Tumblenauts: Bacteria-Inspired Robot Swarm for Microgravity
We are living in an exciting time of space exploration, with a whole new set of space infrastructure being projected to be launched in the coming decade. As more space structures are launched into orbit, we need new ways to monitor the health of these structures and everything inside. We are developing a new bacteria-inspired robot swarm to do just that.
The Tumblenauts are the smallest and simplest robots ever designed for microgravity. Like bacteria, they random-walk through their environment, leveraging swarm algorithms to intelligently monitor space stations. We are actively testing the Tumblenauts in a range of exciting testbeds from a drop tower to parabolic flight. Stay tuned for more updates!
For more info, check out: Tumblenauts: A Minimalist Swarm for MicroGravity (ANTS 2026)
Read the full paper: ANTS2026Ramshanker.pdf
Self-Organized Robot Swarm Localization
Imagine a future where small robotic teams inspect our infrastructure—bridges, pipelines, buildings, and satellites—finding leaks and cracks promptly. To conduct effective inspections, however, the robots must first know precisely where they are on these structures, a problem known as localization. This task consumes a significant portion of a robot's computational resources. Our project delves into how collaboration among robots can streamline this process, freeing up more computational resources for inspection tasks. Drawing inspiration from nature's collaborative strategies, such as animals taking turns watching for predators, we introduce a novel mechanism where robots self-organize into dedicated localizers or inspectors, optimizing group inspection productivity. What's remarkable is that this sacrifice, with some robots bearing the computational burden of localization for the group, is entirely self-organized. The robots autonomously decide their roles, adapting to changing environments seamlessly. This approach, validated through theoretical models, simulations, and hardware experiments, showcases effective inspection in dynamic environments, highlighting the potential of autonomous, self-organized robots in making our infrastructure safer.
Check out my Princeton Research Day Talk.
Read the full paper: DARS2024Ramshanker.pdf (princeton.edu)
Swarm of Metal Climbing Robots
We have built a unique swarm of metal climbing robots that serve as a test bed for exploring different collective behavior algorithms for inspection purposes. The swarm includes 15 Rovables: mini 3-cm wide robots that can traverse metallic structures from any direction. Additionally, we have designed a larger metal climbing robot powered by the NVIDIA Jetson Nano, which is currently being used to generate SLAM-based maps of 3D manifolds. Our collective-localization algorithms have been successfully tested using the Rovable swarm.
Modelling Animal Vigilance
We are modeling anti-predator vigilance in social animals. There is a fundamental trade-off when it comes to vigilance: animals do not get to eat when they look for predators. It has been observed in nature that this cost of vigilance is shared amongst the group in social animals but how vigilance/foraging strategies are negotiated and organized is poorly understood. We are currently developing theoretical models to explain this behavior based on the insights we have found in modeling cooperative robotic swarms.
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