Abstract:MassRobotics, in collaboration with Lattice Semiconductor, has released a report on trends in robotics and AI, focusing on six key areas including sensor fusion and edge AI. FPGA technology addresses issues of latency, power consumption, and security, reshaping the landscape of industrial and consumer-grade smart devices.
Introduction: Endorsed by 40 Industry Experts, Decoding the Evolution of Robotics and AI Technologies
Driven by the dual demands for autonomy, efficiency, and safety, the field of robotics and artificial intelligence is experiencing an unprecedented technological explosion. To accurately capture the pulse of industry transformation, the globally leading robotics innovation ecosystem platform MassRobotics has conducted a comprehensive survey in collaboration with Lattice Semiconductor. This survey gathered insights from 40 core practitioners in the robotics and AI ecosystem, including engineers, technical leaders, product managers, and corporate executives. The sample included both startups and large multinational corporations, as well as top academic institutions, focusing on five core areas: sensor fusion, AI integration, motor control, power consumption optimization, and safety protection. The resulting trend report provides a technical evolution blueprint supported by data and practical guidance for the industry.
Lattice Semiconductor, as the supporting and application partner for this survey, has published two white papers: “Random Bin Picking Based on Structured Light 3D Scanning” and “Sensor Hub for Low-Latency Data Fusion in AI Systems,” which propose FPGA-based hardware solutions addressing the industry pain points revealed by the survey, providing key technical support for the realization of the six trends.
Trend 1: Sensor Fusion Empowering Object Detection – An Efficient Yet Challenging “Double-Edged Sword”
Object detection is the core foundation for autonomous operation in robotics, and multi-sensor fusion has become the mainstream technical path in the industry. Survey data shows that over 85% of respondents have integrated cameras into their systems, and 67.5% of respondents use a combination of LiDAR and cameras, which is rated as the “most effective sensor configuration” by 75.7% of professionals. Additionally, the usage rates of Time-of-Flight (ToF) sensors and Inertial Measurement Units (IMUs) have reached 50% and 62.5%, respectively, becoming key components for auxiliary perception.
Core Application Scenarios:
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Warehouse Logistics Robots:Amazon Robotics employs a “LiDAR + Camera + IMU” fusion solution, achieving a box positioning accuracy of ±2mm, with a dynamic obstacle avoidance response time of <0.5 seconds, improving sorting efficiency by 300% compared to single sensor solutions, and reducing mis-pick rates from 1.2% to 0.08%;
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Autonomous Shuttle Vehicles:Waymo’s autonomous vehicles can identify pedestrians and obstacles 50 meters away even in rainy or foggy weather through the collaboration of LiDAR and high-resolution cameras, with an object detection accuracy improvement of 45% compared to pure vision solutions;
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Industrial Inspection Robots:ABB’s visual inspection robots integrate ToF sensors and cameras to detect defects in electronic component solder joints, identifying the smallest defect size of 0.01mm², with inspection speeds improved by 8 times compared to manual inspection.
Industry Pain Points Not to be Ignored:
Despite the significant advantages of multi-sensor fusion, the survey indicates that the industry still faces three core challenges: high costs (63% of respondents mentioned), high integration complexity (58% of respondents reported), and difficulties in accuracy and calibration maintenance (47% of respondents concerned). For example, in industrial robots, a high-end “LiDAR + Camera” fusion system has a hardware cost of approximately $15,000 to $30,000, accounting for 20-30% of the total robot cost; calibration work requires professional engineers to operate, taking 2-4 hours per calibration, with annual maintenance costs increasing by about $2,000 per unit, becoming a major barrier for small and medium-sized enterprises in automation upgrades.
FPGA Solutions Pathway:
Lattice Semiconductor’s structured light 3D scanning solution provides an efficient solution. By integrating FPGA into the sensor module, computational tasks are offloaded from the main processing module, with the FPGA responsible for generating structured light sequences, synchronizing camera captures, and encoding raw images into 10-bit compressed images (instead of transmitting raw sequences). Test data shows that this solution can reduce Ethernet communication bandwidth requirements by 16 times at 1080p resolution (from 680MB to 41MB), while the FPGA handles computationally intensive tasks such as triangulation, generating depth images and completing some AI object detection and segmentation tasks, reducing the load on the main processing module (CPU/GPU) by 60% and decreasing system BOM costs by 25-30%.
