Robotics Engineer Interview Questions

I would start by defining the requirements: payload capacity, reach, degrees of freedom, speed, and precision needed. Then I'd select an appropriate kinematic configuration (serial, parallel, SCARA, etc.) based on these requirements. Next, I'd perform kinematic analysis to verify the workspace and dynamic analysis to determine torque requirements at each joint. This would inform actuator selection, structural design, and material choices. I'd use CAD software to create detailed designs, perform FEA to validate structural integrity, and iterate based on simulation results. Finally, I'd consider manufacturing constraints, assembly procedures, and maintenance access in the final design.

Forward kinematics determines the end-effector position and orientation given the joint angles or displacements, using the geometric relationships between links. It's used for direct position control and monitoring. Inverse kinematics calculates the joint angles needed to achieve a desired end-effector position and orientation, which is more complex and may have multiple solutions or no solutions. It's essential for task-space control, path planning, and programming robots to interact with their environment. In practice, I've implemented both approaches—forward kinematics for state estimation and inverse kinematics for trajectory following and object manipulation tasks.

I would use a systematic approach like Ziegler-Nichols or manual tuning. First, I'd set all gains to zero and gradually increase the proportional gain (Kp) until the system oscillates with constant amplitude, recording this as the critical gain. From there, I'd calculate initial PID parameters based on the tuning method. Then I'd fine-tune by adjusting Kp to improve response time without excessive overshoot, adding derivative gain (Kd) to dampen oscillations, and finally introducing integral gain (Ki) to eliminate steady-state error. Throughout the process, I'd monitor performance metrics like rise time, settling time, overshoot, and steady-state error. For complex systems, I might use model-based tuning or auto-tuning algorithms to optimize performance across the robot's operating range.

For grid-based environments, I'd use A* or Dijkstra's algorithm when optimality is important and the environment is well-defined. In high-dimensional configuration spaces, sampling-based planners like RRT (Rapidly-exploring Random Trees) or PRM (Probabilistic Roadmaps) are more efficient, with RRT better for single-query problems and PRM for multi-query scenarios. For dynamic environments, D* or anytime planners that can quickly replan are appropriate. Potential field methods work well for reactive obstacle avoidance but can suffer from local minima. For trajectory optimization with dynamic constraints, I'd use methods like CHOMP or TrajOpt. The choice depends on factors like dimensionality, real-time requirements, optimality needs, and environmental complexity. In my experience, hybrid approaches often work best—using sampling-based methods for global planning combined with local optimization techniques.

I would implement a multi-stage sensor fusion approach. First, I'd characterize each sensor's strengths, weaknesses, and error models. For probabilistic fusion, I'd typically use an Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF) for state estimation, combining odometry data (wheel encoders, IMU) with absolute positioning sensors (GPS, vision, LiDAR). For non-Gaussian noise distributions, particle filters might be more appropriate. I'd implement pre-processing to handle outlier rejection and synchronize data from sensors with different sampling rates. The fusion algorithm would weight sensor inputs based on their uncertainty and context—for example, relying more on vision in GPS-denied environments or IMU when wheel slip is detected. I'd also implement fault detection to identify sensor failures and adapt accordingly. The system would be validated in various environments to ensure robust performance across operating conditions.

When selecting sensors, I consider the robot's operating environment (indoor/outdoor, structured/unstructured), required perception range and resolution, power constraints, computational resources, and cost limitations. For localization, I evaluate whether odometry sensors (wheel encoders, IMU) need to be supplemented with absolute positioning (GPS, vision, LiDAR). For obstacle detection, I assess the detection range, field of view, and minimum detectable object size. Environmental factors like lighting conditions, weather resistance, and potential interference sources are critical. I also consider practical aspects like weight, size, power consumption, interface compatibility, and maintenance requirements. Finally, I evaluate the sensor data processing requirements and whether fusion with other sensors is needed. The optimal sensor suite balances these factors while meeting the robot's functional requirements and constraints.

I've used ROS extensively as a middleware for robotics development. In my projects, I've leveraged its publish-subscribe communication model to create modular, reusable components. I've developed custom ROS nodes for sensor interfaces, control algorithms, and behavior planning. I've used standard message types for interoperability and created custom messages for specialized data. For visualization and debugging, I've utilized RViz to display sensor data, robot state, and planning information. I've implemented navigation stacks using ROS Navigation, integrating mapping (gmapping, cartographer), localization (AMCL), and path planning. For manipulation tasks, I've used MoveIt for motion planning and control. I've also worked with ROS tools like rosbag for data recording/playback, rqt for GUI development, and roslaunch for system configuration. In multi-robot systems, I've implemented distributed communication using ROS master synchronization. I'm familiar with both ROS 1 and ROS 2, including the improvements in ROS 2 for real-time performance and security.

I approach debugging systematically, starting with problem isolation to determine whether the issue is mechanical, electrical, or software-related. For software issues, I use logging at various levels of detail to trace execution flow and state changes. I leverage visualization tools to inspect sensor data, control signals, and system state. For intermittent problems, I implement data recording to capture the system state when failures occur. I use unit tests to verify individual components and integration tests for subsystem interactions. For timing-related issues, I employ tools to analyze execution timing and identify bottlenecks. When dealing with hardware-software interaction problems, I use hardware-in-the-loop testing and signal analysis tools. I also follow a methodical process of forming hypotheses, designing experiments to test them, and iteratively narrowing down the root cause. Throughout debugging, I document findings and solutions to build institutional knowledge for future troubleshooting.