Granulobots: Leveraging mechanical properties of a decentralized, multi-unit, dense robotic aggregate for sensorless tasks.

Designing robotic systems that can autonomously interact with their environment to solve tasks remains a major challenge. Conventional approaches use multi-sensor feedback loops to control displacements. This is often coupled with algorithmic and hardware complexity, which tend to be detrimental to energy efficiency, reliability, and form factor , all key aspects in autonomous systems. Here we use a new decentralized and modular robotic platform that we recently developed, Granulobot, to demonstrate tasks based on aggregate mechanical properties. Granulobots consist of active, gear-like particles that magnetically interact with each other and produce torques. They can self-assemble into aggregates that can reconfigure in real-time. The apparent complexity of a system with many degrees of freedom is made an advantage by leveraging the material-like properties of aggregates. In particular, aggregates can transition between rigid and liquid-like states with a wide range of effective viscosities. This enables the robot to grab objects as well as move in complex environments through holes and over obstacles by setting a single ”material” parameter, and without centralized control or real-time sensor-based feedback. Such minimal control, enabled by the modular design of Granulobots, advances robotic autonomy by exploring a morphological form of computation and feedback that greatly reduces the number of control parameters that must be taken care of by the embedded systems or operators.