Material Detail

This university-level open learning module provides a rigorous mathematical and computational foundation in Warehouse Logistics and Autonomous Mobile Robots (AMRs). The module covers non-holonomic mobile kinematics, multi-agent operations research, indoor localization, and modern collision-avoidance control architectures:

• Differential-Drive Planar Kinematics: Non-holonomic unicycle forward and inverse kinematics, mapping body velocities from independent wheel speeds, and zero-radius turning mechanics for narrow picking aisles.

• Fleet Scheduling & Operations Research: Mathematical formulations for minimizing total flow time and makespan across multi-robot teams, predictive battery state-of-charge replenishment, and dynamic task assignment.

• Obstacle Avoidance & Spatiotemporal Safety: Euclidean distance separation constraints, dynamic stopping footprints, and multi-robot conflict mitigation.

• Indoor Industrial Localization: Overcoming GPS-denied environments through multi-sensor fusion of 2D/3D LiDAR SLAM, optical ground fiducial markers (QR/DataMatrix), and extended Kalman filter (EKF) wheel odometry.

• 2026 AI-Era Computational Paradigms: Multi-Agent Path Finding (MAPF) solved via Conflict-Based Search (CBS) in 3D spacetime (x, y, t) and provably safe pedestrian interaction via Control Barrier Functions (CBFs) evaluated through real-time Quadratic Programming (QP).

• Operational Bottlenecks & Failure Modes: Rollover tipping dynamics of elevated inventory pods (2.5 m) under centrifugal acceleration, circular wait deadlocks in single-lane aisles, blind-corner LiDAR occlusions, and cold-chain (-20°C) electrochemical battery impedance scaling.

Pedagogical components include 10 conceptual review questions, 12 thought-provoking analytical problems, and 12 fully worked step-by-step numerical engineering derivations.

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