Quick Fixes:

Sensors, Perception and Sensor Connectivity

Modern robotics and automation systems rely on a diverse array of sensing technologies to navigate, perceive, and interact with their environments. As the range of available sensors expands, engineers face important decisions about which solutions best fit their application’s requirements, from cost and complexity to accuracy and robustness. The following section addresses common questions about sensor selection, integration, and connectivity, offering practical guidance on achieving reliable perception and efficient data communication in real-world robotics systems.

Can I replace LiDAR with other sensors and still achieve similar results?

In some cases, yes. ToF cameras, stereo vision and radar can provide depth information, but LiDAR remains superior for long-range accuracy. Many robots use sensor fusion to approach LiDAR-like robustness without relying on LiDAR alone.

If I can only use two sensors, which should I choose?

A common minimal combination is a depth-capable sensor (ToF or stereo camera) plus an IMU. This supports basic perception and motion estimation with low system complexity.

How do ToF sensors create depth maps?

ToF sensors measure the time or phase shift of reflected light to calculate per-pixel distance. Reliable depth maps require calibration, ambient-light suppression and post-processing.

How do gyroscope, accelerometer and magnetometer work together to reduce IMU drift?

The gyroscope tracks short-term motion, the accelerometer stabilises orientation using gravity, and the magnetometer corrects heading. Sensor fusion algorithms combine all three to minimise drift over time.

Which communication protocol should I use for my sensors?

Protocol choice depends on bandwidth and determinism: I²C/SPI for short-range links, UART for simple devices, Ethernet for high data rates, CAN/CAN-FD for industrial robustness, and TSN for deterministic Ethernet.

Is it common to mix different sensor interfaces?

Yes. Most robots use multiple protocols and aggregate sensor data through gateways or compute platforms that normalise timing and data flow.

3 Unstoppable Trends

1. The Mandate for Explicit Functional Safety and Compliance

The rise of cobots and complex autonomous systems mandates rigorous adherence to updated global safety standards (e.g., ANSI/A3 R15.06-2025).

Safety standards now integrate cybersecurity and dynamic monitoring (SSM). Platforms must support safety-certified processing units and utilize robust, decoupled functional safety stacks (like ROS 2 practices) to enforce limits via reliable hardware interrupts.

2. The Dominance of Hardware-Software Co-Design for Efficiency

As multi-modal AI is pushed to the edge (Physical AI), performance hinges on efficient data movement and specialized hardware, not just raw GPU power.

Leverage dedicated tensor cores and integrate tools like NVIDIA TensorRT for INT8/INT4 quantization, alongside Dynamic Voltage and Frequency Scaling (DVFS), to maximize throughput per watt and minimize costly external memory access.

3. The Need for Interoperable Software and Simulation-First Development

Future-proofing requires ecosystems that facilitate rapid iteration and deployment across heterogeneous hardware environments.

Adoption of ROS 2 is essential for real-time, interoperable middleware. Utilize Digital Twins and tools like NVIDIA Omniverse and NVIDIA Isaac ROS to validate power profiles and performance before fabrication, ensuring Sim-to-Real scalability across the Jetson module family.