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, MassRobotics carried out a complete survey of execs throughout the robotics and AI ecosystems. This market analysis was developed and deployed with the help and steering of Lattice Semiconductor, for whom this report was initially ready.
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. Individuals included a various vary of execs, from engineers and technical results in product managers and executives, representing corporations from startups to massive multinational firms, 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 powerful reliance on subtle sensor mixtures. Over two-thirds of respondents (67.5%) make the most of LiDAR together with cameras (85% use cameras normally), which 75.7% of respondents deemed the “handiest” 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. Price and integration complexity had been probably the most regularly cited limitations for professionals. Moreover, accuracy and calibration/upkeep wants recurrently surfaced as issues. This underscores a transparent trade want for extra streamlined, cost-effective options for integrating a number of sensor modalities.
2. Rising momentum of Edge AI
A big pattern rising from the survey is the growing adoption of AI on the sensor or “edge” degree. Presently, half of the respondents (50%) are already implementing AI on the sensor degree. Of those, 72.7% apply some type of machine studying mannequin, 54.5% particularly use “Edge AI,” and 40.9% incorporate “Neural Networks”.
Trying forward, many anticipate a better shift of intelligence to the sting over the subsequent few years. The first drivers for this distributed intelligence are the will to scale back latency, improve real-time efficiency, and reduce information switch overhead. This shift alerts a rising demand for low-power AI {hardware} that may deal with inference immediately on-device.
3. Motor management: criticality of real-time response and effectivity
Motor management stays a core element of robotics methods, 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 essential” for 51.3% of respondents, and “considerably essential” for an additional 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 instant responsiveness and power conservation factors to an trade want for superior management loops and motor drive options that decrease latency and optimize energy utilization.
4. Energy consumption: perpetual quest for effectivity
Attaining an optimum stability between efficiency and power 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 reasonable satisfaction. Solely 10.5% expressed excessive satisfaction.
For a lot of methods, 44.4% of respondents goal an influence threshold of 50-100 W, with others aiming for even decrease thresholds (
5. Security and safety: rising urgency with AI integration
As robotics methods change into extra autonomous and interconnected, security and safety issues are escalating. A big majority of respondents (64%) already implement redundant sensors and use safety-rated elements. Nonetheless, the mixing of AI introduces new complexities.
Cybersecurity threats had been highlighted by 48.6% of respondents as their greatest safety problem, adopted by information safety (35.1%) and system integrity (35.1%). Whereas many respondents acknowledged these issues, a concrete plan for AI-focused safety is commonly missing, with just a few mentioning {hardware} isolation or encryption. This hole underscores the essential want for strong hardware-level safety measures, comparable to safe boot, encryption, and tamper detection, particularly as extra AI processes migrate to the sting.
Addressing key developments
“Random Bin Selecting Primarily based On Structured-Gentle 3D Scanning,” a white paper by Lattice Semiconductor, outlines an strategy 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 principle computing module. This includes the FPGA producing structured mild sequences and synchronizing digital camera seize.
A key discovering was that the FPGA can encode the captured photographs right into a compact 10-bit coded picture, somewhat than sending uncooked sequences, which considerably reduces the bandwidth required for Ethernet communication (e.g., a 16x information discount for a 1080p state of affairs from 680 MB to 41 MB). Moreover, Lattice recognized that FPGAs can take over compute-intensive duties like triangulation to generate depth photographs and also can carry out elements of machine learning-based object detection and segmentation, thereby decreasing the processing calls for on the principle computing module (CPU/GPU).
This strategy helps the survey’s commentary on the necessity for extra environment friendly on-board processing and decreasing 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 added warmth dissipation elements, contributing to a decreased BOM for the sensor module. A proof-of-concept (PoC) demo system was constructed using a general-purpose projector, a CPNX VVML improvement board, an NVIDIA Jetson Orin Nano, and a UFACTORY LITE6 robotic arm to confirm these ideas.
These capabilities are underpinned by Lattice’s sensAI answer stack, which supplies pre-trained fashions, improvement 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 Techniques” immediately addresses key developments 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 priceless {hardware} answer by performing as a “bridge” between sensors, actuators, and fundamental processing items, 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 information from a number of sensor varieties: a digital camera, lidar, and radar.
By way of this demonstration, Lattice noticed that FPGAs supply 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 usually 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 essential duties comparable to sensor fusion; as an example, they demonstrated processing VLP16 lidar information in 0.32 milliseconds, in comparison with the 1.32 milliseconds for packet transmission.
This near-sensor processing additionally reduces general system power consumption by processing information regionally earlier than transmitting it to the principle computing module, addressing the “perpetual quest for effectivity.” The PoC additional demonstrated efficient sensor fusion by combining camera-based human detection bounding bins with lidar level cloud information and radar object output, which enhanced the system’s accuracy and decision-making, immediately 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 digital camera 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 information). 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 Degree Synthesis (HLS) and Matlab/Simulink, supported by Lattice’s sensAI Studio and Edge Imaginative and prescient Engine, which streamline AI mannequin improvement and deployment for edge functions.
About MassRobotics
MassRobotics is the world’s largest impartial 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 expertise corporations by offering entrepreneurs and startups with the workspace, assets, programming and connections they should develop, prototype, take a look at 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, trade and governments each domestically and internationally.
About Lattice Semiconductor
Lattice Semiconductor (NASDAQ: LSCC) is the low energy programmable chief. We clear up buyer issues throughout the community, from the Edge to the Cloud, within the rising Communications, Computing, Industrial, Automotive, and Shopper markets. Our expertise, long-standing relationships, and dedication to world-class help let our prospects shortly and simply unleash their innovation to create a wise, safe, and related world.
