Mr. Kevin Pham Profile

Kevin Pham

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Publications (2)

Proceedings Article | 7 June 2024 Presentation + Paper

Route planning optimization for symbiotic swarm robotics

Benjamin Hand, Kevin Pham, Colleen Bailey

Proceedings Volume 13051, 1305109 (2024) https://doi.org/10.1117/12.3013973

KEYWORDS: Robots, Robotics, Machine learning, Algorithm development, Telecommunications, Sensors, Robotic systems, Mathematical optimization, Data storage

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Within the landscape of military technology, the time has come for automated systems to assume missioncritical responsibilities demanding computational speeds that are beyond human capabilities. Swarm robotics exhibits a diversity of applications in many fields including surveillance, reconnaissance, and more. Regardless of the specific application, meticulous planning and optimization of traversal routes coupled with seamless communication among individual robots assumes a pivotal role. Optimized cooperative route planning enables heightened operational efficiency, yielding a reduction in operational costs while elevating overall productivity. The significance of optimization extends to each robotic ensemble and an understanding of the intricacies of route selection fosters enhanced collaborative synergy. Consequently, the swarm attains a heightened capability to undertake intricate and multifaceted missions, transcending the limits of individual capabilities. Traditional approaches have restricted the potential of swarm-based systems. There has been little focus on cooperative route planning for swarms, essentially eliminating the advantages that swarm systems provide. We propose to rectify this imbalance by considering and addressing frequently overlooked variables of dynamic obstacles and cooperative route planning. Our approach leverages the fusion of advanced machine learning techniques and the reinforcement of communication channels among the constituent agents within the swarm. The deployment of efficient machine learning models facilitates real-time adaptation to the ever-changing optimal path, effectively mitigating the negative impact of dynamic obstacles in the mission environment. Furthermore, by reinforcing the communication infrastructure within the swarm, our approach fosters a heightened sense of synergetic collaboration among swarm agents. This approach unleashes the swarm robotics’ full potential by amplifying its efficiency and expanding the scope of possible applications, ushering in a new era of versatile and high-performing swarm robotic solutions.

Proceedings Article | 7 June 2024 Presentation + Paper

Practical real-time image compression for resource-challenged devices

Kevin Pham, Arthur Depoian, Colleen Bailey

Proceedings Volume 13033, 130330F (2024) https://doi.org/10.1117/12.3013994

KEYWORDS: Image compression, Image quality, Image processing, Information visualization, Image storage, Visualization, Machine learning, Data storage, Visual compression

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