Trend 2: The Rise of Edge AI – Intelligent Decentralization, Real-Time Response as Core Demand
Another key trend revealed by the survey is the migration of AI computation to the sensor side (“edge side”). Currently, half (50%) of respondents have deployed AI technology at the sensor level, with 72.7% applying machine learning models, 54.5% explicitly adopting “edge AI” architectures, and 40.9% integrating neural network algorithms. Looking ahead to the next 3-5 years, the industry generally expects edge intelligence to experience a large-scale explosion.
Core Driving Factors and Application Scenarios:
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Low Latency Demand:In real-time control scenarios for industrial robotic arms, edge AI can compress data processing latency from 50-100ms in the cloud to 0.3-1ms, improving the repeat positioning accuracy of robotic arms to ±0.02mm, meeting the precision requirements for semiconductor chip assembly and other delicate tasks;
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Reducing Data Transmission Overhead:Agricultural drones complete crop pest identification and flight path planning on-device through edge AI, eliminating the need to transmit high-definition images to the cloud, reducing data transmission volume by 90%, and extending battery life by 40% (from 25 minutes to 35 minutes);
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Privacy and Security Protection:Smart home robots (such as vacuum robots) build environmental maps and analyze user behavior on the edge side, avoiding the upload of sensitive data to the cloud, reducing the risk of data breaches, and complying with privacy regulations such as GDPR;
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Offline Operation Capability:Outdoor inspection robots (such as power inspection) can still complete equipment defect detection and abnormal alarms in environments without network access through edge AI, increasing operational coverage from 70% in cloud-dependent modes to 100%.
Key to Technical Implementation: Low-Power AI Hardware
The popularization of edge AI heavily relies on low-power, high-performance hardware support. Traditional GPUs, while powerful, typically consume over 50-100W, which cannot meet the endurance needs of mobile robots and portable devices. The survey shows that 62% of respondents believe that “low-power AI hardware” is the core bottleneck for the implementation of edge AI, while FPGAs, with their parallel computing architecture and flexible programmability, become the ideal solution – Lattice FPGAs typically consume only 1-10W, reducing power consumption by 80-90% compared to GPUs for the same AI inference tasks, and supporting direct model deployment and iteration on-device.
Trend 3: Motor Control – Dual Rigid Demands for Real-Time Response and Efficiency
Motor control, as the “power core” of robotic systems, directly determines the motion accuracy and endurance of robots. Survey data shows that servo motors (55.3%), DC motors (44.7%), and stepper motors (31.6%) are the three most widely used types of motors. In terms of performance demands, 51.3% of respondents consider “real-time response” to be crucial, and 33.3% consider it “relatively important,” with the two combined accounting for over 84%.
Core Application Scenarios and Performance Requirements:
|
Application Field |
Mainstream Motor Types |
Real-Time Response Requirements |
Power Consumption Targets |
Accuracy Requirements |
|
Collaborative Robots |
Servo Motors |
<1ms |
<10W |
Repeat Positioning ±0.05mm |
|
Industrial Robotic Arms |
Servo Motors |
<0.5ms |
10-30W |
Repeat Positioning ±0.02mm |
|
Mobile Robots |
DC Motors |
<5ms |
30-50W |
Speed Error <1% |
|
Medical Robots (Surgery) |
Stepper Motors |
<0.1ms |
<5W |
Angle Error <0.1° |
Core Industry Challenges:
The survey shows that the three major pain points in the field of motor control are: real-time control demand (43.6%), power efficiency (41%), and control accuracy (28.2%). For example, in collaborative robots, traditional motor control solutions use a CPU+DSP architecture, with control latency of about 2-3ms, and are prone to jitter under dynamic loads, leading to decreased operational accuracy; at the same time, inefficient control algorithms cause motor energy consumption to account for 60-70% of the total energy consumption of the robot, severely limiting endurance.
