Motion planning for hyper-redundant manipulator systems: combining path following and improved APF-RRT* Path finding

Hyper-redundant manipulator systems (HRMSs) have significant applications in various scenarios. Due to their excessive degrees of freedom (DOF), they can navigate into confined spaces and perform tasks such as exploration, detection, and maintenance. However, its flexibility also renders its motion planning highly challenging. This article proposes an efficient motion planning method for HRMSs, based on path finding and path following. First, the characteristics of a typical HRMS are introduced, and the kinematic model of the HRMS is established. Following that, a path tracking algorithm for the HRMS is introduced. This algorithm integrates a ‘follow the leader’ (FTL) approach with traditional methods to enhance path following efficiency. Thirdly, an improved APF-RRT* algorithm is proposed. This algorithm aims to find suitable paths in the workspace while reducing algorithmic dimensionality. It combines the advantages of both APF and RRT* algorithms. Additionally, an angle correction force field is incorporated for high efficiency. Several sampling-based methods are optimized and compared for analysis. Simulation results show that Improved APF-RRT* can efficiently solve the path planning problem of the HRMS, especially in complex environments. In addition, the smoothing method based on Bezier curves makes the path perform better.