In the research article “Supervised morphogenesis: exploiting morphological flexibility of self-assembling multirobot systems through cooperation with aerial robots”, Nithin and his colleagues from multiple research labs and the Fraunhofer Institute present results of two case studies using two different autonomous aerial platforms and up to six self-assembling autonomous robots. The research is a significant step towards realizing the true potential of self-assembling robots by enabling autonomous morphological adaptation to unknown tasks and environments.

Existing self-assembling robots are often pre-programmed by human operators who precisely define the scale and shape of target morphologies to be formed before deployment. Alternatively, robots rely on specific environmental cues to infer target morphologies. This is primarily because self-assembling robots tend to be relatively simple robotic units. They lack the sensory apparatus to characterize the environment with sufficient accuracy to find a suitable target morphology for a given situation.

Supervised morphogenesis is a novel approach that enables aerial robots to provide assistance to ground-based self-assembling robots. The aerial robots have decision-making authority and extend the functionality of a group of self-assembling robots. That is, self-assembling robots rely on the aerial robot to act as an “eye-in-the-sky" and to provide the guidance required to form new morphologies fit for the task and/or the environment. A key feature of supervised morphogenesis is its high portability to other systems because it does not depend on proprietary hardware and can be implemented using standard cameras, LEDs, and wireless Ethernet-based communication available to most robotic platforms.

The paper also confirms that the presented control methodology can provide performance benefits to heterogeneous robot groups in term of task completion times. The methodology enables aerial robots to allocate precise number of resources needed for a target morphology by recruiting robots based on their location on the ground and based on their mutual proximity while freeing up the rest of the group to pursue other tasks.

Before joining Netcetera, Nithin was a researcher at IRIDIA – the artificial intelligence research laboratory of the Université Libre de Bruxelles in Belgium. To finalize the research presented in the article, Nithin received support from Netcetera in the form of educational days and from Wallonia-Brussels-International (WBI) through a Scholarship for Excellence grant. The article is a continuation of Nithin’s previous research on robots with “mergeable nervous systems”.