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Autonomy for sensor-rich vehicles: I...

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Autonomy for sensor-rich vehicles: Interaction between sensing and control actions.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Autonomy for sensor-rich vehicles: Interaction between sensing and control actions./
Author:	Hoffmann, Gabriel M.
Description:	153 p.
Notes:	Source: Dissertation Abstracts International, Volume: 69-10, Section: B, page: 6244.
Contained By:	Dissertation Abstracts International69-10B.
Subject:	Engineering, Aerospace. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3332839
ISBN:	9780549845997

Autonomy for sensor-rich vehicles: Interaction between sensing and control actions.
Hoffmann, Gabriel M.

Autonomy for sensor-rich vehicles: Interaction between sensing and control actions. - 153 p.

Source: Dissertation Abstracts International, Volume: 69-10, Section: B, page: 6244.

Thesis (Ph.D.)--Stanford University, 2008.

While autonomous vehicles have the potential to enable many revolutionary technologies, assisting people through unprecedented automation, they introduce many challenges in control system design. One step toward increasing their autonomy is to formulate an optimization problem that exploits models connecting the effects of the sensing and control systems to optimize the performance of the overall system. Using these models, the robots are able to intelligently experiment with their environment to work toward achieving their goals.

ISBN: 9780549845997Subjects--Topical Terms:

1018395
Engineering, Aerospace.

Autonomy for sensor-rich vehicles: Interaction between sensing and control actions.
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245 1 0 $a Autonomy for sensor-rich vehicles: Interaction between sensing and control actions.
300 $a 153 p.
500 $a Source: Dissertation Abstracts International, Volume: 69-10, Section: B, page: 6244.
502 $a Thesis (Ph.D.)--Stanford University, 2008.
520 $a While autonomous vehicles have the potential to enable many revolutionary technologies, assisting people through unprecedented automation, they introduce many challenges in control system design. One step toward increasing their autonomy is to formulate an optimization problem that exploits models connecting the effects of the sensing and control systems to optimize the performance of the overall system. Using these models, the robots are able to intelligently experiment with their environment to work toward achieving their goals.
520 $a This thesis presents techniques to directly exploit the connection between sensing and control, focusing on algorithms for mobile sensors and mobile sensor networks. Algorithms are developed to compute information theoretic quantities using a particle filter representation of the probability distributions over the states being estimated. To make the approach scalable to increasing network size, single-node and pairwise-node approximations to the mutual information are derived for general probability density models, with analytical bounds on the error incurred, and computation time that is polynomial in the number of sensors. The pairwise-node approximation is proven to be a more accurate objective function than the single-node approximation. A decentralized optimization algorithm is presented to implement these techniques, using a novel collision avoidance method incorporating hybrid control.
520 $a The characteristics of these algorithms are explored in simulation of autonomous search using three sensing modalities: range, bearing, and magnetic rescue beacon. For each sensing modality, the behavior of these non-parametric methods are compared and contrasted with the results of linearized methods. The proposed methods produce similar results in some scenarios, yet they capture effects in more general scenarios not possible with linearized methods. Monte Carlo results demonstrate that the pairwise-node approximation provides superior performance to the single-node approximation.
520 $a To motivate and demonstrate these algorithms, a fleet of quadrotor helicopters is designed and used, the Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC). The algorithms are implemented as an autonomous guidance system onboard these aircraft to automate search for an avalanche rescue beacon. Vehicle design, dynamics, and control are presented. Experimental results demonstrate improved vehicle control over the state-of-the-art, and the ability to autonomously search for a lost rescue beacon.
590 $a School code: 0212.
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