# Visual Tracking Control of Cable-Driven Hyper-Redundant Snake-Like Manipulator
## 1 Introduction  
Robotic arms have been widely developed in the industrial manufactured application and gradually reduced human participation since the first sophisticated robotic arm was designed by [PERSON] in 1495. There is a definite trend in the manufacture of robotic arms toward more dexterous devices, more degrees of freedom (DOF), and capabilities beyond the human arm [1]. The redundant DOF tends to perform more competently under some complicated scenarios such as narrow deep cavities or underwater pipe networks, especially with the discrete sets of joints imitating the structure of muscles and tendons with similar functions [2]. High flexibility of avoiding obstacles, good loading capacity, and easy maintenance should be taken into consideration for the structural design of robotic arms to satisfy the requirement of tasks under complicated working circumstances. A snake-like continuum manipulator with redundant DOF inspired by the biomimetic structure of vertebrate limbs has attracted increasing attention of researchers but limited to the drawbacks such as slight load capacity, imprecisely controlled and limited measurements, alleviated after the cable-driven technique introduced [3]. Cable-driven hyper-redundant snake-like manipulator (CHSM) which has numerous joint units connected adjacently driven by elastic cables with hyper-redundant DOF can achieve the desired positions with different postures [4], which means the flexible motion and good obstacle avoidance capability under complicated working circumstances [5]. The biggest difference from traditional rigid manipulators composed of actuated jointsis that the CHSM has no driving actuator located in joints, which leads to the rather intractable issue of inverse kinematics [6; 7]. [PERSON] et al. [8] proposed an approach based on the Product-Of-Exponential (POE) formula to model instantaneous kinematics for a cable-driven snake-like manipulator, computing the numerical solution of inverse kinematics via the Newton-Raphson method. [PERSON] et al. [9] involved the cyclic coordinate descent method named natural-CCD to simply achieve the best result among the innumerable solutions for the inverse kinematics, mapping the hyper-redundant DOF into three spatial dimensions. Since the snake-like manipulator has different kinematic and dynamic behaviors from traditional rigid robots, it is worthy to develop a visual servo system that can improve stability and precision for controlling [10]. Furthermore, the computer vision techniques endow the traditional robotic arms with the ability to perceive the environment to perform intelligently in complex scenarios. [PERSON] et al. [11] achieved great success in underwater tasks, in which the computer vision techniques play a critical role as the bridge between robotic arms and human cognitive guidance. Considering the advantage of cable-driven snake-like manipulator, visual tracking is recommended as an assistant target-oriented method for guiding the CHSM to lock on mission objectives under tasks such as salvage or rescue, especially in the scenarios such as narrow and tortuous caverns or pipelines. Most research achievements in the visual tracking field have blossomed over the past few decades, divided into two branches called generative model [12; 13; 14] and discriminative model [15; 16]. The generative approaches establish models or templates with respect to the area of target in the current frame, aim at describing the targets' appearance and find the most similar areas in the next frame as the estimated new position (e.g., Kalman filtering [17], Particle filtering [18], Mean-shift [19], etc.). The discriminative approaches depend on the extracted features combined with learning evolution, discriminate between the target and background in the current frame as a positive sample and negative sample respectively, and aim at using a classifier training by machine learning algorithm such as SVM to estimate the optimum areas in the subsequent frames.