## 1 Introduction  
In the unstructured training fields such as golf, tennis, and table tennis, there are some disadvantages in picking balls such as high labor intensity, high risk, boring work, and low efficiency. Although the use of automated pickup devices such as dedicated discs [1] can improve picking efficiency, it is impossible to pick up objects between obstacles. How to realize the task of picking objects quickly in the obstacle environment, the pickup manipulator as the core part of the service robot is one of the effective picking up tools. However, at present, the pickup manipulator still has low work efficiency in the process of picking up objects, and it is difficult to avoid a collision in the obstacle environment [2]. Therefore, the trajectory optimization of the pickup manipulator in the obstacle environment needs to be solved urgently [3, 4]. The operation of the traditional manipulator is usually limited to the specified path or angular displacement range [5]. In 2018, [PERSON] et al. proposed a time-optimal path design method based on a predetermined end-effector path and discussed the time-optimal path following predefined end-effector paths for kinematically redundant robots [6]. [PERSON] et al. proposed a new method for trajectory planning of spraying for industrial robots in 2018. According to the contour trajectory of spraying, the proposed algorithm was used to define the motion law and limit the speed of the end-effector [7]. In 2018, [PERSON] et al. proposed a robotic time-pulse comprehensive optimal trajectory planning method. The fifth-order B-spline interpolation method is used to interpolate the motion trajectory in the joint space, and then the trajectory is optimized by the elite non-dominated sorting genetic algorithm (NSGA-II) for trajectory planning of the surgical robot [8]. In 2019, [PERSON] et al. proposed a smooth and time-optimal S-curve trajectory planning method to meet the requirements of the high-speed and ultra-precise operation of robotic manipulators in modern industrial applications [9]. However, these methods do not consider the influence of the unstructured obstacle environment. When the pickup manipulator is in an unstructured environment, the presence of obstacles may affect the pickup manipulator. These obstacles will collide with the pickup manipulator, leading to a decrease in the working stability of the pickup manipulator and even damage to the motor and other parts. [PERSON] proposed a unique real-time obstacle avoidance approach for manipulators and mobile robots based on the \"artificial potential field\" concept and implemented in the COSMOS system for a PUMA 560 robot in 1985 [10]. [PERSON] introduced the concept of a Rapidly-exploring Random Tree (RRT) as a randomized data structure that was designed for a broad loss of path planning problems in 1998 [11]. [PERSON] et al. proposed an improved RRT algorithm incorporating obstacle boundary information to deal with an optimal path in 2016 [12]. [PERSON], k et al. proposed a dynamic path planning method for robotic autonomous obstacle avoidance based on an improved RRT algorithm in 2018 [13]. This method is used for obstacle avoidance of robots in smart factories. In 2018, [PERSON], [PERSON] et al. proposed a dynamic trajectory planning method based on ACT-R (ideal rational adaptive control) cognitive model [14] and is used for active obstacle avoidance in electric vehicles. In 2019, [PERSON], [PERSON] et al. proposed a method for obstacle avoidance with pure azimuth measurement in the absence of a model of obstacle motion [15]. [PERSON], [PERSON] et al. proposed a dynamic collision avoidance method based on collision risk assessment and improved speed barrier method [16] for uncertain dynamic obstacle environments in 2017. [PERSON] et al. described a socially compliant path planning scheme for robotic autonomous luggage trolley collection at airports in 2019 [17]. However, these methods do not consider the time-optimal trajectory optimization, and the work efficiency needs to be improved.