An international team of scientists have stated recently in the journal Chaos, from AIP Publishing that a new energy-efficient data routing algorithm formulated by them could keep unmanned aerial vehicle swarms flying and aiding much longer.
UAV-aided medical assistance system architecture including base stations and a UAV swarm, with UAVs closest to the base stations acting as relay nodes for otherwise out-of-range UAVs. (Image credit: Wuhui Chen)
UAV swarms are cooperative, intercommunicating groups of UAVs used for a broad and growing range of military and civilian applications. In disaster response, especially when local communications infrastructure is wrecked, UAV swarms connected to one or more local base stations serve as eyes in the sky, offering vital damage and survivor information to first responders.
The battery capacity of UAVs is a critical shortcoming that limits their usage in extended search and rescue missions.
Wuhui Chen, Study Co-Author and Scientist, Sun Yat-Sen University
The majority of a UAV’s energy use can be linked to high bandwidth and long transmission times — visualize the drain on the battery of a phone in such cases. To sort this issue, Chen and colleagues have formulated a UAV swarm data routing algorithm that uses the strength of the group to increase real-time transmission rates and decrease individual UAV battery challenges.
Their new hybrid computational method integrates linear programming and a genetic algorithm to form a “multi-hop” data routing algorithm. A genetic algorithm deciphers chaotic optimization problems using an analog of natural selection, the process that pushes biological evolution.
In real time, the new adaptive LP-based genetic algorithm (ALPBGA) finds the lowest communications energy route among a swarm and concurrently stabilizes individual UAV power use, for example, by establishing which UAV will transmit information to a base station.
By balancing power consumption among the UAVs, we significantly enhance the ability of the whole system. Our simulations show that our approach can outperform the existing state of the art methods.
Patrick Hung, Study Co-Author, University of Ontario Institute of Technology
These computer simulations reveal that, particularly as swarm size increases from tens to hundreds of UAVs, ALPBGA decreases the number of UAVs that stop communicating by 30% to 75% compared to current leading UAV swarm communication algorithms.
“We believe the results of our research will inspire others to design more energy-efficient UAV communication systems,” said Chen, who plans to extend the ALPBGA research to improve it within the context of diverse swarm flying trajectories.