The robotics and artificial intelligence sectors are advancing at a rapid pace as industries demand higher levels of autonomy, efficiency, and safety. To better understand these shifts, MassRobotics conducted a comprehensive survey of 40 professionals working across the global robotics and AI ecosystem. Supported by Lattice Semiconductor, the survey gathered insights from engineers, technical leaders, product managers, executives, and academic researchers. The findings provide an overview of the latest practices, challenges, and expectations related to sensor fusion, AI adoption, motor control, power consumption, and system safety.
1. Sensor Fusion for Enhanced Object Detection
The survey shows that multi-sensor object detection has become a foundational part of modern robotic autonomy. Cameras are nearly universally used, and the combination of cameras with LiDAR is considered by most respondents to be the most effective sensing solution. However, integrating multiple sensing modalities continues to present obstacles. High costs, integration complexity, accuracy limitations, and ongoing calibration needs are repeatedly identified as core issues. These concerns highlight a strong industry demand for simpler, more affordable, and more reliable sensor-fusion technologies.
2. Growing Momentum of Edge AI
The collected data indicates that intelligence is steadily shifting toward the edge. Half of all respondents are already implementing AI directly at the sensor level, often through machine-learning models and neural-network inference. This transition is driven primarily by the need to reduce latency, increase real-time responsiveness, and limit data transfer requirements. These motivations point to a rising demand for low-power AI hardware capable of performing local inference without relying heavily on centralized computing resources.
3. Motor Control: Real-Time Response and Efficiency
Motor control remains a critical component of robotic systems, with respondents emphasizing the importance of real-time performance. A majority regard real-time responsiveness as essential, while precision and power efficiency also rank among the most significant challenges. These findings illustrate the continued need for advanced motor-control architectures, optimized control loops, and drive solutions that minimize latency while maintaining accurate and efficient operation.
4. Power Consumption: Continuous Pursuit of Efficiency
Energy efficiency remains a persistent challenge for robotics developers. Many respondents reported only moderate satisfaction with the power consumption of their current systems. A large portion aim for power budgets between 50 and 100 watts—or even lower in some designs—highlighting the need for more efficient processing technologies. Reducing reliance on energy-intensive GPUs, improving onboard computation efficiency, and advancing battery technologies are repeatedly cited as necessary steps for achieving better performance-per-watt.
5. Safety and Security: Rising Urgency with AI Adoption
As robotic systems become increasingly autonomous and interconnected, safety and security concerns are gaining heightened importance. Many organizations have already implemented redundant sensors and safety-certified components. However, the rise of AI introduces new security challenges, particularly in cybersecurity, data protection, and system integrity. Few respondents report having a complete strategy for addressing AI-related security risks, reflecting the growing need for hardware-based safeguards such as secure boot mechanisms, encryption, and tamper detection.
Addressing Key Trends Through FPGA-Based Solutions
Several of the challenges identified in the survey are reflected in recent white papers published by Lattice Semiconductor. One study on structured-light 3D scanning highlights how FPGA-based processing inside sensor modules can lower system cost and reduce the computational load on the main processor. By encoding structured-light images into compact formats and performing compute-intensive tasks—such as triangulation and certain machine-learning processes—directly within the FPGA, the approach significantly reduces bandwidth consumption and power usage.
Another Lattice white paper examines near-sensor, low-latency sensor fusion using FPGA-based sensor hubs. The findings show that performing parallel processing on data from cameras, lidar, and radar directly on the FPGA can substantially reduce latency and energy consumption. These capabilities also enhance sensor fusion accuracy and expand application possibilities, such as enabling radar-triggered camera inference or AI-defined safety regions. The compact size, low power draw, and minimal cooling requirements of the FPGA architecture support its suitability for robotic platforms, while the development workflow benefits from tools such as High Level Synthesis, Matlab/Simulink, sensAI Studio, and Edge Vision Engine.
About MassRobotics and Lattice Semiconductor
MassRobotics is recognized as one of the world’s largest independent robotics hubs, dedicated to supporting the development, commercialization, and adoption of robotics and Physical AI technologies. Based in Boston, the organization works with startups, research institutions, industry partners, and government entities to accelerate innovation worldwide. Lattice Semiconductor, a leader in low-power programmable technology, provides solutions that serve communications, computing, industrial, automotive, and consumer markets. Its portfolio supports the creation of secure, efficient, and connected intelligent systems through flexible and power-optimized hardware platforms.
