Design Optimizer for Planar Soft-Growing Robot Manipulators

TL;DR

This paper introduces a novel design optimization method for planar soft-growing robot manipulators, enhancing their efficiency and precision in task-specific applications through advanced evolutionary algorithms and obstacle avoidance.

Contribution

It presents a new multi-objective optimization framework with a rank-partitioning algorithm for designing soft-growing robots, outperforming existing methods in accuracy and resource efficiency.

Findings

The method achieves higher precision in reaching targets.

It reduces resource consumption compared to existing approaches.

The optimization process is faster and more effective.

Abstract

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.