Surgical Robot, Path Planning, Joint Space, Riemannian Manifolds

TL;DR

This paper introduces a novel path planning method for surgical robots that transforms joint space into a Riemannian manifold, enabling efficient pathfinding and reduced joint movement in complex, non-concave organ surfaces.

Contribution

It proposes a Riemannian manifold-based approach for joint space path planning, improving efficiency and reducing joint movement in minimally invasive robotic surgery.

Findings

Reduces joint angle range compared to position space methods

Efficient pathfinding on non-concave organ surfaces using gradient descent

Demonstrates improved robot movement efficiency in experiments

Abstract

Robotic surgery for minimally invasive surgery can reduce the surgeon's workload by autonomously guiding robotic forceps. Movement of the robot is restricted around a fixed insertion port. The robot often encounters angle limitations during operation. Also, the surface of the abdominal cavity is non-concave, making it computationally expensive to find the desired path.In this work, to solve these problems, we propose a method for path planning in joint space by transforming the position into a Riemannian manifold. An edge cost function is defined to search for a desired path in the joint space and reduce the range of motion of the joints. We found that the organ is mostly non-concave, making it easy to find the optimal path using gradient descent method. Experimental results demonstrated that the proposed method reduces the range of joint angle movement compared to calculations in position space.