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Worm-like robots have demonstrated great potential in navigating through environments requiring body shape deformation. Some examples include navigating within a network of pipes, crawling through rubble for search and rescue operations, and medical applications such as endoscopy and colonoscopy. In this work, we developed path planning optimization techniques and obstacle avoidance algorithms for the peristaltic method of locomotion of worm-like robots. Based on our previous path generation study using a modified rapidly exploring random tree (RRT), we have further introduced the Bézier curve to allow more path optimization flexibility. Using Bézier curves, the path planner can explore more areas and gain more flexibility to make the path smoother. We have calculated the obstacle avoidance limitations during turning tests for a six-segment robot with the developed path planning algorithm. Based on the results of our robot simulation, we determined a safe turning clearance distance with a six-body diameter between the robot and the obstacles. When the clearance is less than this value, additional methods such as backward locomotion may need to be applied for paths with high obstacle offset. Furthermore, for a worm-like robot, the paths of subsequent segments will be slightly different than the path of the head segment. Here, we show that as the number of segments increases, the differences between the head path and tail path increase, necessitating greater lateral clearance margins.
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This content will become publicly available on June 1, 2026
Immersive Robot Programming Interface for Human-Guided Automation and Randomized Path Planning
Abstract Researchers are exploring augmented reality (AR) interfaces for online robot programming to streamline automation and user interaction in various environments. This study designs, implements, and experimentally validates an AR interface for online programming and data visualization. This new interface integrates human manipulation in the randomized robot path planning, reducing the inherent randomness of the methods with human intervention. The interface uses holographic items that correspond to physical elements to interact with redundant robot manipulators. Utilizing rapidly random tree star (RRT*) and spherical linear interpolation (SLERP) algorithms, the interface achieves end-effector's progression through the collision-free path with smooth rotation. Next, sequential quadratic programming (SQP) achieve robot's configurations for this progression. The platform executes the RRT* algorithm in a loop, with each iteration independently exploring the shortest path through random sampling, leading to variations in the optimized paths produced. These paths are then demonstrated to AR users, who select the most appropriate path based on the environmental context and their intuition. The accuracy and effectiveness of the interface are validated through its implementation and testing with a 7-degrees-of-freedom (DOFs) manipulator, indicating its potential to optimize path planning and to advance current practices in robot programming.
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- Award ID(s):
- 2123346
- PAR ID:
- 10658841
- Publisher / Repository:
- ASME
- Date Published:
- Journal Name:
- ASME Letters in Translational Robotics
- Volume:
- 1
- Issue:
- 2
- ISSN:
- 2997-9765
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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