RAOP connects robotics and automation research with school teaching through guided inquiry-based learning. In 2026, eight educators worked in four pairs on assigned virtual platforms during July 6–17 and rotated through all four core platforms in the AVRC Laboratory during July 20–24.
The beginner investigations below are proposed learning activities from the educator guide. They emphasize sensors, actuators, commands, measurements, and explanations supported by data. Educators can adapt them for courses, curriculum units, after-school activities, and clubs; advanced control theory is an extension.
A Shared Guided Inquiry Process
The facilitator provides a focused question, a prepared model, and appropriate operating limits. Educators predict a response, investigate one approved change, record evidence, and explain the result.
Identify and predict. Name the components and distinguish the command from the measurement.
Investigate virtually. Use the assigned MATLAB/Simulink model and QLabs workspace to record a baseline and a controlled comparison.
Compare with hardware. Use a facilitator-tested model under supervision and explain similarities or differences in the records.
Connect to teaching. Identify a student task, the evidence of learning, and the resources needed in the school setting.
For physical work, confirm the platform configuration, operating area, and stop procedure with the facilitator. Follow the platform manual and keep clear of moving equipment.
Reading, Writing, and the Hardware Interface
Read a Measurement
A sensor measures a physical quantity, and the configured interface makes that measurement available to the application. Identify its source, units, scale, and reference before interpreting the data.
Write a Command
The application sends a command appropriate to the platform and operating mode. A motor voltage, a desired joint angle, and a wheel-speed command are different quantities.
QUARC supports real-time applications built from Simulink models. The selected hardware, target, and model determine the interface blocks. For example, an encoder-read block may return counts, while a prepared model may already provide a scaled angle. QUARC does not replace the amplifier, DAQ, or sensor.
Virtual Group 1
Qube-Servo 3
Motor Motion, Encoders, and Feedback
The inertia disk provides the beginner starting point; the pendulum is an extension.
Objective
Connect motor actuation with encoder measurements and use a short data record to explain rotation.
Guiding Question
How does the encoder record the motion of the inertia disk?
Components and Their Interaction
Components
DC motor, motor encoder, inertia disk or pendulum attachment, integrated data acquisition (DAQ), amplifier, USB connection, and external power supply. The pendulum attachment adds a second angle measurement.
Command path
A configured application sends a command through the DAQ and amplifier to the DC motor. The amplifier supplies motor power.
Measurement path
The motor encoder measures shaft rotation. Check whether the model displays raw counts or an angle already converted to degrees or radians.
Proposed Beginner Investigation
Identify the motor, encoder, command signal, and measured output in the assigned virtual model.
Predict the angle trace for a short, facilitator-approved movement; run the virtual trial and label the time and angle axes.
Compare one approved change and explain the difference using the plotted data.
Observe or repeat the corresponding supervised hardware trial with the inertia disk, then compare virtual and physical records.
Intended Outcome and Evidence
An annotated component or signal view, one labeled comparison plot, the test conditions, and a short explanation linking the encoder to the observed motion.
School Integration Idea
Use a prepared encoder data set to calculate fractions of a revolution and interpret an angle-versus-time graph. Students identify when the disk moved and support their answer with data.
Learning Check
Distinguish the command from the measurement and state the units and starting reference. Apply an encoder conversion only when the model has not already converted the counts.
Optional Extensions and Further Study
Compare a speed estimate from position data with a filtered estimate; describe reduced fluctuations and any delay or loss of detail.
Observe prepared pendulum swing-up and balancing activities. The pendulum adds a measured angle, not a second motor input.
Further study: response modeling, parameter estimation, stability, proportional/PD control, and robustness.
Rotor orientation and axis configuration determine the available motion.
Objective
Distinguish rotor rotation from body motion and identify the measurements used to describe each.
Guiding Question
Which signal describes rotor rotation, and which describes the angle of the body?
Components and Their Interaction
Components
Two DC motors and propellers, motor encoders, pitch and yaw encoders, an inertial measurement unit (IMU), and interface electronics. The USB configuration uses the QFLEX 2 interface and DAQ.
Command path
The configured interface and amplifiers drive the motors. In a two-axis helicopter configuration, the two motor inputs can affect pitch and yaw.
Measurement path
Motor-related measurements describe rotor motion; pitch and yaw measurements describe body angles. Label these different physical quantities separately.
Proposed Beginner Investigation
Identify the rotors, available axes, and selected displays in the assigned virtual model.
Observe a predefined trial and record one rotor-related signal and one body-angle signal where both are available.
Label the units, predict the effect of a second approved trial, and compare the traces.
Observe or run the corresponding supervised hardware trial, recording the mechanical configuration and test conditions.
Intended Outcome and Evidence
A labeled component explanation, two identified signal traces, the selected configuration, and a conclusion supported by the measurements.
School Integration Idea
Use a short demonstration video and paired plots to distinguish motor rotation from pitch or yaw. Students label the signals and explain which measurement supports a statement about motion.
Learning Check
Explain why rotor speed and body angle are different quantities. Limit conclusions to the tested configuration and recorded conditions.
Optional Extensions and Further Study
Use prepared rotor-speed control models after the introductory interfacing activity.
