# Autonomous Operation Method of Multi-DOF Robotic Arm Based on Binocular Vision
## 1 Introduction  
Mechanical arm technology has been widely used since the development of science and technology in the 20 th century. However, the traditional robotic arm mostly completes the corresponding work in accordance with an artificially set working mode. Therefore, it cannot adapt to changes in the working environment, which greatly limits its application and functionality. To solve the above problems, researchers have begun introducing machine vision into the application of robotic arms. As such, robotic arms can now sense the external environment to adapt to changes and operational requirements. An example is a robotic arm for picking fruit in agriculture [1]. [PERSON] et al. combined binocular vision with dual robotic arms to design a robotic system for tomato picking; this robotic system has been used for harvesting tomatoes in real agriculture and demonstratedgood results [2]. [PERSON] et al. conducted a visual positioning study on dynamic litchi clusters with disturbances in the natural environment; they used improved fuzzy c-means clustering method to segment the image, which enabled them to obtain litchi fruits and stems and realize the robotic arm automatic picking of litchi [3]. [PERSON] et al. detected and located kiwifruits by combining machine vision and convolutional neural network techniques; they achieved the autonomous harvest of kiwifruits by combining multiple robotic arms, thereby greatly saving manpower [4]. [PERSON] et al. used visual robotic arm technology to classify surgical instruments and address the low efficiency of manual classification, thereby greatly improving work efficiency [5]. [PERSON] used the region-convolutional neural networks (R-CNN) method to classify construction waste and combine visual technology with robotic arm to complete the independent recycling of construction site waste [6]. For robotic arms to work independently, the target-grabbing object must initially be extracted and positioned. The current most common positioning method is binocular vision positioning based on the principle of parallax. This method has been widely used in related fields locally and internationally. [PERSON] et al. developed a tractor path tracking the control system on the basis of binocular vision to meet the agricultural needs of cotton field operation management [7]. [PERSON] et al. proposed a slope displacement measurement method with high precision for displacement monitoring on the basis of binocular vision [8]. [PERSON] et al. proposed a method following the principle of binocular vision measurement applied to the deformation and strain measurement of concrete filled steel tube, and obtained good results [9]. Binocular vision measurement also plays an important role in many fields, such as mechanical manufacturing, image quality assessment, and path detection [10; 11; 12]. In practical applications, the operation of robotic arms must meet the requirements of time, space, work efficiency, and other factors. Therefore, robot arms should be able to adjust their motion trajectory autonomously. Many mature methods are currently applied to the trajectory planning of robot arms. [PERSON] et al. proposed a robot singular trajectory tracking control method with good trajectory tracking performance on the basis of genetic algorithm [13]. [PERSON] et al. studied the application of the particle swarm optimization (PSO) strategy in the coordinated trajectory planning of dual-arm space robots in free-floating mode, and realized the coordinated control of the dual-arm space robot system [14]. [PERSON] proposed a method for optimizing the trajectory planning and layout of a given path in a redundant coordinated robot working unit using PSO, and achieved good experimental results [15]. [PERSON] et al. proposed a trajectory planning algorithm that can minimize the disturbance of a spacecraft to a spacecraft base for a dual robotic space robot [16].