Article

Riemannian Optimization-Based Whole-Body Obstacle Avoidance Framework for Mobile Continuum Robots

The study proposes a unified planning and control framework for mobile continuum robots, combining a global layer consisting of A* search and minimum-jerk planning with a local layer consisting of a geometry-consistent inverse kinematics solver and Riemannian optimization, to coordinate the mobile base and continuum deformation for obstacle avoidance. The authors compare it with multiple baseline planners in simulated pipe-traversal tasks and conduct real-world experiments on navigation among dense obstacles and manipulation in confined spaces. Results show improvements in motion consistency, feasibility, and adaptability.

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