagrange Systems$(function(){PrimeFaces.cw("OverlayPanel","overlay10937",{id:"formSmash:j_idt503:1:j_idt507",widgetVar:"overlay10937",target:"formSmash:j_idt503:1:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

3. Active Observers for Mobile Robotic Systems$(function(){PrimeFaces.cw("OverlayPanel","overlay10936",{id:"formSmash:j_idt503:2:j_idt507",widgetVar:"overlay10936",target:"formSmash:j_idt503:2:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

4. Online Trajectory Planning for Aerial Vehicle: A Safe Approach with Guaranteed Task Completion$(function(){PrimeFaces.cw("OverlayPanel","overlay10934",{id:"formSmash:j_idt503:3:j_idt507",widgetVar:"overlay10934",target:"formSmash:j_idt503:3:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

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