Getting Ready to Ace Your Robotic Engineer Interview: The Top Technical Questions You’ll Get

If you have an interview coming up for a robotic engineer role you can expect to face a robust set of technical questions to assess your hard skills. Interviewers will want to understand your hands-on expertise in areas like robot design, control systems programming, and system integration.

To help you put your best foot forward, here’s an overview of some of the most common technical questions with examples of strong responses:

How Would You Design a Robust Control System for a Wheeled Mobile Robot?

Mobile robots like self-driving cars or warehouse transport bots need finely tuned control systems to navigate diverse unpredictable environments. For this question, interviewers want to see that you have practical experience engineering control systems and know what it takes to make them robust.

In your response, be sure to cover:

• The types of sensors you’d incorporate – lidar, cameras, wheel encoders etc.

• How you’d fuse sensor data to accurately track position and surroundings.

• The control algorithms you’d implement – PID, model predictive control etc.

• How you’d optimize system performance via simulation and testing.

• Strategies to ensure resilience – handling sensor failures, external disturbances etc.

Sample response:

“For a wheeled mobile robot, I’d design a control system that fuses data from lidar, stereoscopic cameras, IMUs, and wheel encoders to track the bot’s motion and environment. Sensor fusion algorithms like Kalman Filters would integrate the data into an accurate world model. The core control logic would leverage model predictive control to plan optimal trajectories while avoiding obstacles. I’d extensively simulate the system in MATLAB and gazebo to tune gains and refine performance. Varying real-world testing conditions would reveal the most robust tuning. To ensure resilience, I’d implement fallbacks like dead-reckoning using wheel encoders if vision sensors go down. Redundant systems provide robustness against failures.”

What Do You Consider When Selecting Sensors for a Robotic System?

Sensors are the eyes and ears of robots, crucial for understanding their surroundings. For this question, interviewers evaluate your knowledge of different sensor types and ability to select the optimal ones for an application.

In your answer, discuss factors like:

• The environment the robot will operate in.

• Key data needed – position, proximity, force etc.

• Sensor capabilities and limitations. Cost, accuracy, range etc.

• Power, size and processing constraints.

• How the data will be used – closed-loop control, environment mapping etc.

Sample response:

“The operating environment and specific data requirements are the key factors I consider when selecting sensors. For example, lidar provides excellent spatial mapping but doesn’t work underwater where sonar is better. Gyroscopes work when accelerometers fail. Robust solutions use complementary sensors. I also evaluate characteristics like resolution, sample rate, range limits, and susceptibility to noise. Power draw and physical size need to align with robot constraints too. Importantly, I select sensors tailored for how data will be used – whether basic feedback in a control loop or high accuracy mapping. The best outcome combines sensors optimized for the environment and data usage while meeting size, cost and interface requirements.”

How Do You Program a Robotic Arm to Perform a Pick-and-Place Operation?

This tests your expertise in robotic manipulation – an essential skillset. Be sure to demonstrate:

• Your understanding of forward and inverse kinematics models to plan joint motions.

• Using closed-loop control algorithms like PID to execute precise trajectories.

• How you’d integrate sensory feedback – force, torque, vision – to adapt to parts and environment.

• Your experience with programming languages like C++ and ROS to code the behaviors.

Sample response:

“To program a pick-and-place application, I’d model the arm’s kinematics to map end effector motions to required joint angles and velocities. For the pick, I’d generate a trajectory using cubic polynomial interpolation to smoothly move the arm above the part. A PID controller would track this path while gripper torque feedback ensures a stable grasp.

For placing, I’d use the inverse kinematics to guide the end effector to the drop location, applying force control to gently release the part. Computer vision and other sensors would provide feedback to adapt the motions based on actual part positions. Using C++ and ROS, I’d code the modular behaviors and sensory interfaces to enable robust and flexible pick-and-place functionality.”

Can You Explain How to Calibrate the Visual Servoing System of a Robot?

Visual servoing uses camera feedback for closed-loop control, requiring precise calibration. This tests your hands-on experience tuning these systems. In your response, be sure to cover:

• Camera intrinsic calibration – removing distortions using methods like Zhang’s algorithm.

• Extrinsic calibration to properly locate cameras on the robot using techniques like hand-eye calibration.

• How you’d optimize calibration via simulations before real-world trials.

• Strategies for re-calibration over time as components shift.

Sample response:

“Effective visual servoing starts with proper camera calibration. Using calibration patterns, I’d characterize intrinsic parameters like focal length and optical distortion through methods like Zhang’s technique. This models the camera’s internal characteristics for undistorting images.

Next, extrinsic calibration localizes the cameras on the robot’s body through hand-eye calibration techniques – having the robot ‘look’ at its end effector in different poses to map the camera frame to robot frame.

I’d refine the calibration virtually using simulated test motions and camera models prior to physical trials. Over time, calibration accuracy decays as cameras shift, requiring periodic re-calibration. One method is to incorporate mounting points that allow easy camera detachment when recalibration is needed after disturbances or component changes.”

These examples provide a sampling of the rigorous technical questions you may face as a robotic engineer candidate. Thorough preparation across all core domains – from mechanical design to programming – is key to showcasing your proficiency and readiness to take on these complex roles. Use these examples as inspiration but also anticipate questions tailored to the specific technologies and applications relevant to the role. With practice and a mastery of fundamentals, you’ll be primed for success on interview day!