Broxme

Robotics intelligence & embodied AI.

Our research focuses on how robots perceive their surroundings, make decisions, and act. We use indoor navigation to study perception, planning, and control.

See what we tested See our next tests
Engineering evidence
Controlled software tests
Next milestone
Controlled hardware trials planned

Indoor navigation Blocked route

Run the route

Scripted simulation

Start the virtual rover and watch it respond to a barrier.

Ready at the start of the route.

Engineering evidence

What we tested in simulation.

These are observations from controlled tests in the Studio engineering build. Each test isolates one behavior with simulated inputs. The browser scene illustrates the same kind of problem using a scripted route. Reproduce the browser example.

Route planning
The A* planner found a route through an opening in a grid of obstacles and rejected a diagonal shortcut through a blocked corner.
Obstacle response
Simulated distance readings triggered a turn toward clearer space and a zero-motion command at the configured stop threshold.
Localization
Correcting the position estimate with simulated landmark readings reduced the final position error in a test with drifting wheel odometry.
Line tracking
The controller corrected its steering when it drifted off-center and entered search mode when the simulated line disappeared.
Simulation and replay
Identical inputs produced matching simulation and sensor outputs. Replay comparison detected a change when the simulated state differed.

Research priorities.

Indoor navigation gives us a shared test case for three questions about how robots behave in changing environments.

Robot perception and planning

Can a robot choose a useful next action when its route changes? We’re studying how to trace a decision from the sensor readings through the planner to the motion command.

Embodied AI

Can learning improve how a robot recovers when its original plan fails? We plan to compare a learned policy with our rule-based approach on the same scenarios.

Indoor robot navigation

Where does a response seen in simulation differ on a physical robot? A blocked aisle gives us a repeatable way to compare stopping, rerouting, and reaching a goal.

Planned work

From a virtual route to a physical test.

We’ll start with one indoor mobile robot and one controlled course. The goal is to find which simulated behaviors carry over and turn the differences into repeatable tests.

What we’ll measure

  • Goal completion across trials
  • Time and distance to stop
  • Time to resume movement
  • Operator interventions

  1. Establish the simulation baseline

    Fix the start, goal, and obstacle location. Record what the robot senses, which route it selects, and what motion it commands in repeated runs.

  2. Repeat the scenario on hardware

    Use the same course and compare the physical robot’s response with the simulated runs. Document where they agree and where the robot needs help.

  3. Use failures to improve the next run

    Turn observed failures into regression tests. Use those scenarios to evaluate whether a learned recovery policy improves on the rule-based baseline.

Technical references.

External documentation and research for readers following indoor navigation, simulation, and embodied AI.

Bring us a navigation problem.

If you build indoor mobile robots, tell us about a behavior you find hard to reproduce. We’re interested in working with teams on controlled tests.

Contact Broxme