Most studies on path planning of robotic arm focus on obstacle avoidance at the end position of robotic arm, while ignoring the obstacle avoidance of robotic arm joint linkage, and the obstacle avoidance method has low flexibility and adaptability. This paper proposes a path obstacle avoidance algorithm for the overall 6-DOF robotic arm that is based on the improved A* algorithm and the artificial potential field method. In the first place, an improved A* algorithm is proposed to address the deficiencies of the conventional A* algorithm, such as a large number of search nodes and low computational efficiency, in robotic arm end path planning. The enhanced A* algorithm proposes a new node search strategy and local path optimization method, which significantly reduces the number of search nodes and enhances search efficiency. To achieve the manipulator joint rod avoiding obstacles, a method of robotic arm posture adjustment based on the artificial potential field method is proposed. The efficiency and environmental adaptability of the robotic arm path planning algorithm proposed in this paper are validated through three types of simulation analysis conducted in different environments. Finally, the AUBO-i10 robotic arm is used to conduct path avoidance tests. Experimental results demonstrate that the proposed method can make the manipulator move smoothly and effectively plan an obstacle-free path, proving the method’s viability.

In this article, hybridization of IWD (intelligent water drop) and GA (genetic algorithm) technique is developed and executed in order to obtain global optimal path by replacing local optimal points. Sensors of mobile robots are used for mapping and detecting the environment and obstacles present. The developed technique is tested in MATLAB simulation platform, and experimental analysis is performed in real-time environments to observe the effectiveness of IWD-GA technique. Furthermore, statistical analysis of obtained results is also performed for testing their linearity and normality. A significant improvement of about 13.14% in terms of path length is reported when the proposed technique is tested against other existing techniques.