优化软体生长机器人三维设计，提升任务完成精度与资源效率。
Design Optimizer for Soft Growing Robot Manipulators in Three-Dimensional Environments
- 基于进化计算与新排序分区算法，优化三维软体机械臂结构。
- 在三维任务中实现高精度目标到达，资源使用效率显著提升。
- 适合机器人工程师在制造前快速确定最优尺寸参数。
软体生长机器人是模仿植物生长的新颖装置，适用于复杂或危险环境中的导航。其环境适应能力结合执行器与制造技术的进步，使其能完成特定操作任务。本文提出一种针对三维环境的软体生长机器人设计优化方法，扩展了此前用于平面机械臂的优化器。该工具供工程师和机器人爱好者在制造前使用，可建议完成特定任务所需的最优机器人尺寸。设计过程建模为多目标优化问题，以优化软机械臂的运动学链。通过将新颖的排序分区算法融入进化计算（EC）算法，该方法在目标达程度上表现出高精度，并在资源利用上高效。实验结果表明，在解决三维任务时性能显著；对比实验显示，该优化器在不同EC算法下输出稳定，尤其在遗传算法中表现优异。
原文摘要 · Abstract (English)
Soft growing robots are novel devices that mimic plant-like growth for navigation in cluttered or dangerous environments. Their ability to adapt to surroundings, combined with advancements in actuation and manufacturing technologies, allows them to perform specialized manipulation tasks. This work presents an approach for design optimization of soft growing robots; specifically, the three-dimensional extension of the optimizer designed for planar manipulators. This tool is intended to be used by engineers and robot enthusiasts before manufacturing their robot: it suggests the optimal size of the robot for solving a specific task. The design process models a multi-objective optimization problem to refine a soft manipulator's kinematic chain. Thanks to the novel Rank Partitioning algorithm integrated into Evolutionary Computation (EC) algorithms, this method achieves high precision in reaching targets and is efficient in resource usage. Results show significantly high performance in solving three-dimensional tasks, whereas comparative experiments indicate that the optimizer features robust output when tested with different EC algorithms, particularly genetic algorithms.
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