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Asymptotically Optimal Kinematic Design of Robots using Motion Planning
Cenk Baykal1, Chris Bowen2, Ron Alterovitz3
1Massachusetts Institute of Technology, Cambridge, MA, USA, baykal@mit.edu.
Autonomous Robots
|April 23, 2019
Summary
This study presents a new design optimization method for robots, enabling them to reach more targets in confined spaces like the human body. The approach improves robot maneuverability for complex medical procedures.
Area of Science:
- Robotics
- Medical Technology
- Computational Geometry
Background:
- Robots in confined environments, such as medical robots in narrow body cavities, face challenges with generic kinematic designs.
- Reaching all targets while avoiding obstacles is difficult for current robotic systems in minimally invasive surgery.
Purpose of the Study:
- To develop a design optimization method for computing kinematic parameters of robots.
- To enable a single robot to maximize reachability of goal regions while ensuring obstacle avoidance.
Main Methods:
- Integration of sampling-based motion planning in configuration space with stochastic optimization in design space.
- Asymptotic optimality is proven for the proposed method.
- Demonstration in simulation for serial manipulators and concentric tube robots.
Main Results:
- The method enhances the accuracy of evaluating a design's reachability over time.
- Selected robot designs approach global optimality in performance.
- Successful simulation results for both serial and concentric tube robots.
Conclusions:
- The introduced method effectively optimizes robot kinematic designs for constrained environments.
- This approach can improve the capabilities of medical robots for minimally invasive surgery.
- The method offers a pathway to more versatile and effective robotic surgical tools.
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