arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2310.03374cs.ROcs.AI
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平面软体生长机器人操纵器设计优化器

Design Optimizer for Planar Soft-Growing Robot Manipulators

Kadir Has University(卡迪尔哈斯大学)

Fabio Stroppa

AI总结:

本文提出一种基于进化算法和秩划分的软体生长机器人设计优化方法,通过多目标转单目标优化运动学链,实现高精度、低资源消耗的操纵任务。

摘要:

软体生长机器人是一种创新设备,具有受植物启发的生长特性,能够在环境中导航。凭借其适应周围环境的具身智能以及驱动和制造方面的最新创新,它们可以被用于特定的操纵任务。这些设备的应用包括探索精细/危险环境、操纵物品或在家庭环境中提供协助。\n\n本工作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,以向工程师——或机器人设计爱好者——建议为解决特定任务而构建的机器人的最佳尺寸。我将设计过程建模为一个多目标优化问题,在其中优化软体操纵器的运动学链,以到达目标并避免材料和资源的不必要过度使用。该方法利用了基于群体的优化算法(特别是进化算法)的优势,通过高效的数学公式、新颖的秩划分算法以及集成在优化器算子中的避障机制,将问题从多目标转化为单目标。\n\n我在不同任务上测试了所提出的方法以评估其最优性,结果显示其在解决问题方面具有显著性能。最后,对比实验表明,所提出的方法在精度、资源消耗和运行时间方面优于文献中现有的方法。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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