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Effective Drone Control Simply by Thinking

The human mind, with some assistance, can now control multiple flying drones and terrestrial vehicles. Researcher and professor, Panagiotis Artemiadis from the Arizona State University, has developed a system capable of capturing and decoding a person’s thoughts, and executing the commands using one or more robots.

To learn and identify which brain signals to record and decode, the team focused on the concept of human swarm interaction and collective behaviors. The brainwaves are captured by a high-tech skull cap covered with 128 electrodes and wired to a computer. The data gathered through the helmet is sent to one computer for data collection and then processed by another using advanced algorithms. Finally, the thought-based command is served to the drone via bluetooth technology.

The two year project was funded by the Defense Advanced Research Projects Agency of the U.S. Department of Defense and the U.S. Air Force. The pilots who tested the system were originally skeptical with primary concerns regarding the drone execution of thoughts non-relevant to device operation. Indeed, the technology is best used when the operator is entirely focused on the task at hand and the study supports that lethargy or other constant thoughts may impact use of the system technology.

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A Dash of Maxwell’s: A Maxwell’s Equations Primer – Part Two

Maxwell’s Equations are eloquently simple yet excruciatingly complex. Their first statement by James Clerk Maxwell in 1864 heralded the beginning of the age of radio and, one could argue, the age of modern electronics.

Ten or 20 years from now, instead of having big expensive aircraft or drones, you can have hundreds or thousands of inexpensive ones you deploy in an area. Even if you lose half of them, you can still achieve your goals.

Panagiotis Artemiadis, Professor and Researcher, Arizona State University

The team intends to use this technology to further explore potential applications in ground vehicles, mobile robots, and other aerial vehicle quadrotors. Currently the team can control up to four robots and hopes to expand this capability to controlling tens to hundreds of robots.

 

Reference: Arizona State University

 

 

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