This document discusses research towards developing algorithms to enable robots to autonomously right themselves. It notes that proprioceptive joint and body orientation data is available but sensory data is limited. It aims to maximize robot self-righting abilities, improve performance, and transition analysis from 2D to 3D. Exhaustive search strategies are infeasible due to computational complexity. The document proposes using probabilistic roadmap and rapidly exploring random tree algorithms to simplify the search problem. It also suggests applying white box testing concepts to leverage program limits to identify optimal path plans more efficiently than exhaustive searches.

1. • 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 Neal
Majoring in Computer Science
Attending Chicago State University
ARL Mentor: Chad Kessens
Directorate/Division: VTD/ASD
Duration of Project: 06/06/16– 08/12/16
Approved for Public Release; Distribution Unlimited
Approved for Public Release;
Distribution Unlimited
Wider ApplicationsArmy Applications
Resting on its rear and
arm
Joint
• Numbers indicate sets of continuously stable states.
• Decimals indicate transition costs.
Node
-15º
Tipping points
Center of
mass
Support
Polygon
Projection onto
horizontal
A. Find side of the hull that the
robot is resting on
B. Project this onto the horizontal
C. 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!
Technical Approach
• 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
Flowchart 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
0.1
0.1
Set stop at 6, Test legs of the code simultaneously,
eliminate all but the lowest cost to 6 right away6
Discussion
&
Conclusions
Path Forward
Grows exponentially as
degree of freedom
increases
1