Autonomous Systems: Systems Architecture, Multi-Sensor Perception, and Intelligent Control Dynamics

Abstract

This comprehensive foundational curriculum module examines the cyber-physical systems architecture, state estimation theory, and feedback control loops of modern autonomous systems within the Prep4Uni open STEM curriculum. Core Thematic & Pedagogical Foundations: Systems-Level IDEF0 Functional Modeling: Deconstructs autonomous systems education across empirical inputs (automated case studies, dynamic real-world challenges), governing controls (safety verification standards, ISO 26262 frameworks, performance metrics), executing mechanisms (laboratory testbeds, telemetry data, industry mentorship), and verifiable technical outputs. Closed-Loop Autonomy Pipeline: Detailed technical breakdown across five core operational phases: exteroceptive and proprioceptive multi-modal sensing, real-time edge data processing and state estimation ($\mathbf{x}_t$), path planning under non-holonomic kinematic constraints (A*, RRT*, Model Predictive Control), self-adaptation via fleet learning, and high-torque electromechanical actuator execution. Multi-Sensor Perception & Fusion: Comparative trade-off analysis across optical cameras, solid-state LiDAR point clouds, Doppler RADAR, and ultrasonic arrays, evaluating performance boundaries under varying weather and environmental attenuation. Detailed Industrial Case Studies: Grounded engineering analyses of consumer automated driving (Tesla Vision and fleet neural networks), high-altitude long-endurance aerospace robotics (MQ-9 Reaper UAV), and extraterrestrial planetary exploration (NASA Perseverance rover TRN and AutoNav pipelines in Mars's Jezero Crater). Stopping Distance & Reaction Dynamics: Theoretical foundations and interactive stopping time mechanics modeling perception delays, computing latency, and braking friction coefficients. Quantitative Engineering Computations: Worked step-by-step mathematical problem sets covering UAV headwind transit times, sensor polling rates, sensor fusion accuracy gains, battery power optimization scaling, delivery fleet path reductions, super-resolution camera metrics, robotic assembly shift throughput, and high-speed LiDAR scan coverage.