Tag: autonomous vehicles

  • Ultra-Low-Power Chip Lets Tiny Robots Create Real-Time 3D Maps for Navigation

    Ultra-Low-Power Chip Lets Tiny Robots Create Real-Time 3D Maps for Navigation

    MIT researchers have unveiled a new chip that could transform how tiny, battery-powered robots navigate complex environments. The chip, named Gleanmer, enables small autonomous vehicles and other power-limited devices to build detailed 3D maps of their surroundings in real time while consuming only about 6 milliwatts of power — roughly the same as a single LED.

    Traditional 3D mapping systems require significant memory and energy because they store and process every pixel from depth images. The MIT team took a different approach, combining an efficient algorithm with custom hardware to dramatically reduce both power and memory demands.

    How Gleanmer Works

    Instead of representing the environment with rigid cube-shaped voxels (3D pixels), the chip uses flexible ellipsoid shapes called Gaussians. These ellipsoids can be smoothly adjusted in size, shape, and thickness to match curved objects more efficiently than voxels. This compact representation captures both obstacles and free space, allowing a robot to plan collision-free paths without storing massive amounts of data.

    The researchers leveraged an algorithm developed in their lab called GMMap, which generates Gaussian-based maps from depth images in a single pass. By only comparing each pixel to its immediate neighbors, the algorithm avoids storing entire images at once — significantly reducing memory footprint.

    “At any point in time, we only need to store a few pixels in memory, which significantly reduces the memory footprint our algorithm requires,” said co-lead author Peter Zhi Xuan Li, an MIT graduate student.

    Co-Design for Maximum Efficiency

    As a robot moves, it often sees the same object from multiple angles, creating overlapping Gaussians. The chip fuses these overlapping shapes directly — without revisiting the original pixels — keeping the map compact. This principle extends throughout the design: most computations operate on compact Gaussians rather than raw pixels, enabling major energy savings.

    The hardware is built to keep active Gaussians in small, fast on-chip memory right next to the processing units. This eliminates the need to fetch data from distant, power-hungry off-chip storage.

    “By having a dedicated memory that just stores the objects you’ve seen in the previous few frames, you can access the data much more efficiently,” explained co-lead author Zih-Sing Fu.

    Real-World Testing and Future Applications

    The team tested Gleanmer on diverse 3D environments and live data from an iPhone camera. The chip generated accurate maps in real time while using just 2.5% of the power required by the best existing map-construction chip. It also allowed a simulated robot to plan safe trajectories using only about 20% of the energy it would otherwise need.

    Beyond robotics, the ultra-low-power operation makes the chip well-suited for lightweight augmented reality headsets that can be worn for extended periods — for applications like medical training simulations or detailed repair work.

    “Real-time 3D mapping has been the missing piece for small autonomous systems. A drone inspecting a pipeline or a pair of AR glasses navigating a room both need to understand the space around them — instantly, continuously, and at almost no power cost. Gleanmer makes that possible for the first time in a chip you can hold between your fingers,” said co-author Sertac Karaman, a professor of aeronautics and astronautics.

    The work was supported by the MIT-MathWorks Fellowship, Amazon, the U.S. National Science Foundation, and Intel. The paper was presented at the IEEE Very Large-Scale Integrated Circuits Symposium.

  • MIT’s 6-Milliwatt AI Chip Brings Real-Time 3D Mapping to Tiny Robots

    MIT’s 6-Milliwatt AI Chip Brings Real-Time 3D Mapping to Tiny Robots

    Researchers at MIT have unveiled a new system-on-a-chip that could revolutionize how tiny, low-power robots navigate complex environments. The chip, named Gleanmer, enables autonomous drones and other battery-limited devices to construct detailed 3D maps in real time using only about 6 milliwatts of power—roughly the energy of a single LED. This breakthrough, presented at the IEEE Very Large-Scale Integrated Circuits Symposium, promises to make small-scale robotics more capable and energy-efficient than ever.

    A Smarter Approach to 3D Mapping

    Traditional 3D mapping for robots relies on power-hungry systems that process and store thousands of 3D pixels (voxels) per image. The MIT team took a radically different approach by combining an efficient algorithm called GMMap with custom hardware optimized for its workload. Instead of using rigid cube-shaped voxels, GMMap represents obstacles and free space with flexible ellipsoidal blobs known as Gaussians. These shapes can stretch and shrink to match curved objects, storing far less data than traditional methods while still capturing accurate geometry.

    “This paper showcases a key example of how you can leverage co-design of the algorithm and hardware to really push energy efficiency,” says Vivienne Sze, a professor in EECS and senior author of the paper. “Our chip allows you to store very large maps in a very small space, and do it in a very energy efficient manner.”

    Hardware-Algorithm Co-Design at Its Best

    The chip’s efficiency stems from several clever optimizations. First, the GMMap algorithm processes each depth image in a single pass, comparing only neighboring pixels rather than all pixels in an image. This drastically reduces the amount of data that needs to be stored and retrieved. Second, as the robot moves and observes the same object from different angles, overlapping Gaussians are fused into a single compact representation without re-examining the original pixels. Because Gaussians are more compact than raw pixel data, this fusion process runs on-chip with minimal memory traffic.

    The hardware design keeps active Gaussians in small, fast on-chip memory right next to the processing units. “By having a dedicated memory that just stores the objects you’ve seen in the previous few frames, you can access the data much more efficiently,” explains co-lead author Zih-Sing Fu.

    Unmatched Energy Efficiency

    In tests, Gleanmer generated detailed 3D maps in real time while consuming only about 6 milliwatts of power—just 2.5 percent of what the best existing mapping chip requires. When used for path planning, the chip reuses compact Gaussians along the planned route, using only about 20 percent of the energy that conventional approaches would need.

    The low power consumption makes Gleanmer ideal not only for tiny drones inspecting HVAC systems or pipelines, but also for lightweight augmented reality headsets that require continuous environmental mapping for educational medical simulations or repair work. Sertac Karaman, a professor of aeronautics and astronautics and co-author, notes: “Real-time 3D mapping has been the missing piece for small autonomous systems. Gleanmer makes that possible for the first time in a chip you can hold between your fingers.”

    Looking Ahead

    The researchers plan to push energy efficiency even further by moving processing units closer to the sensors that gather environmental data. They are also exploring applications beyond robotics, such as using Gaussians to represent complex schematics for AI reasoning systems. This work was supported by the MIT-MathWorks Fellowship, Amazon, the U.S. National Science Foundation, and Intel.