This document describes a reactive path deformation (RDP) algorithm for navigation among dynamic obstacles. It presents a simulator that implements RDP for local robotic motion planning in unpredictable environments. RDP works by reactively deforming a local patch of the globally planned path when obstacles are detected near the path. Forces are simulated to deform the path away from obstacles while maintaining the overall path shape. The simulator allows setting start/goal positions and adding random obstacles. It uses multi-threading for sensing, planning and control. RDP enables cheaper replanning than recomputing a full new path and prevents getting stuck at local minima. Future work aims to use real sensor data and handle uncertainty.

1. Reactive Deformation of
Path for Navigation Among
Dynamic Obstacles
(RDP NADO)
Anand Taralika
College of Computing
Georgia Tech
Atlanta USA

2. 2
The Title
• What?
– A simulator for a reactive planning algorithm
• Why?
– For local robotic motion / path planning
– For navigation in an environment that is
• Dynamic
• Unpredictable
• How?
– Reactively deforming a local patch of the global planned
path

3. 3
Demo

4. 4
The Navigation Algorithm
Global Planner (A*)
Navigate to next
waypoint
Is the next
waypoint
reachable
?
Is this a
static
environm
ent?
Failure: No
feasible path
Is there an
obstacle
“close” to
the path?
Invoke RDP for obstacle
avoidance (Local Planner)
Yes
No
No
Yes
YesNo
Current Robot Config.
Final Robot Config.
Obstacle Positions

5. 5
RDP Algorithm
• Global Planning

6. 6
RDP Algorithm
• Principle of path deformation
• The obstacle has a charge opposite to the charge on the
path
Not optimal
anymore!
Optimal Path

7. 7
RDP Algorithm
• The forces acting on the path are:
– Internal contraction force, Fi
– External repulsive force, Fe
• Simulates tension in the path
• Used to determine if the elastic limit of the path
is reached before it snaps.
• Deformation stops when equilibrium is attained,
that is when Fi = Fe

8. 8
RDP Algorithm
• Forces make the path taut

9. 9
RDP Algorithm
• Deformation of the trajectory for the robot to pass
through available space

10. 10
RDP Algorithm
• Trajectory regains shape by retraction

11. 11
RDP Algorithm
• Trajectory regains shape by snapping

12. 12
Simulator Implementation
• Implemented in C++
• User can set
– Start position of the robot
– Desired goal position for the robot
• User can also disperse obstacles by mouse
gestures easily in the environment
• Obstacles follow a random / unknown trajectory
which would be impossible to predict
• Global planning is implemented using A* in C++

13. 13
Simulator Implementation
• Multi-threaded model
– Sensor thread
• Monitors obstacles and their positions relative to the plan
• Detects changes in the environment and notifies the
Planner thread about the change.
– Local Planner thread
• Modifies the plan locally to accommodate the changes in
the environment
– Control thread
• Makes the robot navigate along the trajectory defined by
global and local planners

14. 14
Results
• A simulator implementing RDP algorithm was
developed
• The simulator was run with
– One mobile robot
– Varying number of dynamic obstacles
– In a 800x1000 sq. unit workspace
– At a path update rate between 10 and 100 Hz
• The more often the path is updated, the more
fine grained the control is, however, the
algorithm becomes more processor intensive

15. 15
Results
• Unforeseen obstacles invalidate a planned path
and replanning each time could be costly
– RDP is a cheaper alternative!
• RDP also prevents a robot from getting stuck at
local minima since it preserves the global nature
of the plan
• RDP is applied on-the-fly, without suspending
execution of the task

16. 16
Results
• What happens when there are large changes in
the shape?
– The robot might not be able to keep up
– Solution 1: Do not allow “large” changes
•Infeasible to find a good limit, so path might get
invalidated too often, making it less efficient
– Solution 2: No limit on changes, but merge with
the original path to smooth out the outliers

17. 17
Future Work
• To be used for navigation of a humanoid / mobile
robot in an unpredictable / dynamic environment
• Use real-time sensor data and generate path
deformations
• Coping with uncertainty / errors in sensor data
• Extend the simulator to elastic roadmaps that can
recover from invalidation of the global plan

18. 18
Acknowledgement / References
• Prof. Mike Stilman @ Humanoids Lab, Georgia Tech
– For advising on the topic and providing robots to test the
simulator
• S. Quinlan, and O. Khatib, “Elastic bands: Connecting path planning and control,” Proc.
of IEEE Conf. on Robotics and Automation, 1993.
• O. Brock and O. Khatib, “Elastic strips: A framework for motion generation in human
environments,” Int. Journal of Robotics Research, vol. 18, no. 6, pp. 1031–1052, 2002.
• O. Brock, and O. Kathib, “Elastic Strips: A framework for integrated planning and
execution”, Proceedings of the International Symposium on Experimental Robotics,
volume 250 of Lecture Notes in Control and Information Sciences, pp. 328-338, 1999.
• O. Khatib, “Real-Time Obstacle Avoidance for Manipulators and Mobile Robots”, IEEE
International Conference on Robotics and Automation, St. Louis, Missouri, pp. 500-505,
March 25-28, 1990.
• O. Khatib, “Towards integrated planning and control”, Proceedings of IFAC Symposium
on Robot Control, volume 1, pp 305-313, 1994.