Motion Planning Under Uncertainty In Highly Deformable Environments.

Area of Science:

• Robotics
• Control Systems
• Computational Mechanics

Background:

• Manipulators interacting with deformable objects are crucial in surgery, manufacturing, and food handling.
• Existing motion planning methods often assume deterministic deformations or small perturbations, limiting their applicability to complex, large deformations.

Purpose of the Study:

• To develop a unified approach for motion planning under uncertainty in deformable environments.
• To maximize the probability of successful task completion by accounting for uncertainties in deformation models, sensing, and actuation.

Main Methods:

• A sampling-based motion planner integrated with a deformable object simulator is used to generate candidate plans.
• Optimal control and simulation are employed to estimate time-dependent state distributions considering uncertain parameters like material properties and actuation errors.
• The plan with the highest probability of success in avoiding obstacles and reaching the goal is selected.

Main Results:

• The method successfully generates high-quality motion plans for scenarios involving large, time-dependent deformations.
• Demonstrated effectiveness in medical applications, including guiding steerable needles for minimally invasive procedures and manipulating tissues for precision treatment.
• The approach explicitly handles uncertainty in large deformations, outperforming methods that assume deterministic behavior.

Conclusions:

• The proposed unified approach effectively addresses motion planning challenges in uncertain deformable environments.
• This method enhances robotic task success probability by robustly managing various sources of uncertainty.
• The demonstrated medical applications highlight the potential for improved robotic interventions in complex biological tissues.