Ron Alterovitz

TL;DR: The approach represents beliefs (the distributions of the robot’s state estimate) by Gaussian distributions and is applicable to robot systems with non-linear dynamics and observation models and in simulation for holonomic and non-holonomic robots maneuvering through environments with obstacles with noisy and partial sensing.

TL;DR: This paper forms a new motion planning framework that explicitly considers uncertainty in robot motion to maximize the probability of avoiding collisions and successfully reaching a goal, and demonstrates it by generating non-holonomic plans for steerable needles, a new class of medical needles that follow curved paths through soft tissue and can be modeled as a variant of a Dubins car.

TL;DR: A planning algorithm for insertion of highly flexible bevel-tip needles into soft tissues with obstacles in a 2D imaging plane that compensates for simulated tissue de formations, locally avoids polygonal obstacles, and minimizes needle insertion distance is developed.

TL;DR: A new motion planning algorithm for a variant of a Dubins car with binary left/right steering is developed and applied to steerable needles, a new class of flexible bevel-tip medical needle that physicians can steer through soft tissue to reach clinical targets inaccessible to traditional stiff needles.

TL;DR: A robot-assisted needle-steering system that uses three integrated controllers: a motion planner concerned with guiding the needle around obstacles to a target in a desired plane, a planar controller that maintains the needle in the desired plane and a torsion compensator that controls the needle-tip orientation about the axis of the needle shaft is described.