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We present an offline method to generate smooth, feasible motion for robot arms such that end-effector pose goals of a 6-DoF path are matched within acceptable limits specified by the user. Our approach aims to accurately match the position and orientation goals of the given path, and allows deviation from these goals if there is danger of self-collisions, joint-space discontinuities or kinematic singularities. Our method generates multiple candidate trajectories, and selects the best by incorporating sparse user input that specifies what kinds of deviations are acceptable. We apply our method to a range of challenging paths and show that our method generates solutions that achieve smooth, feasible motions while closely approximating the given pose goals and adhering to user specifications.
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RangedIK: An Optimization-based Robot Motion Generation Method for Ranged-Goal Tasks
Generating feasible robot motions in real-time requires achieving multiple tasks (i.e., kinematic requirements) simultaneously. These tasks can have a specific goal, a range of equally valid goals, or a range of acceptable goals with a preference toward a specific goal. To satisfy multiple and potentially competing tasks simultaneously, it is important to exploit the flexibility afforded by tasks with a range of goals. In this paper, we propose a real-time motion generation method that accommodates all three categories of tasks within a single, unified framework and leverages the flexibility of tasks with a range of goals to accommodate other tasks. Our method incorporates tasks in a weighted-sum multiple-objective optimization structure and uses barrier methods with novel loss functions to encode the valid range of a task. We demonstrate the effectiveness of our method through a simulation experiment that compares it to state-of-the-art alternative approaches, and by demonstrating it on a physical camera-in-hand robot that shows that our method enables the robot to achieve smooth and feasible camera motions.
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- Award ID(s):
- 1830242
- PAR ID:
- 10475939
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-2365-8
- Page Range / eLocation ID:
- 9700 to 9706
- Format(s):
- Medium: X
- Location:
- London, United Kingdom
- Sponsoring Org:
- National Science Foundation
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