The cable-driven hyper-redundant snake-like manipulator (CHSM) inspired by the biomimetic structure of vertebrate muscles and tendons, which consists of numerous joint units connected adjacently driven by elastic materials with hyper-redundant DOF, performs flexible kinematic skills and competitive compound capability under complicated working circumstances. Nevertheless, the drawback of lacking the ability to perceive the environment to perform intelligently in complex scenarios leaves a lot to be improved, which is the original intention to introduce visual tracking feedback acting as an instructor. In this paper, a cable-driven snake-like robotic arm combined with a visual tracking technique is introduced. A visual tracking approach based on dual correlation filter is designed to guide the CHSM in detecting the target and tracing after its trajectory. Specifically, it contains an adaptive optimization for the scale variation of the tracking target via pyramid sampling. For the CHSM, an explicit kinematics model is derived from its specific geometry relationships and followed by a simplification for the inverse kinematics based on some assumption or limitation. A control scheme is brought up to combine the kinematics with visual tracking via the processing tracking errors. The experimental results with a practical prototype validate the availability of the proposed compound control method with the derived kinematics model.
An Adaptive Optimization Method Based on Learning Rate Schedule for Neural Networks
