During the MUSE exhibition, Infineon showcased a 360° environmental perception robot head Demo. This Demo integrated the robot's most basic 

multimodal perception capabilities (voice, radar, vision, and movement) onto a rotatable head platform, addressing the question: What components are 

needed for a robot to perceive its surrounding environment, and how do these components work together?

Demo scene: From environmental perception to movement execution

The robot head consists of four MEMS microphones, three millimeter-wave radars, a TOF depth camera, and a motor drive solution. The four microphones 

are for "listening", the three radars for "scanning", the TOF for "seeing", and the motors for "turning the head".

At the Demo site, engineers completed a simple interaction through a voice command: The microphone array first collects voice signals and determines the

 direction of the voice; the millimeter-wave radar synchronously scans the surrounding moving objects and outputs trajectories; when a target is detected 

moving, the motor drive turns the head in that direction; the TOF camera then outputs depth maps and 3D point clouds. The entire process forms a 

collaborative process from environmental perception, target tracking to movement execution.

The real-time response between radar detection results and head movement also demonstrates the collaboration between perception data and motion 

control.

Voice and sound source: Four MEMS microphone arrays

Four ultra-high signal-to-noise ratio MEMS microphones form an array, combined with AI voice algorithms running on the PSOC™ Edge microcontroller, to 

achieve voice recognition and voice positioning. The voice interaction demonstrated at the Demo site not only verified voice recognition but also 

showcased the microphone array's ability to determine the direction of the voice.

For products such as service robots and companion robots that need to interact with people, the microphone array expands "sound" from a single signal to

 a combination of "direction + content", providing both voice content information and input for the robot to determine the speaker's direction. MEMS 

microphones have already formed a mature industry chain, and the consistency, signal-to-noise ratio, and power consumption of multi-microphone arrays 

have new requirements for robot scenarios.

360° monitoring: Three millimeter-wave radars

The three millimeter-wave radar chips in the Demo are small in size and integrate on-chip antennas. The three radars work together to achieve 360° 

environmental monitoring and can simultaneously track multiple moving targets and output motion trajectories.

Compared to vision solutions, millimeter-wave radars are not affected by changes in lighting conditions and are suitable for robots to detect dynamic 

obstacles. In the Demo, the robot head rotates in real time based on the radar's detected target trajectories, achieving tracking of moving objects. 

Millimeter-wave radars have long been driven by the automotive market, with chips, antennas, and algorithms forming a mature ecosystem; the maturity 

of the automotive millimeter-wave radar industry provides a technical foundation for the adoption of millimeter-wave perception in service robots, AGVs, 

and warehouse logistics scenarios. For the supply chain, RF front-end, antenna modules, and signal processing algorithms remain the main thresholds, but 

the diversification of downstream scenarios also provides different suppliers with differentiated entry points. In the Demo, the head can rotate in real time 

along the radar trajectory, demonstrating the support of the motor control scheme for the head movement. It is worth noting that based on the PSOC™ 

Edge microcontroller, it can ensure the intelligence of environmental perception while meeting the low latency requirements of motion control.

The robot's actuating mechanism has requirements for the miniaturization, efficiency, and control accuracy of the motor drive. Gallium nitride power 

devices are gradually penetrating in high-frequency and high-efficiency scenarios, and the role of magnetic sensors in current detection and angle feedback

 is also increasing. These requirements place higher demands on motor control MCUs, gate drivers, power devices, and magnetic sensors.

Depth Vision: TOF Sensor

The integrated TOF depth camera in the robot head is responsible for collecting depth information. The system generates depth maps, IR images, and real-

time 3D point clouds based on this. Compared to the RGB camera that only captures two-dimensional image information, TOF can effectively avoid the 

influence of external light sources and provide better depth information of the scene, providing distance, contour, and spatial position data for the robot. 

Such depth information can further support environmental perception, map construction, simultaneous localization and mapping (SLAM), navigation, and 

obstacle avoidance functions.

On the Demo screen, one can see the 3D point cloud formed in real time by the system. Such three-dimensional data can be used as input for robot 

navigation, obstacle avoidance, grasping, etc. 3D vision is expanding from industrial inspection to service robots, drones, AR/VR, etc. TOF, structured light, 

and binocular vision each have their applicable scenarios. The advantage of TOF is that it can directly obtain depth information. The demand for low-cost, 

low-power 3D vision solutions in the robot market will continuously drive the iteration of related components.

Industry Perspective: The Industrial Landscape of Robot Sensing and Driving

When looking at the four technical modules together, Infineon's Demo does not only showcase the performance of a single sensor, but also the system-

level collaboration between sensing, computing, and driving. For robot manufacturers, this integration can reduce the adaptation work between different 

components and provide a unified technical foundation for multi-sensor fusion and motion control. As robot applications develop towards more complex 

environmental perception and motion control, the importance of system-level solutions will further increase.

Conclusion

The Infineon robot head demo connects four major technical modules - voice recognition, millimeter wave, TOF, and motor drive - to showcase the 

collaborative process from environmental perception, target tracking to motion execution. Behind this is a changing trend in the robotics industry: As the 

sensing requirements of robots become increasingly complex, the focus of competition will shift from the parameters of a single sensor to system-level 

solutions that provide reference designs and fusion algorithms. The ability to integrate is more difficult to replicate than a single metric. However, there is 

still a gap between the demo and mass production. There are a series of engineering problems such as heat dissipation, EMC, algorithm adaptation, and 

supply chain stability that separate the integration of the four technical modules into a rotatable head from making them into low-power, low-cost, and 

scalable products. For OEMs and component suppliers, what needs to be further addressed in the future is the integration problem between components, 

modules, and systems, so that the technical combination in the demo can truly achieve low power consumption, low cost, and large-scale deployment.