FPGA Empowered Solutions:
Lattice FPGAs reduce the execution latency of core algorithms for motor control (such as PID regulation and trajectory planning) to less than 0.1ms through hardware-level parallel processing, improving performance by 10-20 times compared to traditional solutions; at the same time, their flexible I/O interfaces support direct connections with various sensors (encoders, torque sensors), achieving “perception-control” closed-loop optimization, enhancing the dynamic response speed of motors by 30% and reducing accuracy errors by 50%. In terms of power consumption, the low-power characteristics of FPGAs reduce the energy consumption of motor drive modules by 40%, and combined with energy recovery algorithms, can extend the endurance time of mobile robots by 25%.
Trend 4: Power Consumption Optimization – The “Eternal Proposition” of Robot AI
In the design of robotic systems, balancing performance and energy efficiency has always been a challenge in the industry. Survey data shows that half of the respondents rated the current system’s power consumption performance as “3 points” (on a scale of 1-5, with 5 being the most satisfied), and only 10.5% expressed “high satisfaction,” reflecting the urgent need for power consumption optimization. In terms of power targets, 44.4% of respondents aim for the 50-100W range, while 33.3% pursue lower power consumption (<10W or 10-50W), mainly focusing on mobile robots and portable medical devices.
Power Consumption Optimization Cases in Different Scenarios:
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Portable Robots:DJI’s agricultural drones use a hybrid architecture of low-power AI chips + FPGAs, reducing operational power consumption from 120W to 85W, extending endurance time from 20 minutes to 28 minutes, and increasing the area covered in a single operation by 40%;
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Industrial Robots:FANUC’s collaborative robots optimize motor control and AI inference processes through FPGAs, reducing system power consumption from 90W to 45W, saving about $1200 per year in electricity costs per unit (based on industrial electricity prices of $0.15/kWh);
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Smart Home Robots:Ecovacs’ vacuum robots use Lattice’s low-power FPGAs to process sensor data, reducing standby power consumption from 2W to 0.5W, and normal operational power consumption from 35W to 22W, decreasing charging frequency by 30%.
Key Technical Breakthrough Directions:
In the survey, respondents repeatedly mentioned three core needs: more efficient onboard processing (56%), reducing reliance on high-power GPUs (48%), and improving battery technology (42%). The emergence of FPGAs meets the first two needs simultaneously – Lattice FPGAs consume only 1/10-1/5 of the power of GPUs when performing tasks such as sensor data processing and AI inference, and support collaboration with low-power MCUs, forming an efficient architecture of “FPGA for intensive computation + MCU for logical control.” Additionally, the miniaturization of FPGAs (some models are only 10mm×10mm) eliminates the need for additional heat dissipation components, further reducing overall system power consumption and size.
Trend 5: Safety and Security – Upgraded Risks and Protections Under AI Integration
As the autonomy and connectivity of robotic systems increase, safety and security issues are becoming increasingly prominent. The survey shows that 64% of respondents have adopted redundant sensors and safety-grade components in their systems, but the integration of AI technology has brought new risk challenges. Among the types of security threats, cybersecurity threats (48.6%) rank first, followed by data protection (35.1%) and system integrity (35.1%).
High-Risk Application Scenarios and Protection Needs:
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Industrial Internet of Things (IIoT) Robots:If welding robots in automotive factories encounter cyberattacks, it may lead to production interruptions or equipment damage. According to industry statistics, such security incidents average over $500,000 in daily losses per factory, requiring hardware-level isolation and intrusion detection capabilities;
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Medical Robots:The system integrity of surgical robots directly relates to patient safety; any data tampering or logical errors could lead to medical accidents, requiring end-to-end encryption and secure boot functions;
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Autonomous Driving Robots:If sensor data from autonomous vehicles is stolen or interfered with, it may lead to navigation errors, necessitating the establishment of a full-link security protection system from “sensors – edge computing – cloud.”
Industry Protection Shortcomings and FPGA Solutions:
The survey found that although most companies are aware of security risks, only 23% of respondents have specialized security solutions for AI, and the application rate of hardware-level protection (such as hardware isolation and encryption) is less than 15%. Lattice FPGAs fill this gap through three core technologies:
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Secure Boot:Only allows verified firmware and algorithms to run, preventing malicious code injection;
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Hardware Encryption:Real-time encryption transmission and storage of sensor data and AI model parameters, making it over 10 times more difficult to crack than software encryption;
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Physical Isolation:Physically isolates computing resources for safety-critical tasks (such as emergency stop control) from ordinary tasks, preventing a single module from affecting the overall system after being attacked.
