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Multisection continuum arms offer complementary characteristics to those of traditional rigid-bodied robots. Inspired by biological appendages, such as elephant trunks and octopus arms, these robots trade rigidity for compliance and accuracy for safety and, therefore, exhibit strong potential for applications in human-occupied spaces. Prior work has demonstrated their superiority in operation in congested spaces and manipulation of irregularly shaped objects. However, they are yet to be widely applied outside laboratory spaces. One key reason is that, due to compliance, they are difficult to control. Sophisticated and numerically efficient dynamic models are a necessity to implement dynamic control. In this paper, we propose a novel numerically stable center-of-gravity-based dynamic model for variable-length multisection continuum arms. The model can accommodate continuum robots having any number of sections with varying physical dimensions. The dynamic algorithm is of O(n2) complexity, runs at 9.5 kHz, simulates six to eight times faster than real time for a three-section continuum robot, and, therefore, is ideally suited for real-time control implementations. The model accuracy is validated numerically against an integral-dynamic model proposed by the authors and experimentally for a three-section pneumatically actuated variable-length multisection continuum arm. This is the first sub-real-time dynamic model based on a smooth continuous deformation model for variable-length multisection continuum arms.
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Path Planning for Continuum Arms in Dynamic Environments
Multisection continuum arms are bio-inspired manipulators that combine compliance, payload, dexterity, and safety to serve as co-robots in human-robot collaborative domains. Their hyper redundancy and complex kinematics, however, pose many challenges when performing path planning, especially in dynamic environments. In this paper, we present a W-Space based Rapidly Exploring Random Trees * path planner for multisection continuum arm robots in dynamic environments. The proposed planner improves the existing state-of-art planners in terms of computation time and the success rate, while removing the need for offline computation. On average, the computation time of our approach is below 2 seconds, and its average success rate is around 70 %. The computation time of the proposed planner significantly improves that of the state-of-the-art planner by roughly a factor of 20, making the former suitable for real-time applications. Moreover, for application domains where the obstacle motion is not very predictable (e.g., human obstacles), the proposed planner significantly improves the success rate of state-of-the-art planners by nearly 50 %. Lastly, we demonstrate the feasibility of several generated trajectories by replicating the motion on a physical prototype arm.
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- PAR ID:
- 10538629
- Publisher / Repository:
- IEEE
- Date Published:
- ISSN:
- 2769-4534
- ISBN:
- 979-8-3503-8181-8
- Page Range / eLocation ID:
- 900 to 905
- Subject(s) / Keyword(s):
- Adaptation models, Dynamics, Redundancy, Prototypes, Soft robotics, Real-time systems, Trajectory
- Format(s):
- Medium: X
- Location:
- San Diego, CA, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1109/RoboSoft60065.2024.10521950
