PccDiffuser plans multiple safe paths for soft robots in cluttered spaces
PccDiffuser: Multi-solution Motion Planning for Continuum Robots
Robotics
Summary
Planning how soft, bendy robots move through spaces with obstacles is hard because these robots can curve in many ways. The authors introduce PccDiffuser, a new method that learns many possible safe paths simultaneously and picks the best ones quickly. It uses math models for how the robot bends and a type of neural network to understand obstacles. Tests show it finds good paths more often and faster than older methods, even with multiple obstacles.
What this means in practice
- •For robotics engineers: Plan multiple executable paths for continuum robots navigating cluttered environments to improve motion safety and efficiency.
- •For medical device developers: Generate precise motion plans for soft surgical robots that must avoid obstacles like tissues during minimally invasive procedures.
Authors
Ke Qiu, Sifan Chen, Si Wang, Rong Xiong, Yue Wang, Haojian Lu
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.