Quasi-static Path Planning for Continuum Robots By Sampling on Implicit Manifold.

Area of Science:

• Robotics
• Control Theory
• Computational Geometry

Background:

• Continuum robots (CRs) offer superior dexterity and compliance for confined environment interaction compared to rigid robots.
• Existing path planning methods for CRs lack comprehensive consideration of external elastic contact, a critical limitation.
• The non-unique forward kinematics and complex configuration spaces of CRs in contact pose significant path planning challenges.

Purpose of the Study:

• To develop a robust quasi-static path planning framework for continuum robots interacting with elastic obstacles.
• To address the challenges of non-well-defined forward kinematics and complex configuration spaces in CRs under elastic contact.
• To enable efficient and reliable navigation of CRs in environments with deformable objects.

Main Methods:

• Modeled elastic obstacles as external potential fields influencing robot statics.
• Formulated robot statics within potential fields as an optimal control problem to find extremal trajectories.
• Characterized stable robot configurations as a smooth manifold and employed AtlasRRT*, a manifold-specific sampling-based planner.

Main Results:

• Demonstrated that the set of stable robot configurations forms a smooth manifold.
• Showcased that the proposed manifold-based path planning outperforms Euclidean space planners.
• Achieved higher success rates and improved computational efficiency in simulations across various scenarios.

Conclusions:

• The proposed quasi-static path planning on an implicit manifold effectively handles elastic contact for continuum robots.
• AtlasRRT* provides a viable solution for path planning in the complex configuration spaces of CRs interacting with deformable environments.
• This research advances the capabilities of continuum robots for intricate tasks requiring interaction with soft or elastic structures.