Lifelong Adaptive Neuronal Learning for Complete, Coverage Path Planning by an Autonomous Multi-Robot Team

Case ID:

This invention is an approach to neural networks solving both the human intervention problem and the power mitigation problem. Artificial Neural Tissue is a neural network with no centralized controller that allows a swarm of robots to cover a new area in near linear time. In this instance providing a solution to large spacecraft monitoring.  

UAVs, AUVs, artificial intelligence, machine learning, and swarm intelligence are the present and future of taking care of tasks difficult or expensive for humans to perform. And, as mankind branches into new zones like space, deep sea etc., it is not feasible for individuals to take care of many tasks like surveillance and testing required for exploration. 

And, while improvements seem to pour in daily, the idea of machine learning and swarm technology remain challenges to implement effectively and reliably. These become not only important, but necessary. Otherwise, one-to-one human control of the robots would be required which may solve problems of extreme conditions, but the necessity of a human controller sacrifices efficiency and progress.

Swarm technologies and machine learning algorithms sound like great solutions. The problem is most of these algorithms and groups of robots still require a central controller. In addition, there is a problem of efficiency of these robotic swarms: how does the swarm know when they are done? Can we cover an entire area without redundancies? These are the problems solved by this technology.


  • Spacecraft/small satellites
  • Deep sea exploration
  • UAV surveillance


  • No central controller
  • Proven few redundancies
  • Full coverage of complex geographic areas
Patent Information:
Contact For More Information:
Tariq Ahmed
Sr Licensing Manager, College of Engineering
The University of Arizona
Lead Inventor(s):
Byong Kwon
Jekan Thangavelautham