PccDiffuser: Multi-solution Motion Planning for Continuum Robots

• URL: http://arxiv.org/abs/2609.09745v1
• Date: Wed, 09 Sep 2026 05:41:27 GMT
• Title: PccDiffuser: Multi-solution Motion Planning for Continuum Robots
• Abstract summary: PccDiffuser is a conditional diffusion framework for continuum robots.<n>It learns a multimodal distribution over complete configuration-space paths and samples multiple candidate solutions in parallel.<n>It is converted into an executable trajectory by time allocation considering actuator constraints.
• Score: 24.360111540705002
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present the PccDiffuser, a conditional diffusion framework for continuum robots that learns a multimodal distribution over complete configuration-space paths and samples multiple candidate solutions in parallel, which are subsequently converted into an executable trajectory by time allocation considering actuator constraints. Under the piecewise constant-curvature model, we use exponential co-ordinates to describe the robot kinematics, and use graph neural network to encode a variable number of environment obstacles. Analytical differential kinematics is incorporated in the denoising process to improve terminal accuracy and whole-body clearance. On a mixed test set comprising workspace with zero to four obstacles, PccDiffuser achieved a success rate of 91\%. Compared with existing sampling- and optimisation-based benchmarks, it delivered both a higher success rate and greater computational efficiency, with the latter advantage becoming more substantial when sampling more candidate solutions. Experiments on a three-section tendon-driven continuum robot further demonstrate consecutive planning, multi-solution planning, and whole-body obstacle avoidance.

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