Design optimizer for planar soft-growing robot manipulators

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 innovations 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 to engineers - or robot designer enthusiasts - the optimal size of the robot to be built for solving a specific task. The design process is modeled as a multi-objective optimization problem, optimizing 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 single-objective thanks to an efficient mathematical formulation, the novel rank-partitioning algorithm, and obstacle avoidance integrated within the optimizer operators. The proposed method was tested on different tasks to assess its optimality, which showed significant performance in solving the problem: the retrieved designs are short, smooth, and precise at reaching targets. Finally, comparative experiments show that the proposed method works better than the one existing in the literature in terms of precision (14% higher), resource consumption (2% shorter configurations with 4% fewer links), actuation (85% less wavy and undulated configurations), and run time (13% faster).