Adaptive Vision-Based Control for Flexible Robot with Unknown Environmental Constraints

Flexible robots have gained considerable attention due to their advantages over rigid robots, such as an infinite number of degrees of freedom and the ability to operate in diverse environments. However, controlling flexible robots remains challenging, particularly in unknown and constrained settings. Sensor-based shape reconstruction and distal pose estimation for flexible robots present significant challenges, especially in medical applications. In this article, we propose an adaptive controller designed to address the challenges of flexible robot visual servoing in constrained environments, relying solely on image feedback. We validate our method through simulations and compare its performance with traditional model-based and adaptive approaches. The results demonstrate that our controller not only improves the robot’s robustness but also provides a promising solution for real-time applications in minimally invasive surgeries and other delicate operations requiring high dexterity.