Key Technologies Driving Next-Generation Robotic Systems

As robotic systems transition from research labs to commercial deployments across manufacturing, logistics, and services, it is essential to identify the remaining barriers that limit widespread adoption.

Although both hardware and software have seen significant gains, rapid design evolution continues to push robots toward greater utility and intelligence across domains such as agriculture, warehousing, last‑mile delivery, inspection, and smart manufacturing.

In practice, a robot ingests data from sensors and cameras, localizes itself, perceives its surroundings, predicts the motion of nearby objects, and plans safe, coordinated movements—all of which demand substantial processing power and energy.

Power consumption in robotic platforms falls into three main categories: drive motors and controllers, sensing systems, and processing units. A new generation of low‑power, high‑accuracy sensors is needed to determine the robot’s orientation and position efficiently, while the processing platforms must balance performance with energy use, especially since robots typically move at modest speeds.

At this critical juncture, the system‑on‑chip (SoC) emerges as the linchpin that integrates diverse sensing modules and powerful artificial‑intelligence (AI) engines, enabling the next wave of commercial robots.

Robotic operations often require concurrent real‑time execution of dozens of algorithms—including odometry, path planning, vision, and perception. This complexity demands SoCs that can deliver unparalleled integration, supporting specialized functions such as sparse coding, path planning, and simultaneous localization and mapping (SLAM).

Qualcomm’s SDA/SDM845 chip (Figure 1) exemplifies this leap in integration. Its octa‑core Kryo CPU runs at 2.8 GHz, complemented by a Hexagon 685 DSP for on‑device AI processing and a mobile‑optimized computer‑vision stack for perception, navigation, and manipulation. A dual 14‑bit Spectra 280 image‑signal processor (ISP) handles up to 32‑megapixel cameras and 4K video at 60 fps.

Figure 1: The architectural building blocks of the Qualcomm SDM845 chip for robotic designs (Image: Qualcomm)

The platform also incorporates a secure‑processing unit (SPU) that provides secure boot, cryptographic acceleration, and a trusted execution environment (TEE). Connectivity is covered by Wi‑Fi, with plans to add 5G for low‑latency, high‑throughput communication in industrial settings.

Qualcomm’s Robotics RB3 platform builds on the SDA/SDM845, paired with the DragonBoard 845c development board and kit for rapid prototyping.

Nvidia’s Jetson Xavier (Figure 2) showcases another highly integrated solution, targeting delivery and logistics robots. The module contains 9 billion transistors and delivers over 30 trillion operations per second (TOPS), featuring an eight‑core ARM64 CPU, a Volta Tensor‑Core GPU, dual NVIDIA deep‑learning accelerators (NVDLA), an image processor, a vision processor, and a video processor.

Figure 2: The 80 × 87‑mm Jetson Xavier module delivers workstation‑level compute in a fraction of the size (Image: Nvidia)

These examples illustrate that AI accelerators are indispensable in SoCs and modules, enabling robots to fuse sensor data, localize, map, and navigate with precision.

AI is pivotal for elevating a robot’s responsiveness and accuracy, especially in object detection and recognition. By moving beyond rigid programming, AI allows robots to interact more naturally and precisely with their surroundings.

Yet designers must embed additional AI capabilities without inflating component size or power draw. Moreover, large form factors can impede commercial adoption.

Figure 3: AI modules must balance performance, size, and power to meet market demands.

Another hurdle is ensuring broad AI‑framework support, as industrial and service robots increasingly rely on inference models for orientation and positioning.

Robots such as autonomous vacuum cleaners and hoverboards demand highly stable, high‑performance sensors that can withstand intense vibration. Achieving high‑precision sensor processing often drives up both cost and development time when software‑based control is used.

Consequently, integrated sensing solutions are essential. For the RB3 platform, InvenSense (now a TDK company) supplies a suite of low‑power, tightly matched sensors and microphones with high acoustic overload points.

The RB3 uses InvenSense’s six‑axis IMUs—a trio of gyroscopes and accelerometers—alongside a capacitive barometric pressure sensor and multi‑mode digital microphones. These IMUs provide real‑time clock‑based positioning, while the pressure sensor achieves 10‑cm elevation accuracy.

Robots are increasingly adopting smart sensor and camera systems equipped with SLAM‑based navigation, enabling them to operate reliably in complex real‑world environments. Machine‑learning capabilities are being embedded into 3D‑vision cameras to further enhance perception.

Designers must still prioritize compactness and low power, ensuring that high‑resolution sensors interface seamlessly with robot controllers over standard digital buses.

Like AI, smart sensors and cameras remain in early stages, but the industry anticipates significant maturity by 2020, offering lower costs and higher accuracy.

As sensors and AI mature, robots will transition from standalone autonomous units in warehouses and factories to collaborative partners across consumer and industrial landscapes.