While developing a mobile robot for warehouse navigation, the team needed a controller for precise velocity tracking.

Select a control strategy that balances performance and computational load.

Described PID as a time‑domain, error‑based controller easy to implement and tune for single‑input single‑output systems. Explained LQR as a state‑space optimal controller that minimizes a quadratic cost function, requiring a model of the system dynamics. Compared tuning complexity, robustness, and suitability for multi‑variable systems.

Recommended PID for the low‑cost prototype due to simplicity, and LQR for the final product where a full dynamic model existed, resulting in smoother trajectories and reduced overshoot.

Designing a 6‑DOF cobot for assembly line assistance where safety and precision were critical.

Choose actuators that meet performance, safety, and cost requirements.

Identified torque and speed requirements, evaluated series‑elastic actuators for compliance, considered back‑drivable motors to reduce injury risk, reviewed encoder resolution for accurate positioning, and assessed thermal management and maintenance intervals.

Selected series‑elastic drives with integrated torque sensors, achieving safe interaction forces under 10 N while maintaining ±0.1 mm positioning accuracy, leading to successful pilot deployment.

Tasked with creating a navigation system for an autonomous forklift operating in a busy warehouse.

Develop a perception pipeline that reliably identifies pallets, static obstacles, and moving humans in real time.

Outlined sensor placement (mounted RGB‑D on mast), preprocessing (depth denoising, point‑cloud generation), object detection using a pretrained YOLOv5 model fine‑tuned on pallet images, obstacle segmentation via voxel‑grid clustering, and human detection using OpenPose on RGB frames. Integrated sensor fusion with Kalman filtering to track dynamic entities and generate occupancy maps.

Achieved 95 % pallet detection accuracy, sub‑0.2 m obstacle avoidance margin, and safe human‑robot interaction with a 0.5 s reaction time, enabling 20 % increase in throughput during pilot trials.

Leading the control software for a fleet of inspection drones that operate in harsh industrial environments.

Ensure the system continues operating despite node failures or communication drops.

Proposed a micro‑service architecture with ROS2 DDS for reliable publish/subscribe, implemented health‑check heartbeats, used redundant master nodes with leader election (Raft algorithm), and added state replication via CRDTs. Designed fallback local controllers that can take over if the central planner is unreachable, and incorporated watchdog timers to reset faulty modules.

System maintained 99.8 % uptime over six months, with automatic failover handling 12 simulated node crashes without mission interruption.

During a product line upgrade, the mechanical team preferred upgrading existing PLC‑controlled stations, while the software team advocated for a new ROS‑based robot.

Secure approval for the ROS platform within a limited R&D budget.

Prepared a cost‑benefit analysis highlighting long‑term savings from modular software, demonstrated a rapid prototype that reduced cycle time by 30 %, and organized workshops to address concerns about training and integration.

Management approved a phased rollout, saving $150 k annually and delivering a 25 % increase in production speed within the first year.

While integrating a vision system into an autonomous guided vehicle (AGV), the camera firmware caused intermittent frame drops.

Deliver a reliable perception module on schedule.

Conducted root‑cause analysis, identified a driver incompatibility with the real‑time OS, collaborated with the vendor to release a patched driver, and implemented a fallback frame‑buffer strategy. Kept stakeholders informed with daily status updates and adjusted the project timeline for testing.

Stabilized the vision pipeline, met the launch deadline, and the AGV achieved 99.5 % detection reliability in field trials.