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You searched for +publisher:"Delft University of Technology" +contributor:("McGuire, Kimberly"). Showing records 1 – 3 of 3 total matches.

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Delft University of Technology

1. van Dijk, Tom (author). Low-memory Visual Route Following for Micro Aerial Vehicles in Indoor Environments.

Degree: 2017, Delft University of Technology

This thesis presents a visual route following method that minimizes memory consumption to the point that even Micro Aerial Vehicles (MAV) equipped with only a simple microcontroller can traverse distances of a few hundred meters. Existing Simultaneous Localization and Mapping (SLAM) algorithms are too complex for use on a microcontroller. Instead, the route is modeled by a sequence of snapshots that can be followed back using a combination of visual homing and odometry. Three visual homing methods are evaluated to find and compare their memory efficiency. Of these methods, Fourier-based homing performed best: it still succeeds when snapshots are compressed to less than twenty bytes. Visual homing only works from a small region surrounding the snapshot, therefore odometry is used to travel longer distances between snapshots. The proposed route following technique is tested in simulation and on a Parrot AR.Drone 2.0. The drone can successfully follow long routes with a map that consumes only 17.5 bytes per meter.

BioMechanical Design & Systems and Control

Advisors/Committee Members: McGuire, Kimberly (mentor), de Croon, Guido (mentor), Campoy Cervera, Pascual (mentor), Jonker, Pieter (mentor), Delft University of Technology (degree granting institution).

Subjects/Keywords: navigation; route following; visual homing; odometry; indoor; micro aerial vehicle; MAV; unmanned aerial vehicle; UAV; quadrotor; vision; ARDrone; MAVLAB

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

van Dijk, T. (. (2017). Low-memory Visual Route Following for Micro Aerial Vehicles in Indoor Environments. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:82c91d74-6c01-4718-a574-221df210f01a

Chicago Manual of Style (16th Edition):

van Dijk, Tom (author). “Low-memory Visual Route Following for Micro Aerial Vehicles in Indoor Environments.” 2017. Masters Thesis, Delft University of Technology. Accessed January 21, 2021. http://resolver.tudelft.nl/uuid:82c91d74-6c01-4718-a574-221df210f01a.

MLA Handbook (7th Edition):

van Dijk, Tom (author). “Low-memory Visual Route Following for Micro Aerial Vehicles in Indoor Environments.” 2017. Web. 21 Jan 2021.

Vancouver:

van Dijk T(. Low-memory Visual Route Following for Micro Aerial Vehicles in Indoor Environments. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Jan 21]. Available from: http://resolver.tudelft.nl/uuid:82c91d74-6c01-4718-a574-221df210f01a.

Council of Science Editors:

van Dijk T(. Low-memory Visual Route Following for Micro Aerial Vehicles in Indoor Environments. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:82c91d74-6c01-4718-a574-221df210f01a


Delft University of Technology

2. Bendriş, Bianca (author). Decentralized Stochastic Optimal Control for a Swarm of Micro Aerial Vehicles.

Degree: 2019, Delft University of Technology

In this work, we model a multi-robot formation planning and control task as an optimization problem, which we solve on-line and in a decentralized manner using the Stochastic Optimal Control (SOC) framework. Typically, the solution of a SOC problem requires solving the Hamilton-Jacobi-Bellman (HJB) equation for all system states and controls. However, this operation becomes intractable when high-dimensional systems are used. In recent years, advances on a certain type of SOC problem, which can be efficiently solved by sampling from a diffusion process have been presented and are better known as path integral (PI) control. We build upon this theory and implement a decentralized formulation of the PI algorithm to compute the optimal controls of real Micro Aerial Vehicles (MAVs) flying in formation using solely on-board computational resources. One challenging aspect of the PI control method is the efficient sampling of useful trajectories. It is not clear how to guide the samples towards the optimal states. To this end, we propose a probe enhanced importance sampling (PEIS) method which performs a coarse exploration of the state space with the objective of identifying an optimal guiding trajectory around which the samples are taken. The feasibility of the proposed method is shown by means of simulation and real-hardware experiments with up to four MAVs in an indoor environment.

