• Proprioceptive data available:Position and movement of JOINTS, BODY ORIENTATION & RATE OF ORIENTATION CHANGE

• Not much sensory data available

• Maximize self-rightability of fielded robots

• Improve performance • MATLAB C++• Exhaustive Terminal• Increase degrees of freedom handled• Transition the analysis framework from 2

dimensions to 3 dimensions

• Exhaustive search strategy = big issue.• We must continue to translate the code

to a platform with more memory, and expand code to 3D.

• We can use PRMs and RRTs to simplify the search problem.

• We can use the idea behind White Box testing and apply it to use the limits of the program to our advantage.

• These methods can make the problem smaller by only considering the best of the random path plans vs. finding all paths and the best of all the path plans.

Towards Autonomous Self-Righting for Robots in 3D 

Barbara Jean NealMajoring in Computer ScienceAttending Chicago State University

ARL Mentor: Chad KessensDirectorate/Division: VTD/ASDDuration of Project: 06/06/16– 08/12/16

Approved for Public Release; Distribution Unlimited

Approved for Public Release; Distribution Unlimited

From: Tool To: TeammateWider ApplicationsArmy Applications

Urban Search and

Rescue (USAR)

Planetary Surface

Exploration

Inspection, observance, and surveys of regions.

Explosive ordnance

disposal (EOD) missions

Resting on its rear and arm

Self-righting Validation Platform Joint

What is the minimum data that would be available on

most robots?

• Numbers indicate sets of continuously stable states. • Decimals indicate transition costs.

Node

-15º

Tipping points

Center of mass

Support Polygon

Projection onto horizontal

Stability depends on center of mass location

A. Find side of the hull that the robot is resting on

B. Project this onto the horizontalC. Where is the Center of mass,

in the projection? • Above projection =Stable• Edge of projection =Tipping

Point• Not above projection

=Unstable

2D depiction of Self-righting Validation Platform

Objectives

Impact

Past ARL Work and Challenges

Conformation Space Map(Grows exponentially)

Directed Graph Illustration Structure(Smaller subspaces)

2D swing Consider a program:

• Analyzes each joint motion in one degree increments, each joint range is100 degrees

• Analyzing each state 1 ms.• Single DOF system 0.1 s.• 4 DOF system 100,000 s.• 6 DOF system 10^9 s, or 31+

years to compute!

Exhaustive Approach & Issues

Technical ApproachProbabilistic Road Maps (PRM) Rapidly Exploring Random Trees (RRT)

• Both PRM and RRT use a distance function to measure the effective displacement between two points in configuration space

• Now consider: Distance from one orientation to another

• Point 1 = Current• Point 2 = an orientation Δ toward the goal• Repeat

White Box TestingFlowchart Flow Graph

If/else statements

Programming strategy:• Imagine numbers on

the PRM method at every point

• The white box testing method gives a way to eliminate any extra information before testing the entire path.

• If a common number is met, drop the longer paths immediately

• This will guarantee the most optimized outcome with the available data.

2

1

3

4

5

0.1 0.2

0.5

0.1

0.1

0.10.1

Set stop at 6, Test legs of the code simultaneously, eliminate all but the lowest cost to 6 right away6

Discussion &

Conclusions

Path ForwardConcave And

Convex GroundDynamic Motions

Grows exponentially as degree of freedom

increases

1