Towards Autonomous Self-Righting for Robots in 3D

Neal, Barbara 

US soldiers have indicated that the robots they use sometimes unintentionally flip over in

field conditions, and it can be difficult to return them to their up-right orientation

remotely. A universal self-righting solution to this problem is quintessential to retrofit

and equip future systems. To date, ARL researchers have developed a 2D generic

analysis framework whereby any robot can maximize its own ability to self-right using

whatever intrinsic hardware it has. In this effort, we work toward redesigning the

software for higher degree of freedom 3D analysis while improving performance.

Specifically, we focus on translating the existing code from MATLAB to C++, and

changing the existing exhaustive algorithms to using the idea of white and black box

testing solutions for efficiency. We also leverage two traditional motion planning

techniques, Probabilistic Road Maps (PRMs) and Rapidly Exploring Random Trees

(RRTs). We expect that this will enable future robots to be designed to self-right under a

wider variety of circumstances, enhancing their usefulness to soldiers.

Acknowledgements

I wish to acknowledge the mentorship of Chad Kessens a robotic manipulation researcher

with the Army Research Laboratory's Autonomous Systems Division. I would also like to

thank Lenora Longstreet-Haire, for guiding me to this amazing opportunity. 

Student Bio

I am a rising senior at Chicago State University. I’m a computer science major with a

background in design, management, and innovative problem solving. This internship at

APG with the DOD is my second research experience. Last summer I did research for

Argonne National Laboratory. Considering the many directions my field of study and

experience can take me, ideally I will land a position in telematics and machine learning

research. This opportunity has been an invaluable step on my optimal path.

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