1Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, Ohio.
2Department of Biology, Case Western Reserve University, Cleveland, Ohio.
Soft Robot. 2019 Aug;6(4):560-577. doi: 10.1089/soro.2018.0080. Epub 2019 May 8.
Inspired by earthworms, soft robots have demonstrated locomotion using segments with coupled length-wise elongation and radial contraction. However, peristaltic turning has primarily been studied empirically. Surface-dependent slip, which results in frictional forces that deform the body segments, makes accurate models challenging and limited to a specific robot and environment. Here, instead of modeling surfaces and segments, we take a geometric approach to analyzing the constraints that result from elimination of slip for the case of peristaltic locomotion. Thus, our abstract two-dimensional model applies to many different mechanical designs (e.g., fluidic actuation, origami, woven mesh). Specifically, we show how turning is limited by segment range of motion, which means that more than one wave will be required to completely reorient the body in an environment where slip is not possible. As a result, to eliminate slip, segments must undergo nonperiodic shape changes. By representing segments as isosceles trapezoids with reasonable ranges of motion, we can determine control waves that in simulation do not require slip. These waves follow from an initial "reach" (i.e., kinematic movement range) of the second segment. A strategy for choosing the second segment reach is proposed based on evaluating long-term turn stability. To demonstrate the value of the approach, we applied the nonperiodic waveform (NPW) to our earthworm-inspired soft robot, Compliant Modular Mesh Worm with Steering (CMMWorm-S). With the NPW, the robot slips less when compared with a naive periodic waveform, where each segment of the robot has the same kinematic reach of each wave, as indicated by the difference between predicted and actual body position over multiple waves. Using an NPW for turning, we observe a decrease in prediction error compared with a naive periodic waveform by 66%. Thus, while our model ignores many factors (inertial dynamics, radial deformation, surface forces), the resulting turn strategies can improve kinematic motion prediction for planning. The theoretical constraints on NPWs that eliminate slip during turning will help robot designers make application-specific design choices about body stiffness, frictional properties, body length, and degrees of freedom.
受蚯蚓启发,软体机器人已经展示了使用具有耦合的长度伸长和径向收缩的节段进行运动的能力。然而,蠕动转弯主要是通过经验研究进行的。表面相关的滑动会导致摩擦力使身体节段变形,这使得准确的模型变得具有挑战性,并且仅限于特定的机器人和环境。在这里,我们不是建模表面和节段,而是采用几何方法来分析蠕动运动时消除滑动所产生的约束。因此,我们的二维抽象模型适用于许多不同的机械设计(例如,流致动、折纸、编织网)。具体来说,我们展示了转弯是如何受到节段运动范围的限制的,这意味着在不可能发生滑动的环境中,需要不止一个波才能完全重新定向身体。因此,为了消除滑动,节段必须经历非周期性的形状变化。通过将节段表示为具有合理运动范围的等腰梯形,我们可以确定在模拟中不需要滑动的控制波。这些波遵循第二个节段的初始“到达”(即运动范围)。基于对长期转弯稳定性的评估,提出了一种选择第二阶段到达的策略。为了证明这种方法的价值,我们将非周期性波形(NPW)应用于我们受蚯蚓启发的软体机器人Compliant Modular Mesh Worm with Steering (CMMWorm-S)。使用 NPW 时,与每个机器人节段在每个波中的运动范围相同的简单周期性波形相比,机器人的滑动更少,这可以通过在多个波中预测的身体位置与实际身体位置之间的差异来表示。使用 NPW 进行转弯时,与简单的周期性波形相比,预测误差减少了 66%。因此,尽管我们的模型忽略了许多因素(惯性动力学、径向变形、表面力),但由此产生的转弯策略可以改善规划中的运动学运动预测。在转弯过程中消除滑动的 NPW 的理论约束将帮助机器人设计人员针对特定应用做出关于身体刚度、摩擦特性、身体长度和自由度的设计选择。