We present a framework for deformable object manipulation that interleaves planning and control, enabling complex manipulation tasks without relying on high-fidelity modeling or simulation. The key question we address is when should we use planning and when should we use control to achieve the task? Planners are designed to find paths through complex configuration spaces, but for highly underactuated systems, such as deformable objects, achieving a specific configuration is very difficult even with high-fidelity models. Conversely, controllers can be designed to achieve specific configurations, but they can be trapped in undesirable local minima owing to obstacles. Our approach consists of three components: (1) a global motion planner to generate gross motion of the deformable object; (2) a local controller for refinement of the configuration of the deformable object; and (3) a novel deadlock prediction algorithm to determine when to use planning versus control. By separating planning from control we are able to use different representations of the deformable object, reducing overall complexity and enabling efficient computation of motion. We provide a detailed proof of probabilistic completeness for our planner, which is valid despite the fact that our system is underactuated and we do not have a steering function. We thenmore »
Near-optimal Smooth Path Planning for Multisection Continuum Arms
We study the path planning problem for continuum-arm robots, in which we are given a starting and an end point, and we need to compute a path for the tip of the continuum arm between the two points. We consider both cases where obstacles are present and where they are not. We demonstrate how to leverage the continuum arm features to introduce a new model that enables a path planning approach based on the configurations graph, for a continuum arm consisting of three sections, each consisting of three muscle actuators. The algorithm we apply to the configurations graph allows us to exploit parallelism in the computation to obtain efficient implementation. We conducted extensive tests, and the obtained results show the completeness of the proposed algorithm under the considered discretizations, in both cases where obstacles are present and where they are not. We compared our approach to the standard inverse kinematics approach. While the inverse kinematics approach is much faster when successful, our algorithm always succeeds in finding a path or reporting that no path exists, compared to a roughly 70% success rate of the inverse kinematics approach (when a path exists).
- Award ID(s):
- Publication Date:
- NSF-PAR ID:
- Journal Name:
- 2019 2nd IEEE International Conference on Soft Robotics (RoboSoft)
- Page Range or eLocation-ID:
- 416 to 421
- Sponsoring Org:
- National Science Foundation
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