# Obstacle Avoidance Path Planning for the Dual-Arm Robot Based on an Improved RRT Algorithm
## 1 Introduction  
With the development of science and industrial automation, robot technology has been greatly developed in recent decades, and gradually applied in military, aerospace, industry, medical, service, and other fields [1, 2]. Single-arm industrial robots have achieved notable development and application in China, widely replacing manual casting, welding, patletizing, and other operations [3, 4]. However, many complex operational tasks require collaboration between the robotic arms. The dual-arm robot has a larger working space, stronger load capacity, and obvious advantages in heavy lifting and assembly scenarios. However, unlike a simple combination of two single-arm robots, a dual-arm robot has some overlap in its workspace. The path planning of two arms should not only consider static obstacles in space, but also consider the interference between the two arms. In the field of dual-arm robotics, how to realize obstacle avoidance motion planning is always a hot issue [5, 6]. In the field of robot path planning, many path planning algorithms have been formed. The traditional methods mainly include the artificial potential field algorithm [7, 8, 9], the A* algorithm [10, 11], and the RRT algorithm [12, 13], etc. The methods based on computational networks mainly include neural network algorithms [14, 15] and bioinspired planning algorithms [16, 17]. Bioinspired planning algorithms mainly include the genetic algorithm, ant colony optimization (ACO), and so on. The genetic algorithm is an intelligent bionic algorithm based on natural selection and genetic mechanisms [18]. The ACO is an intelligentoptimization search model established with reference to the foraging method of ant populations [19; 20]. However, in the joint space of a manipulator with high degrees of freedom, each movement of the arms will generate a high computational workload. Algorithms based on computational networks have problems such as excessive computational load and insufficient real-time performance in the field of dual-arm robot path planning. Among the traditional path planning algorithms, the artificial potential field method has the characteristics of simple implementation and good real-time performance, and has been widely used. Nonetheless, once the resultant force is zero, that is, at the local potential energy minimum point, the algorithm will fall into a deadlock state and stop searching [21]. In addition, the artificial potential field algorithm needs the sampling information of the entire workspace to avoid local minima, which affects the dynamic obstacle avoidance of the dual-arm robot. The A* algorithm is a method for solving the shortest path in a known static road network. This method is a search algorithm obtained by adding a heuristic function to the Dijkstra algorithm [22]. It evaluates the nodes and guides the search process based on the evaluation value. [PERSON] et al. applied a PRM (probabilistic road map) to multi-robot path planning, but the increase in degrees of freedom and obstacles reduced the speed of the method [23]. In the field of multi-axis robot planning, due to the constraints of complex environments, most search methods are based on random sampling. Among the path planning methods used in the past few decades, the RRT algorithm, based on random search strategy, is suitable for path planning in high-dimensional space, and has been widely used in the path planning of robots [24; 25; 26]. The algorithm avoids modeling the entire space by detecting collisions at sample points in the state space. Therefore, it can effectively solve path planning problems with complex constraints in high-dimensional space, and has the advantages of probabilistic completeness and perfect scalability. RRT is a fast search algorithm proposed by [PERSON] [27]. However, it is a random search algorithm, and the search path may not be in the direction of the target, therefore the convergence speed is relatively slow. In order to improve the target orientation of the RRT algorithm, [PERSON] et al.