Robot-assisted surgery is gaining popularity worldwide and there is increasing scientific interest to explore the potential of soft continuum robots for minimally invasive surgery. However, the remote control of soft robots is much more challenging compared with their rigid counterparts. Accurate modeling of manipulator dynamics is vital to remotely control the diverse movement configurations and is particularly important for safe interaction with the operating environment. However, current dynamic models applied to soft manipulator systems are simplistic and empirical, which restricts the full potential of the new soft robots technology. Therefore, this article provides a new insight into the development of a nonlinear dynamic model for a soft continuum manipulator based on a material model. The continuum manipulator used in this study is treated as a composite material and a modified nonlinear Kelvin-Voigt material model is utilized to embody the visco-hyperelastic dynamics of soft silicone. The Lagrangian approach is applied to derive the equation of motion of the manipulator. Simulation and experimental results prove that this material modeling approach sufficiently captures the nonlinear time- and rate-dependent behavior of a soft manipulator. Material model-based closed-loop trajectory control was implemented to further validate the feasibility of the derived model and increase the performance of the overall system.
Robots primarily made of soft and elastic materials have potential applications such as traveling in confined spaces due to their adaptive morphology. However, their energy efficiency is still subject to improvement. Although a possible approach to increase efficiency is by harvesting the energy used during their behavioral motion, it is not trivial to do so due to their complex dynamics. This work seeks to pioneer a study that exploits the tight coupling between a robot's adaptive morphology, control, and consequent behaviors to harvest energy and increase energy efficiency. It is hypothesized that since varying the robot's morphology may change the energy use that leads to contrasting behavior and efficiency, harvesting the robot's energy will need to be adapted to its morphology. To verify the hypothesis, we developed a shape-changing robot with an elastic structure that achieves locomotion via vibration controlled by a single motor, such that the complex dynamics of the robot can be characterized through its resonance frequencies. It will be shown that harvesting energy at opportune occasions is more important than maximizing the harvest capacity to increase energy efficiency. We will also show how the robot's shape affects energy use in locomotion and how energy harvesting will feedback additional energy that increases the magnitude and affects the robot's behavior. We conclude with an understanding of the role of the robot's morphology, that is, shape, in using the energy provided to the robot and how the understanding can be used to harvest the robot's energy to increase its efficiency.
Data-driven methods with deep neural networks demonstrate promising results for accurate modeling in soft robots. However, deep neural network models rely on voluminous data in discovering the complex and nonlinear representations inherent in soft robots. Consequently, while it is not always possible, a substantial amount of effort is required for data acquisition, labeling, and annotation. This article introduces a data-driven learning framework based on synthetic data to circumvent the exhaustive data collection process. More specifically, we propose a novel time series generative adversarial network with a self-attention mechanism, Transformer TimeGAN (TTGAN) to precisely learn the complex dynamics of a soft robot. On top of that, the TTGAN is incorporated with a conditioning network that enables it to produce synthetic data for specific soft robot behaviors. The proposed framework is verified on a widely used pneumatic-based soft gripper as an exemplary experimental setup. Experimental results demonstrate that the TTGAN generates synthetic time series data with realistic soft robot dynamics. Critically, a combination of the synthetic and only partially available original data produces a data-driven model with estimation accuracy comparable to models obtained from using complete original data.