Additionally, the sensor fusion capabilities of FPGAs can enhance the proactivity of security protections – for example, by establishing a virtual safety fence through the collaboration of radar and cameras, when unauthorized personnel enter a hazardous area, the robot can stop and alarm in real-time, with a response time of <0.3 seconds, improving upon traditional safety systems by 5 times.
Trend 6: FPGA Hardware Empowerment – A “Unified Solution” for Six Trends
The realization of the aforementioned five trends faces common pain points: low latency, low power consumption, high flexibility, and high security, and FPGAs, with their unique hardware characteristics, become the “bridge” connecting sensors, actuators, and main processing units, serving as the core enabling technology for the six trends. Lattice Semiconductor’s two white papers and prototype verification systems (PoC) fully demonstrate the key role of FPGAs in the realization of these trends.
Quantitative Comparison of FPGA Core Advantages (vs Traditional CPU/GPU):
| Performance Indicators | FPGA (Lattice Avant Series) | CPU (Intel Core i7) | GPU (NVIDIA Jetson Orin) | Advantage Margin |
|
Sensor Data Processing Latency |
0.32ms (VLP16 LiDAR) |
8.5ms |
1.32ms |
26 times faster than CPU, 3 times faster than GPU |
|
Typical Power Consumption |
5W |
45W |
30W |
89% lower than CPU, 83% lower than GPU |
|
Sensor Compatibility |
Supports multiple types including cameras, LiDAR, radar, etc. |
Requires additional interface modules |
Some sensors require adaptation |
Reduces BOM costs by 20% |
|
AI Inference Efficiency Ratio |
15 TOPS/W |
2 TOPS/W |
10 TOPS/W |
6.5 times higher than CPU, 50% higher than GPU |
|
Security Protection Level |
Hardware-level encryption + isolation |
Mainly software-level protection |
Mainly software-level protection |
10 times higher resistance to attacks |
Typical Application Case: Low-Latency Data Fusion Near Sensors
Lattice’s sensor hub prototype system developed based on the Avant FPGA can simultaneously process raw data from cameras, LiDAR, and radar:
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Data Processing Latency:VLP16 LiDAR data processing takes only 0.32ms, reducing the data packet transmission time (1.32ms) by 75%;
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Fusion Accuracy:By fusing camera human detection boxes with LiDAR point clouds and radar target data, the object recognition accuracy improves from 82% with a single sensor to 97%;
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Power Consumption Optimization:Near-sensor processing reduces data transmission volume by 85%, lowering overall system power consumption by 40%;
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Flexible Expansion:Supports High Level Synthesis (HLS), Matlab/Simulink, etc., combined with Lattice’s sensAI Studio and edge vision engine, shortening AI model deployment cycles from months to weeks.
Industry Outlook: Robotics and AI Technology Evolution Roadmap 2024-2027
Combining survey data and industry forecasts (Grand View Research, IDC), the robotics and AI field will present three major development directions:
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Accelerated Technology Integration:The combination of sensor fusion and edge AI will become standard, with the global edge AI robot market expected to exceed $38 billion by 2026, with a compound annual growth rate of 37%;
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Rise of Domestic Hardware:The process of domestic substitution for key hardware such as FPGAs will accelerate, with the self-selection rate of domestic robotics companies for FPGAs expected to increase from the current 15% to 40% by 2025;
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Improvement of Safety Standards:Safety standards for AI robots will gradually be implemented, with hardware-level safety protection becoming an industry entry threshold. By 2027, over 80% of industrial robots globally are expected to be equipped with hardware safety modules such as FPGAs.
Lattice Semiconductor’s sensAI solution stack (including pre-trained models, development tools, reference designs) will further lower the technical implementation threshold, accelerating the innovation speed of small and medium-sized robotics companies by 50%. As the MassRobotics survey conclusion indicates, the future development of robotics and AI relies not only on the iteration of algorithms and software but also on the deep collaboration of hardware and software – FPGAs, as representatives of “flexible hardware,” are becoming the key link connecting technological trends and industrial applications.
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