Networked Robots to Execute Tasks as a Team
#BotTeam
Networked Robots to Execute Tasks as a Team
#BotTeam
Research Terms
Artificial Intelligence Computer Networks Communications Engineering Electronics Systems Ocean Engineering Information Theory Biology Applied Mathematics Computational Complexity Discrete Mathematics Mathematical Optimization Mathematical Statistics Electronic Equipment and Devices Imaging Technology
Industries
Ocean Microelectronics & Computer Products Software & Computer Systems Design Telecommunications
Center for Connected Autonomy and Artificial Intelligence (CA-AI)
| Director |
Dimitris Pados |
| Phone | 561-297-2988 |
| Website | http://eng.fau.edu/research/c2a2/ |
| Mission | The mission of the Center is to carry out fundamental and applied research and support workforce development to build future connected autonomous systems and team-AI solutions based on (i) autonomous resilient machine-to-machine wireless communications, (ii) robust team (multi-agent) AI training, and (iii) real-time operational stage monitoring for safety and security. |
Wireless communication systems that rely on Orthogonal Frequency Division Multiplexing (OFDM) increasingly suffer from co-channel interference, narrowband interference, multipath fading, and noise in congested and contested spectrum environments. Current mitigation approaches—such as static subcarrier nulling, fixed windowing, notch filtering, and high-complexity MIMO or iterative receiver processing—are often ineffective under time-varying interference conditions. These methods either waste valuable spectrum, increase latency and power consumption, or require hardware-intensive implementations, limiting their ability to maintain high signal-to-interference-plus-noise ratio (SINR) and reliable throughput in real-world deployments.
Researchers at Florida Atlantic University have developed an adaptive waveform shaping technique for OFDM systems that dynamically optimizes the transmitted signal to maximize signal-to-interference-plus-noise ratio (SINR) in real time. Unlike conventional approaches that rely on static filtering, subcarrier deactivation, or hardware-intensive receiver processing, this innovation applies optimized coding at the inverse discrete Fourier transform (IDFT) stage, allowing the waveform to adapt directly to measured channel and interference conditions. Simulation and prototype software-defined radio studies demonstrate SINR improvements of up to 8.5 dB under heavy interference. The technology is currently at the algorithm validation and simulation stage, with feasibility demonstrated in software-based implementations and positioned for further development and system-level integration.
FAU seeks to advance this innovation into the marketplace through licensing or development partnerships.
Adaptive Waveform Shaping for SINR-Optimal Interference Avoidance in OFDM Systems
Unmanned vehicles in both terrestrial and extraterrestrial environments can be remotely guided and controlled by radio frequency signals. Autonomous vehicles are also frequently equipped with LiDAR (light detection and ranging) technology for mapping, obstacle detection and navigation. In the case of autonomous unmanned underwater vehicles, radio frequency and optical signals do not penetrate far enough into water. Consequently, functionalities that are enabled by radio frequency signals, such as remote control and GPS-based navigation are not available for underwater vehicles. This limitation has led to the use of sound waves for underwater wireless communication and positioning. Acoustic signals can reliably travel long distances through water, but require the use of low frequencies, which in turn, limits available bandwidth.
Researchers at FAU have developed an estimation algorithm that uses acoustic signals with a direction-of-arrival technique for localization. This technology allows for the infrastructure-less 3D self-localization of multiple autonomous underwater vehicles with no reliance on GPS nor on availability of global clock synchronization. The vehicles leverage simple coded beacon signals to self-localize. Self-positioning and automated neighbor discovery of peers in multi-vehicle missions will support position-aware geographic data routing, directional communication links, swarming, collaborative sensing, coordinated sampling and vehicle navigation. The invention has been validated through simulation in statistically modeled underwater acoustic communication channels and water-tank experimentation, which resulted in significantly superior positioning accuracy compared with state-of-the-art estimation techniques.
FAU is seeking partners to advance this technology into the marketplace through licensing or development partnerships.
Airborne Cognitive Networking: Design, Development, and Deployment
Governments and commercial groups are rapidly developing highly sophisticated unmanned undersea vehicles for various applications like deep sea exploration, energy pipelines and defense. While there have been many technological advances, one of the primary challenges is the lack of high-speed and reliable communication components to control and monitor these vehicles.
Researchers at Florida Atlantic University have developed a novel underwater modem offering wideband acoustics, real-time configuration, and wireless communications for underwater vehicles. A prototype of the device has been developed and tested within an underwater environment showing proof of concept using binary frequency shift keying and fast frequency hopping communications protocols.
FAU seeks to advance this innovation into the marketplace through licensing or development partnerships.
A First-of-its-kind Low Size, Weight and Power Run-Time Reconfigurable Underwater Modem
