How advances in event-based sensors can change autonomous mobile robots
Insights on the latest developments in the world of autonomous mobile robots.
One of the areas in which advances will have an impact on autonomous mobile robots (AMRs) is event-based sensor technology. These neuromorphic vision systems, also known as dynamic vision sensors (DVS), could significantly change how robots perceive and navigate their environments.
Unlike conventional cameras that capture complete frames at fixed intervals, event-based sensors mimic the human retina by detecting only changes in brightness at the pixel level. This paradigm shift means robots equipped with these sensors generate 10 to 1000 times less data while operating at equivalent frame rates exceeding 10,000 frames per second.
For AMRs operating in dynamic industrial environments, this translates to dramatically reduced computational overhead and faster response times.
Recent research demonstrates that event-based sensors offer microsecond-level temporal resolution with over 120dB dynamic range, enabling robots to function seamlessly across diverse lighting conditions — from pitch darkness to blinding sunlight. This capability is particularly valuable for AMRs that must transition between indoor warehouses and outdoor loading docks without missing a beat.
Traditional AMRs rely heavily on LiDAR, cameras, and ultrasonic sensors for mapping and obstacle detection. While effective, these systems struggle with motion blur during high-speed operations and consume significant power processing redundant visual data. Event-based sensors address these limitations by focusing exclusively on relevant motion and changes in the environment.
Cutting-edge implementations show AMRs equipped with neuromorphic vision achieving 47.3 percent improved detection accuracy in challenging scenarios, such as navigating from dark tunnels into bright outdoor environments. This improvement stems from the sensors' ability to adapt quickly to lighting changes, eliminating the exposure adjustment delays that are a limitation with traditional cameras.
Perhaps most significantly, event-based sensors enable true edge computing for AMRs. As far back as 2017, Intel’s first-generation Loihi research chip enabled processing of 130 million events per second on single CPU cores. Since then, there have been significant advances.
This efficiency allows robots to make real-time decisions without relying on cloud connectivity or power-hungry GPU processing, crucial for battery-powered mobile platforms.
The integration of physics-guided neural networks with event-based perception creates what researchers call ‘neuromorphic navigation stacks’ — systems that combine energy-efficient sensing with intelligent planning algorithms. These advances enable AMRs to operate for extended periods while maintaining high-performance autonomous navigation capabilities.
The event-based sensor market is projected to grow from $500 million in 2025 to $2.5 billion by 2033, we can expect wider adoption across AMR applications. From warehouse automation to outdoor delivery robots, these sensors promise to unlock new levels of autonomy, efficiency, and reliability in mobile robotics, fundamentally changing how robots see and interact with the world around them.