Riemannian Optimization-based Whole-body Obstacle Avoidance for Mobile Continuum Robots in Cluttered Environments
Riemannian Optimization-based Whole-body Obstacle Avoidance for Mobile Continuum Robots in Cluttered Environments
This paper presents a unified planning and control framework inspired by biological systems like snakes and octopus arms, designed for mobile continuum robots operating in cluttered environments. The approach integrates long-horizon trajectory planning with real-time geometric refinement using A* search, minimum-jerk planning, and Riemannian optimization to coordinate base motion and continuum deformation while avoiding obstacles. Evaluation includes simulated pipe-traversal tasks and real-world experiments demonstrating improved motion consistency and adaptability.
The authors propose a unified planning and control framework integrating A* search, minimum-jerk planning, and Riemannian optimization for mobile continuum robots; evaluation is reported via simulated pipe-traversal tasks and real-world experiments in cluttered environments.
Source: arXiv Robotics — research abstracts · Read original article ↗
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Source：arXiv Robotics — research abstracts · arxiv.org