Ultimately, these use cases are enabled
by the convergence of AI and advanced
sensing and control. Specifically, AMRs
rely on position and torque sensors,
image sensors, ultrasonic sensors, and
intelligent motor control to operate
safely around people and objects.
Torque sensing, for example, helps
guarantee safety by detecting resistance
or collisions, and also enables training
through guided manipulation. This kind of
physical feedback allows an AMR to learn
tasks through demonstration and adapt
to user input without reprogramming.
Naturally, human-robot collaboration
is now a design focus. For AMRs to
function in spaces designed for people,
they must sense and respond to the
physical world in ways that mimic human
awareness. This requirement is now
driving the development of humanoid
AMRs equipped with legs, arms, and
dexterous manipulators to perform tasks
in the human-made world. Such platforms
will rely on multi-modal sensor fusion
and real-time AI to navigate complex
layouts and interact with tools or objects
designed for human hands.
onsemi supports emerging AMR use
cases by
• Enabling safe and responsive human-
robot interaction through integrated
position and inductive sensing
• Providing scalable component platforms
suited for AMRs operating outside
traditional industrial boundaries
• Supporting modular robot architectures
adaptable to varied tasks across
logistics, healthcare, and agriculture
C h a p t e r 1 | A M R A p p l i c a t i o n s a n d I n d u s t r y I m p a c t
AMRs can automate intralogistics
workflows by transferring components
and raw materials between cells, lines,
or departments along fixed routes.
They can also handle on-demand
deliveries from storage based on
production requests and move Work-
In-Progress (WIP) parts between
production cells and lines."
Manoj Kumar S
Sr. Robotics System Engineer, Universal Robots
8
Engineering the Future: The Sensors and Systems Powering Modern Mobile Robots
