A Snake-Inspired Collision-Free Path Planning Algorithm and Experiments for Hyper-Redundant Manipulators

The detection of complex and confined spaces has always been a significant challenge in specialized operations and the industrial sector. Hyper-redundant Manipulators , with their high flexibility and high fault tolerance, hold promising application prospects in complex constrained spaces. However, the hyper-redundant structure poses substantial challenges to its inverse kinematics, making it difficult to achieve precise control and trajectory planning in restricted spaces. This paper presents a Self-motion Searching and Clawing (SSC) algorithm to fast plan the collision-free path for the hyper-redundant manipulators. First, The algorithmic strategy of SSC, inspired by the movement of snakes, is introduced, and a physical experimental system is constructed to verify its efficacy. Second, the gravity compensation function is implemented to ensure the control accuracy of the experimental system under the interference of gravity. Third, various experiments are conducted to verify and analyze the capability of exploring the complex environments. As a result, both the straight pipe and the narrow curved pipe are successfully explored to the target positions without collision.