Design Optimizer for Soft Growing Robot Manipulators in Three-Dimensional Environments

TL;DR

This paper introduces a 3D design optimizer for soft growing robots that enhances their ability to perform precise manipulation tasks in complex environments, aiding engineers in pre-manufacturing design decisions.

Contribution

It extends a planar robot optimizer to three dimensions using a novel Rank Partitioning algorithm within Evolutionary Computation, improving design accuracy and efficiency.

Findings

High precision in reaching targets in 3D tasks

Robust performance across different EC algorithms

Efficient resource usage in optimization process

Abstract

Soft growing robots are novel devices that mimic plant-like growth for navigation in cluttered or dangerous environments. Their ability to adapt to surroundings, combined with advancements in actuation and manufacturing technologies, allows them to perform specialized manipulation tasks. This work presents an approach for design optimization of soft growing robots; specifically, the three-dimensional extension of the optimizer designed for planar manipulators. This tool is intended to be used by engineers and robot enthusiasts before manufacturing their robot: it suggests the optimal size of the robot for solving a specific task. The design process models a multi-objective optimization problem to refine a soft manipulator's kinematic chain. Thanks to the novel Rank Partitioning algorithm integrated into Evolutionary Computation (EC) algorithms, this method achieves high precision in reaching targets and is efficient in resource usage. Results show significantly high performance in solving three-dimensional tasks, whereas comparative experiments indicate that the optimizer features robust output when tested with different EC algorithms, particularly genetic algorithms.