Bio-inspired Excavator Digging Trajectory Planning: Insights from Mole Digging Patterns

Abstract

The automatic and rapid generation of excavation trajectories is the foundation for achieving an intelligent excavator. To obtain high-performance trajectories that enhance operational capacity while avoiding the numerous issues present in existing methods for generating effective excavation paths, this paper proposes a trajectory generation method for excavators based on imitation learning, using the mole as a bionic prototype. Given the high excavation efficiency of moles, this paper first analyzes the structural characteristics of the mole’s forelimbs, its digging principles, morphology, and trajectory patterns. Subsequently, a higher-order polynomial is employed to fit and optimize the mole’s excavation trajectory. Next, imitation learning is conducted on sample trajectories based on Dynamic Movement Primitives, followed by the introduction of an obstacle avoidance algorithm. Simulation experiments and comparisons demonstrate that the mole-inspired trajectory method used in this paper performs well and possesses the ability to generate obstacle avoidance trajectories, as well as the convenience of transferring across different machine models.