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1
Intro
2
Medical procedures
3
Why medical robots? (cont.)
4
Autonomous planning for medical robots
5
Motivation & Problem definition
6
Measuring distances between paths
7
Following reference paths-Take (1)
8
Domain-Specific challenges following reference
9
Key challenge-obstacles along the reference path
10
Following Paths in Task Space
11
Following reference paths-Extending the optimization
12
Design or setup optimization
13
Design optimization via global optimization
14
Existing optimization criteria
15
Pop quiz
16
Planning to minimize shearing
17
Evaluation
18
What types of guarantees do planners offer?
19
Planning for steerable needles
20
Site inspecting for CRISP robots
21
Algorithmic approach
22
Graph inspection planning-challenges
23
(Optimal) Graph inspection planning
24
Using approadmate dominance
25
Properties and implementation detais
26
Referenced papers
Description:
Explore algorithmic motion planning for minimally-invasive robotic surgery in this comprehensive lecture from the Robotics Research Colloquium. Delve into the potential of steerable needles and concentric-tube robots to revolutionize common medical procedures, reducing patient recovery time and scarring. Examine the challenges of manual control and the need for automatic planning methods with provable guarantees. Learn about efficient planning capabilities for medical robots, combining techniques from computational geometry, graph theory, and machine learning. Discover how to measure distances between paths, follow reference paths, optimize designs, and minimize shearing in robotic surgeries. Investigate planning for steerable needles and site inspection for CRISP robots, including graph inspection planning challenges and implementation details. Gain insights from Oren Salzman, Assistant Professor at the Technion - Israel Institute of Technology, as he shares his research on addressing computational challenges in robot motion planning. Read more

Algorithmic Motion Planning Meets Min-Invasive Robotic Surgery

Paul G. Allen School
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