Next-Gen Navigation Systems Change
the Scope of Robot Deployment R O B
Robots are all around us every day, in automated gas pumps, bank ATMs, and self-service check- out lanes – machines are already
automating our world. They are also working behind the scenes, and now, navigation
systems have made them more clever and
Mobility presents one major challenge to safety:
operation in dynamic, unstructured environments
where objects, people, and even physical infrastructure, like walls, move with different frequencies in
With many robotic systems, magnetic tracks, lines,
mirrors, or beacons must be installed in order to create an infrastructure for the robot to be piloted within.
However, the effort to install these beacons is costly
and time consuming.
Even for the more sophisticated robots that do not
require these lines, tracks, or beacons, there is an
expensive mapping phase involved in acclimating
a robot to its environment. This process uses SLAM
(simultaneous location and mapping) – the academic
blue-ribbon algorithm for world mapping – which
creates a static map at the time of deployment.
However, as the robot navigates its world over time,
there becomes a gap between the true
world state and the original map due to
moving objects. Thus, the robot’s perfor-
mance can slowly degrade over time.
The method for deploying a robot is
changing. The next-generation navigation system requires zero infrastructure
and practically eliminates the need for
extensive mapping that has become so
commonplace for the robotics industry.
No longer will a human have to do a tremendous amount of work to integrate a
robot within its work environment.
Robots will dynamically learn about the
environment that they are in, removing
the concept of the static map and elimi-
nating the challenge of keeping up with
mapping. The robot learns and applies its
knowledge and constantly updates itself
dynamically to increase its performance.
For instance, if you move a couch in
a hospital waiting room, the robot will
sense it, even going a step further to
acknowledge that it has not moved in some
time. It deduces that it will likely be in this same
place and accounts for that in future trips. There are
some obstacles in the environment that are very
dynamic, like a human walking down a hallway, and
there are some obstacles that are semi-dynamic, like
a chair, and yet others that are static, like a wall or
door. It is important that the robotic system under-
stands the differences between those types of objects
and does not treat them the same way.
By Daniel Theobald, Vecna Co-Founder & CTO