# A Dynamic Multiple-Query RRT Planning Algorithm for Manipulator Obstacle Avoidance
## 1 Introduction  
Autonomous robots are widely used for agriculture, manufacturing, aviation, etc. The intelligent factory is a man-machine hybrid system that has substantial economic benefits in production [1, 2]. As for the robot, it is a significant challenge to work in a dynamic environment [3]. Obstacle avoidance methods require feasible solutions, such as processing information, data delivery, computational treatment, etc., within a stipulated period [4]. Especially in computational process, the collision is caused by unpredictable obstacles, e.g., operators appearing in the workspace with manipulators. Therefore, an efficient real-time planner is demanded in a dynamic environment, where the appearance, movement and disappearance of obstacles are not a priori conditions [5]. Path planning and trajectory planning are two types of tasks for the manipulator in the planning layer that typically generates an executable trajectory [6, 7]. Once dynamic obstacles are affected while executing this trajectory, a new trajectory is planned to avoid the dynamic obstacles. Compared with the new trajectory, the initial trajectory is called as the previous trajectory. In this paper, a dynamic replanning frame and a dynamic bias-goal factor, rapidly exploring random tree (DBG-RRT) motion replanning algorithm, are introduced. In a static environment, the BG-RRT algorithm is efficient, and the dynamic strategy is introduced to support the robot in real-time replanning [8, 9]. The proposed method does not distinguish between local and global planning, and gives priority to fast convergence to ensure a high success rate. As for the sampling-based method, the RRT algorithm is widely used [10]. According to the characteristics of the RRT algorithm [11], different evolution algorithms are proposed to satisfy unpredictable or moving environments. [PERSON] et al. [12] proposed the execution extended RRT (ERRT) algorithm with efficient performance in mobile robots using the way-point cache approach. [PERSON] et al. [13] reused the previous path to propose a dynamic RRT (DRRT) algorithm. The new path grew to the root of the previous path. The RRT*FN dynamic (RRT*FND) algorithm was suggested by [PERSON] et al. [14]. The useful sectionof the extension tree was clipped and maintained, and the regrow and reconnect methods identified a new solution route. This strategy, however, would need some computations. [PERSON] et al. [3] presented the RRT\({}^{X}\), which was an asymptotically optimal and single-query sampling-based motion replanning algorithm. The RRT\({}^{X}\) algorithm respected the kinematics of the robot and was asymptotically optimal during navigation. [PERSON] et al. [15] used the dueling DQN algorithm to solve the navigation under unknown environments, and the proposed algorithm had a good performance for autonomous navigation in a complex environment. [PERSON] et al. [16] focused on task-restricted conflict resolution. They demonstrated an adaptive motion planning strategy for the manipulator to solve a variety of dynamic situations. [PERSON] et al. [17] proposed frontier exploration planning as a global planner and best-view planning under the receding horizon as a local planner. The experimental results showed the proposed method can quickly explore large unknown environments without falling into a local minimum. The DBG-RRT algorithm, which is a multiple-query sampling-based motion replanning algorithm, is proposed to provide a high success rate and stable performance in dynamic environments. The main contributions are concluded as follows:  
* In order to move continuously in dynamic environments, the concept of a relay node based on motion planning is introduced to generate a new no-collision trajectory according to the position, robot's velocity, and the predicted velocity of moving obstacles. * The connection strategy is designed with the reconnect, regrow, and un-interrupt strategy to make up all possible replanning cases and fix the interrupted planning in dynamic obstacle avoidance.