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.

翻译：软体生长机器人是一种创新设备，其受植物生长启发，能够穿越环境。凭借其适应周围环境的具身智能以及驱动和制造领域的最新创新，这类设备可应用于特定操纵任务，包括探索脆弱/危险环境、物品操纵或家庭环境辅助。本文提出了一种用于软体生长机器人设计优化的新方法，该方法将在制造前使用，为工程师或机器人设计爱好者提供解决特定任务所需构建机器人的最佳尺寸建议。我将设计过程建模为多目标优化问题，通过优化软体操纵器的运动学链来达成目标并避免材料与资源的过度消耗。该方法利用基于种群优化算法（特别是进化算法）的优势，通过高效的数学公式、新型秩划分算法以及集成在优化器算子中的避障机制，将多目标问题转化为单目标问题。我在不同任务上测试了所提方法的最优性，结果显示其在求解问题方面具有显著性能。最后，对比实验表明，该方法在精度、资源消耗和运行时间方面均优于现有文献中的方法。