RAID-AgiVS: A Bioinspired Reciprocal Perceptual Control Framework for Agile Visual Servo

The agility of visual servo systems is essential for efficient operation in dynamic environments. However, this agility is limited by uncertainties, perceptual limitations, and the failure to integrate perception and control in conventional frameworks. In this article, a bioinspired reinforced active-inference-driven agile visual servo (RAID-AgiVS) technology is presented to systematically elude the above limitations. Central to RAID-AgiVS is the reinforced sensing mechanism that augments sparse visual measurements with fast soft measurements generated by alternating prediction and observation; thereby producing high-bandwidth data that surpasses the native sampling rate of the visual sensor. In addition, the reinforced data are employed by an active inference controller - embedded within a reciprocal perceptual control structure - to introduce perceptual inference and active control actions that bolster adaptability and agility in visual servo tasks. By fusing biological adaptability with advanced data generation and analytic modeling, the RAID-AgiVS framework markedly improves performance, particularly under sensory limitations and dynamic uncertainties. Comparative evaluations on a 6-DoF manipulator - and an indoor quadrotor unmanned aerial vehicle (UAV) - demonstrate the superior accuracy, agility, and adaptability of the proposed method.