Surgical Robot, Path Planning, Joint Space, Riemannian Manifolds

2026-03-16

AI Summary

This work addresses the challenges in path planning for minimally invasive surgical robots, which are constrained by fixed entry points and limited joint ranges, compounded by the non-concave surfaces of abdominal organs that render conventional planning methods computationally complex. The study introduces Riemannian manifolds into joint-space path planning for the first time, mapping end-effector poses onto a manifold and designing a geometry-aware edge cost function. By leveraging the non-concavity of organ surfaces, the approach simplifies the optimization landscape and employs gradient descent to efficiently refine feasible trajectories. This method significantly reduces joint motion amplitudes and enhances both computational efficiency and trajectory feasibility, demonstrating clear advantages over traditional position-space planning techniques.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Machine Learning: Learning with Manifolds

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• Surgical Robot
• Path Planning
• Joint Space
• Riemannian Manifolds
• Minimally Invasive Surgery

Innovation

• Riemannian Manifolds
• Joint Space Path Planning
• Surgical Robot
• Edge Cost Function
• Gradient Descent