# Design Optimizer for Planar Soft-Growing Robot Manipulators

arXiv: https://arxiv.org/abs/2310.03374 · HTML: https://mlsanity.com/paper/2310.03374/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 94 references that are in the map.
- First posted on arXiv 5 Oct 2023.

## Builds on (most cited first, 7 of 7)

- [Evolving Modular Soft Robots without Explicit Inter-Module Communication using Local Self-Attention](https://mlsanity.com/paper/2204.06481/) (2022)
- [A Minimal Developmental Model Can Increase Evolvability in Soft Robots](https://mlsanity.com/paper/1706.07296/) (2017)
- [Task-Specific Design Optimization and Fabrication for Inflated-Beam Soft Robots with Growable Discrete Joints](https://mlsanity.com/paper/2103.04942/) (2021)
- [Vine Robots: Design, Teleoperation, and Deployment for Navigation and Exploration](https://mlsanity.com/paper/1903.00069/) (2019)
- [Retraction of Soft Growing Robots without Buckling](https://mlsanity.com/paper/1910.11863/) (2019)
- [Human Interface for Teleoperated Object Manipulation with a Soft Growing Robot](https://mlsanity.com/paper/1910.12998/) (2019)
- [Dynamically Reconfigurable Discrete Distributed Stiffness for Inflated Beam Robots](https://mlsanity.com/paper/2002.04728/) (2020)

## Built on by (most cited first, 2 of 2)

- [Multiple Global Peaks Big Bang-Big Crunch Algorithm for Multimodal Optimization](https://mlsanity.com/paper/2410.18102/) (2024)
- [Design Optimizer for Soft Growing Robot Manipulators in Three-Dimensional Environments](https://mlsanity.com/paper/2501.00368/) (2024)

## Abstract (arXiv)

Soft-growing robots are innovative devices that feature plant-inspired growth to navigate environments. Thanks to their embodied intelligence of adapting to their surroundings and the latest innovation in actuation and manufacturing, it is possible to employ them for specific manipulation tasks. The applications of these devices include exploration of delicate/dangerous environments, manipulation of items, or assistance in domestic environments. This work presents a novel approach for design optimization of soft-growing robots, which will be used prior to manufacturing to suggest engineers -- or robot designer enthusiasts -- the optimal dimension of the robot to be built for solving a specific task. I modeled the design process as a multi-objective optimization problem, in which I optimize the kinematic chain of a soft manipulator to reach targets and avoid unnecessary overuse of material and resources. The method exploits the advantages of population-based optimization algorithms, in particular evolutionary algorithms, to transform the problem from multi-objective into a single-objective thanks to an efficient mathematical formulation, the novel rank-partitioning algorithm, and obstacle avoidance integrated within the optimizer operators. I tested the proposed method on different tasks to access its optimality, which showed significant performance in solving the problem. Finally, comparative experiments showed that the proposed method works better than the one existing in the literature in terms of precision, resource consumption, and run time.