High-bandwidth sensors stress the data path long before algorithms become the bottleneck: buffering, framing and sustained throughput determine whether frames/packets arrive reliably under load. FPGA-based streaming pipelines provide lossless acquisition, deterministic buffering/backpressure handling and robust transport toward the compute platform. This keeps the perception system stable at sustained data rates and makes bandwidth limits measurable via built-in diagnostics. The result is a predictable, observable data backbone feeding SoC/GPU resources.

Navigation and control benefit from preprocessing that is time-deterministic and close to the signal source: alignment, filtering, gating and event detection should not depend on CPU scheduling. FPGA-based pipelines provide fixed-latency conditioning and feature extraction that reduces data volume and improves real-time behavior for downstream fusion and control loops. This is especially relevant when multiple sensors must remain phase-aligned or when triggers must be repeatable. The processing scope is typically kept modular and verifiable, focused on what improves timing and stability.

Mobile systems require fast, predictable reactions when something goes wrong: communication loss, sensor plausibility violations, limit breaches or actuator faults should lead to defined states independent of the main compute stack. FPGA-based supervision implements deterministic watchdogs, limit checks, interlocks and inhibit paths with well-defined behavior on reset and power events. This reduces common-mode failures between control software and safety reactions and makes fault handling reproducible during testing. The result is a clear separation between mission logic and a minimal, robust supervision layer.