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Modeling Framework for Collision Avoidance Learning in Multirotor Unmanned Aerial Variables.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Modeling Framework for Collision Avoidance Learning in Multirotor Unmanned Aerial Variables./
Author:	Gabani, Krushang Khimjibhai.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	92 p.
Notes:	Source: Masters Abstracts International, Volume: 81-09.
Contained By:	Masters Abstracts International81-09.
Subject:	Robotics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27736635
ISBN:	9781658421706

Modeling Framework for Collision Avoidance Learning in Multirotor Unmanned Aerial Variables.
Gabani, Krushang Khimjibhai.

Modeling Framework for Collision Avoidance Learning in Multirotor Unmanned Aerial Variables. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 92 p.

Source: Masters Abstracts International, Volume: 81-09.

Thesis (M.Eng.)--State University of New York at Buffalo, 2020.

This item must not be sold to any third party vendors.

This work focuses on the idea of generating the learning framework for cooperative collision avoidance between two quadcopters. Two strategies for reciprocal online collision-avoiding actions (i.e., coherent maneuvers without requiring any real-time consensus) are proposed. In the first strategy, UAVs change their speed, while in the second strategy, they change their heading to avoid a collision. The avoidance actions are parameterized in terms of the time difference between detecting the collision and starting the maneuver and the amount of speed/heading change. These action parameters are used to generate intermediate way-points, subsequently translated into a minimum snap trajectory, to be executed by a PD controller. This work presents a learning-based approach to train such reciprocal maneuvers. A Neuroevolution approach, which uses evolutionary algorithms to optimize the topology and weights of neural networks simultaneously, is used as the learning method -- which operates over a set of sample approach scenarios to evaluate the fitness of each neural network candidate. Unlike most existing work (that minimize travel distance), the training process here has the capability of minimizing the required detection range and detection time. The specialized design of experiments and line search is used to identify the minimum detection range for each sample scenarios. This training process can also handle substantial challenges like uncertain UAV localization. For that, the relative pose of the other UAV, estimated by each UAV (at the point of detection), is considered to be uncertain. These capabilities have essential practical implications w.r.t. alleviating the dependency on sophisticated sensing and their reliability under various environments. Performing supervised learning based on optimization derived labels (as done in prior work) becomes computationally burdensome under these uncertainties. For an efficient training process, a classifier is used to discard actions (without simulating them) where the controller would fail. Also, a surrogate model is used to estimate the energy consumption and minimum distance between UAVs. The model obtained via neuroevolution is observed to generalize well to (i.e., successful collision avoidance over) unseen approach scenarios.

ISBN: 9781658421706Subjects--Topical Terms:

561941
Robotics.
Subjects--Index Terms:

Collision avoidance

Modeling Framework for Collision Avoidance Learning in Multirotor Unmanned Aerial Variables.
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300 $a 92 p.
500 $a Source: Masters Abstracts International, Volume: 81-09.
500 $a Advisor: Chowdhury, Souma.
502 $a Thesis (M.Eng.)--State University of New York at Buffalo, 2020.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be sold to any third party vendors.
520 $a This work focuses on the idea of generating the learning framework for cooperative collision avoidance between two quadcopters. Two strategies for reciprocal online collision-avoiding actions (i.e., coherent maneuvers without requiring any real-time consensus) are proposed. In the first strategy, UAVs change their speed, while in the second strategy, they change their heading to avoid a collision. The avoidance actions are parameterized in terms of the time difference between detecting the collision and starting the maneuver and the amount of speed/heading change. These action parameters are used to generate intermediate way-points, subsequently translated into a minimum snap trajectory, to be executed by a PD controller. This work presents a learning-based approach to train such reciprocal maneuvers. A Neuroevolution approach, which uses evolutionary algorithms to optimize the topology and weights of neural networks simultaneously, is used as the learning method -- which operates over a set of sample approach scenarios to evaluate the fitness of each neural network candidate. Unlike most existing work (that minimize travel distance), the training process here has the capability of minimizing the required detection range and detection time. The specialized design of experiments and line search is used to identify the minimum detection range for each sample scenarios. This training process can also handle substantial challenges like uncertain UAV localization. For that, the relative pose of the other UAV, estimated by each UAV (at the point of detection), is considered to be uncertain. These capabilities have essential practical implications w.r.t. alleviating the dependency on sophisticated sensing and their reliability under various environments. Performing supervised learning based on optimization derived labels (as done in prior work) becomes computationally burdensome under these uncertainties. For an efficient training process, a classifier is used to discard actions (without simulating them) where the controller would fail. Also, a surrogate model is used to estimate the energy consumption and minimum distance between UAVs. The model obtained via neuroevolution is observed to generalize well to (i.e., successful collision avoidance over) unseen approach scenarios.
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650 4 $a Mechanical engineering. $3 557493
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653 $a Machine learning
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653 $a Sensor deficiency
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