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About

Alexander Procenko is a robotics researcher specializing in fault detection and fault-tolerant control systems for autonomous robotic platforms. His work primarily focuses on enhancing the reliability and safety of robot manipulators and autonomous underwater vehicles (AUVs) through innovative sensor fusion and accommodation strategies. In his most cited work, "Fault Detection of Actuators of Robot Manipulator by Vision System" (2017, 4 citations), Procenko introduced a novel synthesis method that fuses data from stereo cameras, joint angle sensors, and desired joint variables to detect actuator faults in real time. This approach leverages vision systems to track markers on the robot, enabling precise fault identification without relying solely on internal sensors. His earlier research (2015, 3 and 2 citations) developed methods for synthesizing fault-tolerant systems that accommodate navigation sensor failures in AUVs, using kinematic models and data fusion to maintain operational integrity. Though his citation counts are modest, Procenko’s contributions are foundational for advancing robust autonomy in challenging environments, offering practical solutions for critical applications in underwater exploration and industrial robotics. His work underscores the importance of multi-sensor integration for resilient robotic systems.