6 traits shaping robotics and AI

By The Robotic Report Workers | November 19, 2025

The fields of robotics and synthetic intelligence are evolving at an unprecedented tempo, pushed by innovation and growing calls for for autonomy, effectivity, and security. To raised perceive these shifts, Lattice Semiconductor and MassRobotics performed a complete survey of pros throughout the robotics and AI ecosystems.

This report summarizes key insights from 40 respondents from the innovation ecosystem, providing a snapshot of present practices, challenges, and future expectations in sensor fusion, AI integration, motor management, energy consumption, and security and safety. Contributors included a various vary of pros, from engineers and technical results in product managers and executives, representing corporations from startups to giant multinational companies, in addition to tutorial establishments.

1. Sensor fusion for enhanced object detection: a double-edged sword

Object detection is foundational to robotic autonomy, and the survey highlights a robust reliance on refined sensor mixtures. Over two-thirds of respondents (67.5%) make the most of LiDAR along with cameras (85% use cameras typically), which 75.7% of respondents deemed the “simplest” mixture. Different sensor varieties generally used embrace Time-of-Flight (50%) and IMUs (62.5%).

Regardless of the effectiveness of those multi-sensor approaches, vital challenges persist. Value and integration complexity have been probably the most continuously cited boundaries for professionals. Moreover, accuracy and calibration/upkeep wants repeatedly surfaced as considerations. This underscores a transparent business want for extra streamlined, cost-effective options for integrating a number of sensor modalities.

2. Rising momentum of Edge AI

A big development rising from the survey is the growing adoption of AI on the sensor or “edge” stage. At the moment, half of the respondents (50%) are already implementing AI on the sensor stage. Of those, 72.7% apply some type of machine studying mannequin, 54.5% particularly use “Edge AI,” and 40.9% incorporate “Neural Networks”.

Wanting forward, many anticipate a higher shift of intelligence to the sting over the subsequent few years. The first drivers for this distributed intelligence are the need to scale back latency, improve real-time efficiency, and reduce knowledge switch overhead. This shift alerts a rising demand for low-power AI {hardware} that may deal with inference instantly on-device.

3. Motor management: criticality of real-time response and effectivity

Motor management stays a core part of robotics techniques, with servo motors (55.3%), DC motors (44.7%), and stepper motors (31.6%) being the most typical varieties used. The survey revealed that real-time response is “extremely vital” for 51.3% of respondents, and “considerably vital” for one more 33.3%.

Key challenges in motor management embrace the demand for real-time management (43.6%), energy effectivity (41%), and precision (28.2%). This emphasis on rapid responsiveness and vitality conservation factors to an business want for superior management loops and motor drive options that reduce latency and optimize energy utilization.

4. Energy consumption: perpetual quest for effectivity

Attaining an optimum steadiness between efficiency and vitality effectivity is a persistent problem in robotics. Half of the respondents rated their present satisfaction with energy consumption at a “3” on a 1-5 scale (with 5 being most glad), indicating average satisfaction. Solely 10.5% expressed excessive satisfaction.

For a lot of techniques, 44.4% of respondents goal an influence threshold of 50-100 W, with others aiming for even decrease thresholds (<10 W or 10-50 W). The necessity for extra environment friendly on-board processing, lowering reliance on power-hungry GPUs, and improved battery know-how have been repeatedly cited as essential developments. This highlights a robust market demand for options that supply sturdy processing capabilities with out compromising on vitality effectivity.

5. Security and safety: rising urgency with AI integration

As robotics techniques grow to be extra autonomous and interconnected, security and safety considerations are escalating. A big majority of respondents (64%) already implement redundant sensors and use safety-rated parts. Nevertheless, the combination of AI introduces new complexities.

Cybersecurity threats have been highlighted by 48.6% of respondents as their largest safety problem, adopted by knowledge safety (35.1%) and system integrity (35.1%). Whereas many respondents acknowledged these considerations, a concrete plan for AI-focused safety is commonly missing, with just a few mentioning {hardware} isolation or encryption. This hole underscores the vital want for sturdy hardware-level safety measures, reminiscent of safe boot, encryption, and tamper detection, particularly as extra AI processes migrate to the sting.

Addressing key traits

“Random Bin Choosing Primarily based On Structured-Gentle 3D Scanning,” a white paper by Lattice Semiconductor, outlines an method to deal with a number of challenges highlighted within the MassRobotics survey, significantly concerning object detection, sensor fusion complexity, and the demand for more cost effective options. Lattice posits that their FPGA options can scale back system Invoice of Supplies (BOM) value. They arrived at this discovering by designing a system the place the FPGA, situated within the sensor module, partitions computing duties by offloading processing from the primary computing module. This entails the FPGA producing structured mild sequences and synchronizing digicam seize.

