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：软体生长型机器人是一种创新装置，其通过模拟植物生长方式在环境中移动。凭借其适应环境的具身智能以及驱动与制造领域的最新创新成果，这类机器人可被应用于特定的操作任务。其应用场景包括探索精密/危险环境、物品操作或家庭环境辅助。本文提出一种面向软体生长型机器人的设计优化新方法，该方法将在制造前用于指导工程师或机器人设计爱好者确定解决特定任务所需的最佳机器人尺寸。我将设计过程建模为多目标优化问题，通过优化软体机械臂的运动链以实现目标定位，同时避免材料和资源的过度消耗。该方法充分利用了群体优化算法（尤其是进化算法）的优势，通过高效的数学表达、新型排名分区算法以及集成于优化算子中的避障机制，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优化性能，结果表明其在问题求解中具有显著效能。最终对比实验证明，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同类方法。