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Operation of the Collaborative Composite Manufacturing (CCM) System

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Planning Curvature-Constrained Paths to Multiple Goals Using Circle Sampling.

Edgar Lobaton¹, Jinghe Zhang, Sachin Patil

¹Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA lobaton@cs.unc.edu , jing2009@cs.unc.edu , sachin@cs.unc.edu , ron@cs.unc.edu.

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|February 2, 2012

Summary

This summary is machine-generated.

This study introduces a novel method for nonholonomic robots to plan paths to multiple goals efficiently. The approach reduces tissue damage in medical needle steering by optimizing multi-goal path planning.

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Area of Science:

Robotics
Computational Geometry
Medical Technology

Background:

Nonholonomic robots require specialized path planning for complex tasks.
Visiting multiple goals efficiently is crucial for applications like medical procedures.

Purpose of the Study:

To develop a sampling-based method for optimal, collision-free, curvature-constrained multi-goal path planning.
To apply this method to medical needle steering for improved clinical outcomes.

Main Methods:

Constructing a roadmap by sampling constant curvature circles and generating feasible transitions.
Formulating the multi-goal problem as a minimum directed Steiner tree.
Employing greedy heuristics for efficient path computation.

Main Results:

A novel closed-form formula for connecting circles in 2D, generalized to 3D.
Efficient computation of multi-goal paths using greedy heuristics.
Demonstrated significant reduction in tissue damage for medical needle steering compared to sequential planning.

Conclusions:

The proposed method offers an effective solution for multi-goal path planning in nonholonomic systems.
This approach has direct applications in improving minimally invasive medical procedures.
The planner significantly enhances efficiency and reduces invasiveness in clinical interventions.