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Alexandros Nikou

Bio: Alexandros Nikou is an academic researcher from Royal Institute of Technology. The author has contributed to research in topics: Multi-agent system & Workspace. The author has an hindex of 15, co-authored 47 publications receiving 488 citations. Previous affiliations of Alexandros Nikou include National Technical University of Athens & Ericsson.

Papers
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Proceedings ArticleDOI
06 Jul 2016
TL;DR: A method for automatic control synthesis in a two-stage systematic procedure that guarantees that all the agents satisfy their own individual task specifications as well as that the team satisfies a team global task specification.
Abstract: In this paper the problem of cooperative task planning of multi-agent systems when timed constraints are imposed to the system is investigated. We consider timed constraints given by Metric Interval Temporal Logic (MITL). We propose a method for automatic control synthesis in a two-stage systematic procedure. With this method we guarantee that all the agents satisfy their own individual task specifications as well as that the team satisfies a team global task specification.

58 citations

Journal ArticleDOI
TL;DR: This paper presents a novel control protocol for distance and orientation formation control of rigid bodies, whose communication graph is a static and undirected tree, in the special Euclidean group SE(3).

58 citations

Journal ArticleDOI
TL;DR: A robust nonlinear model predictive control scheme is presented for the case of underactuated autonomous underwater vehicles (AUVs) and is presented a reliable control strategy that takes into account the aforementioned issues, along with dynamic uncertainties of the model and the presence of ocean currents.
Abstract: This article addresses the tracking control problem of 3-D trajectories for underactuated underwater robotic vehicles operating in a constrained workspace including obstacles More specifically, a robust nonlinear model predictive control (NMPC) scheme is presented for the case of underactuated autonomous underwater vehicles (AUVs) (ie, unicycle-like vehicles actuated only in the surge, heave, and yaw) The purpose of the controller is to steer the unicycle-like AUV to the desired trajectory with guaranteed input and state constraints (eg, obstacles, predefined vehicle velocity bounds, and thruster saturations) inside a partially known and dynamic environment where the knowledge of the operating workspace is constantly updated via the vehicle’s onboard sensors In particular, considering the sensing range of the vehicle, obstacle avoidance with any of the detected obstacles is guaranteed by the online generation of a collision-free trajectory tracking path, despite the model dynamic uncertainties and the presence of external disturbances representing ocean currents and waves Finally, realistic simulation studies verify the performance and efficiency of the proposed framework Note to Practitioners —This article was motivated by the problem of robust trajectory tracking for an autonomous underwater vehicle (AUV) operating in an uncertain environment where the knowledge of the operating workspace (eg, obstacle positions) is constantly updated online via the vehicle’s onboard sensors (eg, multibeam imaging sonars and laser-based vision systems) In addition, there may be other system limitations (eg, thruster saturation limits) and other operational constraints, induced by the need of various common underwater tasks (eg, a predefined vehicle speed limit for inspecting the seabed, and mosaicking), where it should also be considered into the control strategy However, based on the existing trajectory tracking control approaches for underwater robotics, there is a lack of an autonomous control scheme that provides a complete and credible control strategy that takes the aforementioned issues into consideration Based on this, we present a reliable control strategy that takes into account the aforementioned issues, along with dynamic uncertainties of the model and the presence of ocean currents In future research, we will extend the proposed methodology for multiple AUV performing collaborative inspection tasks in an uncertain environment

57 citations

Journal ArticleDOI
TL;DR: This paper presents a force/position tracking control protocol for an Underwater Vehicle Manipulator System (UVMS) in compliant contact with a planar surface, without incorporating any knowledge of the UVMS dynamic model, the exogenous disturbances or the contact stiffness model.

45 citations

Journal ArticleDOI
TL;DR: This paper addresses the problem of decentralized tube-based nonlinear Model Predictive Control for a class of uncertain nonlinear continuous-time multi-agent systems with additive and bounded disturbance and proposes a decentralized feedback control protocol that guarantees that the initially connected agents remain connected for all times.
Abstract: This paper addresses the problem of decentralized tube-based nonlinear model predictive control (NMPC) for a general class of uncertain nonlinear continuous-time multiagent systems with additive an ...

39 citations


Cited by
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01 Jan 2016
TL;DR: In this paper, the authors describe how to download and install guidance and control of ocean vehicles in the house, workplace, or perhaps in your method can be all best place within net connections.
Abstract: By searching the title, publisher, or authors of guide you in reality want, you can discover them rapidly. In the house, workplace, or perhaps in your method can be all best place within net connections. If you objective to download and install the guidance and control of ocean vehicles, it is utterly easy then, past currently we extend the colleague to buy and make bargains to download and install guidance and control of ocean vehicles therefore simple!

611 citations

Journal ArticleDOI
TL;DR: In this article, the authors tried to read modelling and control of robot manipulators as one of the reading material to finish quickly, and they found that reading book can be a great choice when having no friends and activities.
Abstract: Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will concern you to try reading modelling and control of robot manipulators as one of the reading material to finish quickly.

517 citations

Proceedings ArticleDOI
16 May 2016
TL;DR: An eight-rotor configuration is derived that maximizes the vehicle's agility in any direction and possesses full force and torque authority in all three dimensions of the proposed six degrees-of-freedom aerial vehicle.
Abstract: In this paper we present the design and control of a novel six degrees-of-freedom aerial vehicle. Based on a static force and torque analysis for generic actuator configurations, we derive an eight-rotor configuration that maximizes the vehicle's agility in any direction. The proposed vehicle design possesses full force and torque authority in all three dimensions. A control strategy that allows for exploiting the vehicle's decoupled translational and rotational dynamics is introduced. A prototype of the proposed vehicle design is built using reversible motor-propeller actuators and capable of flying at any orientation. Preliminary experimental results demonstrate the feasibility of the novel design and the capabilities of the vehicle.

221 citations

01 Jan 2004
TL;DR: In this paper, robust decentralized model predictive control (DMPC) for a team of cooperating uninhabited aerial vehicles (UAVs) is implemented. But the authors focus on the non-convex problem of path-planning subject to avoidance constraints.
Abstract: This paper implements robust decentralized model predictive control (DMPC) for a team of cooperating uninhabited aerial vehicles (UAVs). The problem involves vehicles with independent dynamics but with coupled constraints to capture required cooperative behavior. Using a recently-developed form of DMPC, each vehicle plans only for its own actions, but feasibility of the sub-problems and satisfaction of the coupling constraints are guaranteed throughout, despite the action of unknown but bounded disturbances. UAVs communicate relevant plan data to ensure that decisions are consistent across the team. Collision avoidance is used as an example of coupled constraints and the paper shows how the speed, turn rate and avoidance distance limits in the optimization should be modified in order to guarantee robust constraint satisfaction. Integer programming is used to solve the non-convex problem of path-planning subject to avoidance constraints. Numerical simulations compare computation time and target arrival time under decentralized and centralized control and investigate the impact of decentralization on team performance. The results show that the computation required for DMPC is significantly lower than for its centralized counterpart and scales better with the size of the team, at the expense of only a small increase in UAV flight times.

201 citations