Faster path planning for rubble-roving robots

Date:
August 13, 2021
Source:
University of Michigan
Summary:
Robots that need to use their arms to make their way across
treacherous terrain just got a speed upgrade with a new path
planning approach. The improved algorithm path planning algorithm
found successful paths three times as often as standard algorithms,
while needing much less processing time.



FULL STORY ========================================================================== Robots that need to use their arms to make their way across treacherous
terrain just got a speed upgrade with a new path planning approach,
developed by University of Michigan researchers.


==========================================================================
The improved algorithm path planning algorithm found successful paths
three times as often as standard algorithms, while needing much less
processing time.

A new algorithm speeds up path planning for robots that use arm-like
appendages to maintain balance on treacherous terrain such as disaster
areas or construction sites, U-M researchers have shown. The improved
path planning algorithm found successful paths three times as often as
standard algorithms, while needing much less processing time.

"In a collapsed building or on very rough terrain, a robot won't always be
able to balance itself and move forward with just its feet," said Dmitry Berenson, associate professor of electrical and computer engineering
and core faculty at the Robotics Institute.

"You need new algorithms to figure out where to put both feet and
hands. You need to coordinate all these limbs together to maintain
stability, and what that boils down to is a very difficult problem."
The research enables robots to determine how difficult the terrain is
before calculating a successful path forward, which might include bracing
on the wall with one or two hands while taking the next step forward.



========================================================================== "First, we used machine learning to train the robot on the different ways
it can place its hands and feet to maintain balance and make progress,"
said Yu- Chi Lin, recent robotics Ph.D. graduate and software engineer
at Nuro Inc.

"Then, when placed in a new, complex environment, the robot can use
what it learned to determine how traversable a path is, allowing it to
find a path to the goal much faster." However, even when using this traversability estimate, it is still time- consuming to plan a long path
using traditional planning algorithms.

"If we tried to find all the hand and foot locations over a long path,
it would take a very long time," Berenson said.

So, the team used a "divide-and-conquer" approach, splitting a path into
tough- to-traverse sections, where they can apply their learning-based
method, and easier-to-traverse sections, where a simpler path planning
method works better.

"That sounds simple, but it's really hard to know how to split up that
problem correctly, and which planning method to use for each segment,"
Lin said.



==========================================================================
To do this, they need a geometric model of the entire environment. This
could be achieved in practice with a flying drone that scouts ahead of
the robot.

In a virtual experiment with a humanoid robot in a corridor of rubble, the team's method outperformed previous methods in both success and total time
to plan -- important when quick action is needed in disaster scenarios.

Specifically, over 50 trials, their method reached the goal 84% of the
time compared to 26% for the basic path planner, and took just over two
minutes to plan compared to over three minutes for the basic path planner.

The team also showcased their method's ability to work on a real world,
mobile manipulator -- a wheeled robot with a torso and two arms. With
the base of the robot placed on a steep ramp, it had to use its "hands"
to brace itself on an uneven surface as it moved. The robot utilized the
team's method to plan a path in just over a tenth of a second, compared
to over 3.5 seconds with the basic path planner.

In future work, the team hopes to incorporate dynamically stable motion, similar to the natural movement of humans and animals, which would free
the robot from having to be constantly in balance, and could improve
its speed of movement.

The paper describing the work was published in Autonomous Robots. Funding
for the research was provided by the Office of Naval Research (N00014-17-1-2050).

========================================================================== Story Source: Materials provided by University_of_Michigan. Note:
Content may be edited for style and length.


========================================================================== Related Multimedia:
* YouTube_video:_Long-horizon_humanoid_navigation_planning_using
traversability_estimates_and_previous_experience ========================================================================== Journal Reference:
1. Yu-Chi Lin, Dmitry Berenson. Long-horizon humanoid navigation
planning
using traversability estimates and previous experience. Autonomous
Robots, 2021; DOI: 10.1007/s10514-021-09996-3 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/08/210813151949.htm

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