A key discovering was that the FPGA can encode the captured photos right into a compact 10-bit coded picture, slightly than sending uncooked sequences, which considerably reduces the bandwidth required for Ethernet communication (e.g., a 16x knowledge discount for a 1080p situation from 680 MB to 41 MB). Moreover, Lattice recognized that FPGAs can take over compute-intensive duties like triangulation to generate depth photos and can even carry out facets of machine learning-based object detection and segmentation, thereby lowering the processing calls for on the primary computing module (CPU/GPU).

This method helps the survey’s remark on the necessity for extra environment friendly on-board processing and lowering reliance on power-hungry GPUs. The low energy consumption and small type issue of Lattice FPGAs additionally permit the sensor module to be designed with out the necessity for extra warmth dissipation parts, contributing to a lowered BOM for the sensor module. A proof-of-concept (PoC) demo system was constructed using a general-purpose projector, a CPNX VVML growth board, an NVIDIA Jetson Orin Nano, and a UFACTORY LITE6 robotic arm to confirm these idea

These capabilities are underpinned by Lattice’s sensAI answer stack, which gives pre-trained fashions, growth instruments, and reference designs to speed up deployment.

Lattice’s white paper on “Sensor Hub For Close to-Sensor Low-Latency Information Fusion In AI Programs” instantly addresses key traits from the MassRobotics survey, together with the rising momentum of Edge AI, the criticality of real-time response, persistent energy consumption challenges, and the growing urgency of security and safety with AI integration. Lattice posits that FPGAs function a useful {hardware} answer by performing as a “bridge” between sensors, actuators, and most important processing models, supporting the shift of intelligence to the sting. They arrived at these findings by growing a proof-of-concept (PoC) demo system the place a Lattice Avant FPGA concurrently processes uncooked knowledge from a number of sensor varieties: a digicam, lidar, and radar.

Via this demonstration, Lattice noticed that FPGAs provide versatile and customizable Enter/Output (I/O) capabilities, enabling connectivity with a big selection of various sensors and actuators, which helps overcome the I/O limitations typically present in high-performance computing modules. Lattice’s findings point out that performing hardware-based parallel processing close to the sensors considerably reduces latency for vital duties reminiscent of sensor fusion; as an example, they demonstrated processing VLP16 lidar knowledge in 0.32 milliseconds, in comparison with the 1.32 milliseconds for packet transmission.

This near-sensor processing additionally reduces total system vitality consumption by processing knowledge regionally earlier than transmitting it to the primary computing module, addressing the “perpetual quest for effectivity.” The PoC additional demonstrated efficient sensor fusion by combining camera-based human detection bounding containers with lidar level cloud knowledge and radar object output, which enhanced the system’s accuracy and decision-making, instantly addressing the survey’s famous “sensor integration and fusion challenges” and the necessity for “extra accessible sensor fusion options.”

This fusion functionality permits functions that may scale back energy consumption (e.g., radar triggering digicam AI/ML solely when movement is detected) or improve security (e.g., creating digital security fences by utilizing AI/ML to outline areas of curiosity for radar knowledge). The small type issue, low energy consumption, and lack of want for a cooling system for Lattice FPGAs additionally make them appropriate for robotic functions. The event course of for these options can combine instruments like Excessive Stage Synthesis (HLS) and Matlab/Simulink, supported by Lattice’s sensAI Studio and Edge Imaginative and prescient Engine, which streamline AI mannequin growth and deployment for edge functions.

About MassRobotics

MassRobotics is the world’s largest unbiased robotics hub devoted to accelerating robotics innovation, commercialization and adoption. Its mission is to assist create and scale the subsequent technology of profitable robotics and Bodily AI know-how corporations by offering entrepreneurs and startups with the workspace, assets, programming and connections they should develop, prototype, check and commercialize their merchandise and options. Whereas MassRobotics originated and is headquartered in Boston, we’re reaching and supporting robotics acceleration and adoption globally and are working with startups, academia, business and governments each domestically and internationally.

About Lattice Semiconductor

Lattice Semiconductor (NASDAQ: LSCC) is the low energy programmable chief. We resolve buyer issues throughout the community, from the Edge to the Cloud, within the rising Communications, Computing, Industrial, Automotive, and Client markets. Our know-how, long-standing relationships, and dedication to world-class assist let our clients shortly and simply unleash their innovation to create a sensible, safe, and related world.

Supply: https://www.therobotreport.com/6-trends-shaping-robotics-and-ai/