Introduce multiple-input, multiple-output (MIMO) behavior by identifying two motor inputs and two body-angle measurements.
Further study: block diagrams, rotor response modeling, parameter estimation, filtering, PID control, and gain scheduling.
Joint-position feedback and camera information are separate measurement paths.
Objective
Connect a joint command with its measured position and explain the role of sensing in a simple object-handling sequence.
Guiding Question
How can we check whether a joint reached its commanded position?
Components and Their Interaction
Components
Base, shoulder, elbow, and wrist joints; servo motors; gripper; and an RGBD camera providing color and depth information.
Command path
In position mode, a desired joint angle is sent through the configured interface. The joint’s built-in servo controller uses feedback to regulate its motion.
Measurement path
Joint-position feedback reports the arm’s motion; camera data describes the scene through a separate connection. Check the units provided by the selected model.
Proposed Beginner Investigation
Identify the four joints and gripper, then locate a commanded position and its corresponding measurement.
Predict the motion for a facilitator-provided position command and compare the commanded and measured values in the digital twin.
Arrange a short approach, grip, move, and release sequence using approved positions.
Observe the corresponding supervised hardware sequence and explain where measurements help verify progress.
Intended Outcome and Evidence
An annotated action sequence, a labeled joint-position record, and a short explanation of the gripper’s role and the available sensing.
School Integration Idea
Use action cards, a digital twin, or a recorded demonstration to plan and revise an object-moving sequence. Students connect logical order and geometry with checks of measured position.
Learning Check
Identify a command, joint, and measurement. A sequence using known positions does not by itself show camera-based object recognition.
Optional Extensions and Further Study
Use prepared trajectory-generation and pick-and-place activities to investigate motion sequences.
Compare camera observations with known object locations; introduce vision processing only when it is implemented in the selected activity.
Further study: forward/inverse kinematics, velocity manipulation, and custom control. PWM mode is a separate advanced pathway, not the beginner position-mode activity.
Wheel measurements and environment sensors provide different kinds of information.
Objective
Relate a short movement command to wheel measurements and compare information from the available sensors.
Guiding Question
What evidence tells us how the robot moved and what it could detect?
Components and Their Interaction
Components
Two drive wheels and encoders, motor electronics, an onboard computer, batteries, cameras, LiDAR, and an IMU.
Command path
The configured application exchanges movement commands and measurements with the QBot driver. The assigned example determines where the application runs and how it communicates over the network.
Measurement path
Wheel encoders measure rotation. Cameras, LiDAR, and the IMU provide image, range, or inertial information. Wheel-based travel estimates can differ from actual motion when wheels slip.
Proposed Beginner Investigation
Identify forward and turning controls, wheel measurements, and available sensor displays in the assigned virtual model.
Predict and run a short approved movement-and-stop sequence, then compare wheel records with the observed motion.
Compare the same scene using two available displays, such as a camera image and a LiDAR scan, and explain what each reveals.
Observe or repeat the corresponding supervised hardware trial within the designated operating area.
Intended Outcome and Evidence
A movement sequence, a labeled wheel or motion record, selected sensor views, and an explanation of one observation and one limitation.
School Integration Idea
Combine a floor plan, a short robot video, and recorded sensor views. Students trace the route, identify a possible obstacle, and justify which measurement supports their interpretation.
Learning Check
Use the model’s sign conventions when explaining wheel motion. Distinguish manual control, a prepared sequence, and an implemented autonomous behavior.
Optional Extensions and Further Study
Compare left and right wheel-speed records during straight travel and turning.
Compare forward and downward camera views, or depth-camera and LiDAR displays, using the same identified scene.
Further study: sensing and perception, navigation algorithms, and multi-robot coordination.
The 2026 experience also included an advanced QDrone 2 mission-control demonstration in the Autonomous Vehicle Research Studio using OptiTrack localization and Motive software. This was a demonstration beyond the four core platform rotations. The control laboratory also includes the mounted 3 DOF Hover platform for further study of coupled rotational motion.
Activity Records and Reflection
Keep a brief record of the question, configuration, prediction, test conditions, labeled measurements, and conclusion. Identify whether evidence comes from a digital twin, a physical trial, or a supplied recording. Include one possible school adaptation and the resources it would require.
T2 documents virtual work and T3 documents on-site hardware experience. T4 gathers participant feedback; T5 supports group reflection and school integration planning. These records help the team refine the curriculum; proposed classroom activities remain distinct from documented school implementation.
Technical Resources and Preparation
Use the RAOP team’s assigned model and platform-specific quick-start guide. Confirm software versions, required toolboxes, QLabs access, and hardware configuration before beginning. The 2026 program centered on MATLAB and Simulink; software requirements depend on the selected virtual or hardware pathway.
Downloading Resources and Additional Software References
Quanser recommends keeping its resource folders together in a local Documents/Quanser folder. Download the repository using Code → Download ZIP and extract the contents, or use Git:
Keep the folder structure intact so models can locate their supporting files. Follow the repository’s computer setup instructions for the selected platform and software pathway.
QLabs setup differs between MATLAB/Simulink and Python-only virtual workflows. Use the instructions for the assigned activity. Account availability and permitted classroom use are confirmed with the RAOP team.
Participant Support provides information about preparation and technical assistance.
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