Integrated Design of a Mobile Rigid-Flexible Coupled Snake-Like Manipulator and Diffusion Policy-Based Motion Planning

Abstract

Conventional snake-like manipulators are plagued by limited mobility, inadequate flexibility, low load-bearing capacity, and inefficient motion planning when operating in constrained 3D environments. To address these challenges, this paper proposes a rigid-flexible coupled mobile snake-like manipulator along with an enhanced diffusion policybased follow-the-leader motion planning method. The proposed manipulator integrates an all-terrain mobile chassis, a threejoint rigid manipulator, and a seven-joint flexible manipulator actuated by a stepped double-screw cable system; it adopts rigid inverse kinematics and flexible hierarchical kinematics to achieve high-precision trajectory tracking. Derived from the Denoising Diffusion Probabilistic Model (DDPM) framework, the planning method fuses PointNet-based scene encoding, cross-attention feature fusion, and trajectory post-processing techniques (including endpoint correction, trajectory pruning, and smoothing), which collectively enhance its adaptability to environmental constraints and engineering practicability. Experimental results confirm the integrated system exhibits outstanding performance in load-bearing capacity, motion accuracy, and trajectory tracking. Specifically, the proposed method attains a 79.6% planning success rate, outperforming state-ofthe-art baselines by a margin of 10%–20%, while delivering optimized trajectory length, obstacle clearance, and smoothness. This work offers an efficient and robust solution for motion planning of confined-space robots, and advances the application of generative AI in bionic motion planning.