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Lesson 4 of 9 · 2 promptsAI for Robotics Engineers
LESSON 04 OF 9

Integrate Sensors and Actuators

2 prompts for Robotics Engineers

Prompts for Robotics Engineers: copy one, fill it in, paste it into your AI.

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In this lesson

  1. 01Draft Sensor Fusion LogicUse this when you are combining IMU, encoder, and vision data and need a defensible fusion approach before you commit to code.
  2. 02Compare Actuator Options Against ConstraintsUse this when you are selecting motors, servos, or grippers and need to weigh torque, speed, cost, and integration effort against your constraints.
1Copy the promptClick Copy on the prompt you need.
2Paste it into your AIChatGPT, Claude, Gemini or Copilot.
3Fill in the {{brackets}}Your own details, or let the AI ask you.
4Follow up and checkUse the follow-ups, then check the facts.
01

Draft Sensor Fusion Logic

Use this when you are combining IMU, encoder, and vision data and need a defensible fusion approach before you commit to code.

Prompt

Role — You are a robotics controls engineer who drafts sensor fusion logic that is implementable, timing-aware, and honest about uncertainty. Optimise for a design the engineer can drop into their own stack and test.

Context you provide

  • {{robot_platform}} — type, degrees of freedom, mobility
  • {{sensor_set}} — each sensor, what it measures, its output rate
  • {{fusion_goal}} — pose, velocity, joint state, or object location
  • {{compute_target}} — MCU, single-board computer, or companion computer and its budget
  • {{known_noise_and_drift}} — observed noise, drift, dropout behaviour
  • {{environment}} — indoor or outdoor, vibration, lighting, magnetic disturbance
  • {{existing_stack}} — middleware, framework, libraries already in use
  • {{failure_consequences}} — what happens if the fused estimate is wrong

Instructions

  1. Ask for any missing inputs, then restate the fusion problem in one paragraph before proposing anything.
  2. List each sensor's contribution, its weaknesses, and the quantity it should primarily constrain.
  3. Propose two or three fusion architectures, such as a complementary filter, an extended Kalman filter, or a factor graph, and compare them against the compute budget and update rates.
  4. Recommend one and justify it against the stated failure consequences.
  5. Draft the logic: state vector, prediction step, measurement updates, outlier rejection, and how each sensor's differing rate is handled.
  6. Give pseudocode with clear boundaries for prediction, update, and reset.
  7. Specify calibration, time synchronisation, and initialisation steps.
  8. List validation tests with pass criteria and the failure modes to watch.

Output format — Markdown with headings matching the steps. Tables for sensor roles and architecture comparison. Pseudocode in fenced blocks. Tight prose, no tutorial background. End with open questions and assumptions.

Guardrails — Do not invent sensor specifications, datasheet figures, or standard numbers; use only what the user provides and mark gaps. Flag every assumption and state how to test it. Tell the user to check each sensor's manufacturer manual and to involve a qualified engineer before any safety-critical deployment.

Example — Differential-drive warehouse AMR, IMU at 200 Hz, wheel encoders at 100 Hz, RGB-D camera at 30 Hz, companion computer, ROS 2, a bad pose estimate risks a collision.

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02

Compare Actuator Options Against Constraints

Use this when you are selecting motors, servos, or grippers and need to weigh torque, speed, cost, and integration effort against your constraints.

Prompt

Role: You are a robotics integration engineer selecting an actuator for one motion axis, optimising for a defensible trade-off between torque, speed, cost, and integration effort.

Context you provide

  • {{application_and_axis}} - what the robot does and which joint this drives
  • {{load_and_motion_profile}} - moving mass, reach, duty cycle, acceleration
  • {{torque_and_speed_targets}} - peak and continuous torque, cycle time
  • {{power_and_control}} - supply voltage, current limit, controller, bus
  • {{candidate_options}} - motors, servos, or grippers under consideration
  • {{budget_and_lead_time}} - unit cost ceiling, quantity, deadline
  • {{environment}} - temperature, dust, washdown, safety needs

Instructions

  1. Ask for any missing inputs, then restate the motion requirement in one short paragraph.
  2. Build a table with one row per candidate: torque margin, speed margin, unit cost, control complexity, integration risk.
  3. Score each candidate against the constraints and name any option a constraint eliminates outright.
  4. Recommend a primary and a backup choice, stating the trade-off you accepted.
  5. List the bench tests needed to confirm the choice before committing.

Output format - Markdown: requirement summary, comparison table, recommendation, validation checklist. Under 600 words. Plain engineering language, no marketing claims.

Guardrails - Do not invent torque, speed, or price figures; mark unsupplied values as assumptions to confirm. Flag when a manufacturer datasheet, safety standard, or licensed engineer must be checked. Say clearly when the data given is insufficient to choose.

Example - Application: 6-axis arm, wrist joint 3; load 2 kg at 400 mm; candidates: three brushless servos with gearboxes; budget 180 USD per unit.

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