Carnegie Mellon University’s Autonomous Exploration Research Team has developed a suite of robotic systems and planners that enable robots to explore more quickly, probe the darkest corners of unknown environments, and create more accurate and detailed maps. The systems allow robots to do all this autonomously, finding their way without human intervention.

The group combined a 3D scanning lidar sensor, forward-looking camera and inertial measurement unit sensors with an exploration algorithm to enable the robot to know where it is, where it has been and where it should go next.

“You can set it in any environment, like a department store or residential building after a disaster, and off it goes,” said Ji Zhang, a systems scientist in the Robotics Institute. “It builds the map in real-time, and while it explores, it figures out where it wants to go next. You can see everything on the map.”

The team has worked on exploration systems for more than three years, using a modified motorized wheelchair and drones for much of its testing. They’ve explored and mapped several underground mines, a parking garage, and several other indoor and outdoor locations on the CMU campus. And the system’s computers and sensors can be attached to nearly any robotic platform, transforming it into an explorer.

These robots can explore in three modes

(1) A person can control the robot’s movements and direction while autonomous systems keep it from crashing into walls, ceilings or other objects. (2) A person can select a point on a map and the robot will navigate to that point. (3) The robot sets off on its own, investigates the entire space and creates a map.

The new systems can also work in low-light, treacherous conditions where communication is spotty, like caves, tunnels and abandoned structures.

The group’s most recent work appeared in Science Robotics: “Representation Granularity Enables Time-Efficient Autonomous Exploration in Large, Complex Worlds.” More information: the group’s website.

Citation: Cao, C., Zhu, H., Ren, Z., Choset, H., & Zhang, J. (2023). Representation granularity enables time-efficient autonomous exploration in large, complex worlds. Science Robotics. https://doi.org/adf0970 (open-access)

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