Aerospace Engineering

Advisors/Committee Members: de Croon, Guido (mentor), McGuire, Kimberly (graduation committee), Kappen, Bert (graduation committee), Delft University of Technology (degree granting institution).

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Bendriş, B. (. (2019). Decentralized Stochastic Optimal Control for a Swarm of Micro Aerial Vehicles. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:0cd4f9e3-faf3-4e65-995c-7dd401c8da4c

Chicago Manual of Style (16th Edition):

Bendriş, Bianca (author). “Decentralized Stochastic Optimal Control for a Swarm of Micro Aerial Vehicles.” 2019. Masters Thesis, Delft University of Technology. Accessed January 21, 2021. http://resolver.tudelft.nl/uuid:0cd4f9e3-faf3-4e65-995c-7dd401c8da4c.

MLA Handbook (7th Edition):

Bendriş, Bianca (author). “Decentralized Stochastic Optimal Control for a Swarm of Micro Aerial Vehicles.” 2019. Web. 21 Jan 2021.

Vancouver:

Bendriş B(. Decentralized Stochastic Optimal Control for a Swarm of Micro Aerial Vehicles. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 21]. Available from: http://resolver.tudelft.nl/uuid:0cd4f9e3-faf3-4e65-995c-7dd401c8da4c.

Council of Science Editors:

Bendriş B(. Decentralized Stochastic Optimal Control for a Swarm of Micro Aerial Vehicles. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:0cd4f9e3-faf3-4e65-995c-7dd401c8da4c


Delft University of Technology

3. van der Helm, Steven (author). On-board Range-based Relative Localization: For Leader-Follower Flight of Micro Aerial Vehicles.

Degree: 2018, Delft University of Technology

In this paper a range-based relative localization solution is proposed and demonstrated in practice. The approach is based on wireless range measurements between robots, along with the communication of their velocities, accelerations, yaw rates, and height. It can be implemented on many robotic platforms without the need for dedicated sensors. With respect to previous work, we remove the dependency on a common heading reference between robots. The main advantage of this is that it makes the relative localization approach independent of magnetometer readings, which are notoriously unreliable in an indoor environment. A theoretical observability analysis shows that it may also have two disadvantages: the motion of the robots must meet more stringent conditions and the relative localization method becomes more susceptible to noise on the range measurements. However, simulation results have shown that in the presence of significant magnetic disturbances that are common to indoor environments, removing the heading dependency is beneficial. We conclude the paper by implementing the heading-independent method on real Micro Aerial Vehicles (MAVs) and performing leader-follower flight in an indoor environment. Despite the observability analysis showing leader-follower flight to be an especially difficult task, we still manage to successfully fly for over 3 minutes with two fully autonomous followers using only on-board sensing. Advisors/Committee Members: de Croon, Guido (mentor), Mcguire, Kimberly (graduation committee), Coppola, Mario (graduation committee), Chu, Qiping (graduation committee), Verhoeven, Chris (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: Relative Localization; Leader-Follower; Swarming; Ultra Wideband

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

van der Helm, S. (. (2018). On-board Range-based Relative Localization: For Leader-Follower Flight of Micro Aerial Vehicles. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:6a3c6f9c-1634-4575-844c-510092f73dc6

Chicago Manual of Style (16th Edition):

van der Helm, Steven (author). “On-board Range-based Relative Localization: For Leader-Follower Flight of Micro Aerial Vehicles.” 2018. Masters Thesis, Delft University of Technology. Accessed January 21, 2021. http://resolver.tudelft.nl/uuid:6a3c6f9c-1634-4575-844c-510092f73dc6.

MLA Handbook (7th Edition):

van der Helm, Steven (author). “On-board Range-based Relative Localization: For Leader-Follower Flight of Micro Aerial Vehicles.” 2018. Web. 21 Jan 2021.

Vancouver:

van der Helm S(. On-board Range-based Relative Localization: For Leader-Follower Flight of Micro Aerial Vehicles. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 21]. Available from: http://resolver.tudelft.nl/uuid:6a3c6f9c-1634-4575-844c-510092f73dc6.

Council of Science Editors:

van der Helm S(. On-board Range-based Relative Localization: For Leader-Follower Flight of Micro Aerial Vehicles. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:6a3c6f9c-1634-4575-844c-510092f73dc6

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