Dr. Shankar Sankararaman Profile

Dr. Shankar Sankararaman

Data Scientist

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 10 May 2018 Presentation + Paper

Computational architecture for autonomous decision-making in unmanned aerial vehicles

Shankar Sankararaman, Kai Goebel

Proceedings Volume 10639, 106391Y (2018) https://doi.org/10.1117/12.2304506

KEYWORDS: Computer architecture, Sensors, Network architectures, Unmanned aerial vehicles, Information fusion, Safety, Unmanned systems

Read Abstract +

This paper presents a computational architecture to facilitate autonomous decision-making under uncertainty for safe operation of drone-like vehicles. The proposed framework is based on identifying and predicting the occurrence of various risk-factors that affect the safe operation of such vehicles and estimating the likelihood of occurrence of these risk-factors. This analysis is then used to select trajectories for the operation of the vehicle. Feasible trajectories are classified into four different categories: nominal and safe, off-nominal but safe, unsafe and abort the mission, and unsafe and ditch the vehicle. An important challenge in the operation of drones is that there are several sources of uncertainty that affect their operation; these sources of uncertainty arise from wind conditions, imprecise future power-demands, inexact future trajectories, etc. Therefore, it is important to develop a decision-making framework that can incorporate all these sources of uncertainty and make decisions that are robust to the presence of such uncertainty. Potential risk-factors such as dynamic obstacles, battery drain, etc. are identified and the likelihood of occurrence of these risk-factors are predicted preemptively and proactively in order to facilitate risk-informed safety-assured decision-making.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years