# An Efficient RRT Algorithm for Motion Planning of Live-Line Maintenance Robots
###### 1 Introduction  
Live-line maintenance includes replacing or testing of power system equipment without interrupting the current. It can effectively improve power supply reliability, reduce outage losses, ensure grid safety and reduce economic losses. However, the risk of electric shock is very high when the work is performed manually. With the development of robotics, robotics have been gradually applied to the electric power industry since the 1980s to replace human beings in performing high-risk operation tasks [1]. Early robotic systems for live-line maintenance include the remote-controlled robot system of Quebec Hydropower in Canada [2; 3], the Spanish remote robot system ROBTET [4], and the Japanese robot phase series [5]. These robot systems are all operated remotely, and an operator manually controls a robot arm to perform complicated tasks. In live-line working scene, manually operating a robot for live-line maintenance is an inefficient, dangerous and difficult task. Automatic robot motion planning instead of manual operation can effectively improve operational efficiency and reduce risks. Robot path planning algorithms include graph search [6; 7], visibility graph [8; 9], artificial potential field [10; 11], probabilistic roadmap [12; 13], and rapid exploring random tree (RRT) [14]. RRT and extended algorithm are widely used in the planning problem of multi degrees of freedom (multi-DOF) robot. Compared with other planning methods, this method is more suitable for dynamic obstacle avoidance path planning of a manipulator in high dimensional space under a complex environment. Moreover, RRT is applicable to the motion planning problem under kinematic constraints [15]. RRT was first proposed by [PERSON] in 1998 for solving high-dimensional space problems with algebraic constraints (imposed by obstacles) and differential constraints (imposed by nonintegrity and dynamic environments). RRT samples points in the planning space,and eligible points are added to a tree. As the number of samples increases, the tree is expanded to the entire space. The RRT method does not need to map obstacles to the joint space, and it is more suitable for dynamic obstacle avoidance path planning of robots in high-dimensional space in complex environments than other planning methods. It is a probabilistically complete path planning method with good scalability [14; 16]. The paths planned by RRT have randomness and are not optimal solutions. The randomness of sampling also reduces the speed of convergence of the algorithm. In response to the shortcomings of RRT, researchers have proposed many improvements. RRT*[17; 18] adds a reconnection process to the original method to obtain a sub-optimal solution. However, the reconnection method has a slower convergence speed, especially in larger planning spaces. Informed RRT*[19; 20] improves the convergence speed of RRT* by centralized sampling. [PERSON] provides the RT-RRT* algorithm to further improve operations, making it possible to complete real-time path planning. The RRT-connect [21; 22; 23] algorithm uses two trees growing in opposite directions and speeds up the search with a greedy strategy. The method saves search time by reducing useless searches in blank areas. In addition, there are many other variants of RRT algorithms, such as the parallel-RRT [24] algorithm that uses GPU parallel computing to accelerate RRT solution speeds, real-time RRT [25] that improves the real-time performance, and dynamic domain RRT [26] for dynamic environments. Most RRTs and their algorithms are designed for robot planar motion planning and are not suitable for robots with multiple DOFs. In the multi-DOF robot motion planning problem, the existing RRT algorithm has the following problems: (1) The expansion of RRT requires frequent collision detection calculations. To obtain a better path, it is usually necessary to increase the number of samples. This leads to too long planning times. (2) The uncertainty brought about by the results of motion planning increases the risks in operation processes.