36 research outputs found
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First-Order Dynamic Modeling and Control of Soft Robots
Modeling of soft robots is typically performed at the static level or at a second-order fully dynamic level. Controllers developed upon these models have several advantages and disadvantages. Static controllers, based on the kinematic relations tend to be the easiest to develop, but by sacrificing accuracy, efficiency and the natural dynamics. Controllers developed using second-order dynamic models tend to be computationally expensive, but allow optimal control. Here we propose that the dynamic model of a soft robot can be reduced to first-order dynamical equation owing to their high damping and low inertial properties, as typically observed in nature, with minimal loss in accuracy. This paper investigates the validity of this assumption and the advantages it provides to the modeling and control of soft robots. Our results demonstrate that this model approximation is a powerful tool for developing closed-loop task-space dynamic controllers for soft robots by simplifying the planning and sensory feedback process with minimal effects on the controller accuracy
Tactile perception in hydrogel-based robotic skins using data-driven electrical impedance tomography
Combining functional soft materials with electrical impedance tomography is a promising method for developing continuum sensorized soft robotic skins with high resolutions. However, reconstructing the tactile stimuli from surface electrode measurements is a challenging ill-posed modelling problem, with FEM and analytic models facing a reality gap. To counter this, we propose and demonstrate a model-free superposition method which uses small amounts of real-world data to develop deformation maps of a soft robotic skin made from a self-healing ionically conductive hydrogel, the properties of which are affected by temperature, humidity, and damage. We demonstrate how this method outperforms a traditional neural network for small datasets, obtaining an average resolution of 12.1 mm over a 170 mm circular skin. Additionally, we explore how this resolution varies over a series of 15,000 consecutive presses, during which damages are continuously propagated. Finally, we demonstrate applications for functional robotic skins: damage detection/localization, environmental monitoring, and multi-touch recognition - all using the same sensing material
A bistable soft gripper with mechanically embedded sensing and actuation for fast closed-loop grasping
Soft robotic grippers are shown to be high effective for grasping
unstructured objects with simple sensing and control strategies. However, they
are still limited by their speed, sensing capabilities and actuation mechanism.
Hence, their usage have been restricted in highly dynamic grasping tasks. This
paper presents a soft robotic gripper with tunable bistable properties for
sensor-less dynamic grasping. The bistable mechanism allows us to store
arbitrarily large strain energy in the soft system which is then released upon
contact. The mechanism also provides flexibility on the type of actuation
mechanism as the grasping and sensing phase is completely passive. Theoretical
background behind the mechanism is presented with finite element analysis to
provide insights into design parameters. Finally, we experimentally demonstrate
sensor-less dynamic grasping of an unknown object within 0.02 seconds,
including the time to sense and actuate
Soft Self-Healing Fluidic Tactile Sensors with Damage Detection and Localization Abilities
Self-healing sensors have the potential to increase the lifespan of existing sensing technologies,
especially in soft robotic and wearable applications. Furthermore, they could bestow
additional functionality to the sensing system because of their self-healing ability. This paper presents
the design for a self-healing sensor that can be used for damage detection and localization in a
continuous manner. The soft sensor can recover full functionality almost instantaneously at room
temperature, making the healing process fully autonomous. The working principle of the sensor is
based on the measurement of air pressure inside enclosed chambers, making the fabrication and the
modeling of the sensors easy. We characterize the force sensing abilities of the proposed sensor and
perform damage detection and localization over a one-dimensional and two-dimensional surface
using multilateration techniques. The proposed solution is highly scalable, easy-to-build, cheap and
even applicable for multi-damage detection.This work was supported by the SHERO project, a Future and Emerging Technologies284(FET) programme of the European Commission (grant agreement ID 828818
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Improving Robotic Cooking using Batch Bayesian Optimization
With advances in the field of robotic manipulation,
sensing and machine learning, robotic chefs are expected to
become prevalent in our kitchens and restaurants. Robotic chefs
are envisioned to replicate human skills in order to reduce
the burden of the cooking process. However, the potential
of robots as a means to enhance the dining experience is
unrecognised. This work introduces the concept of food quality
optimization and its challenges with an automated omelette
cooking robotic system. The design and control of the robotic
system that uses general kitchen tools is presented first. Next, we
investigate new optimization strategies for improving subjective
food quality rating, a problem challenging because of the
qualitative nature of the objective and strongly constrained
number of function evaluations possible. Our results show that
through appropriate design of the optimization routine using
Batch Bayesian Optimization, improvements in the subjective
evaluation of food quality can be achieved reliably, with very
few trials and with the ability for bulk optimization. This study
paves the way towards a broader vision of personalized food
for taste-and-nutrition and transferable recipes
Learning-Based Control Strategies for Soft Robots: Theory, Achievements, and Future Challenges
In the last few decades, soft robotics technologies have challenged conventional approaches by introducing new, compliant bodies to the world of rigid robots. These technologies and systems may enable a wide range of applications, including human-robot interaction and dealing with complex environments. Soft bodies can adapt their shape to contact surfaces, distribute stress over a larger area, and increase the contact surface area, thus reducing impact forces
Learning to stop: a unifying principle for legged locomotion in varying environments.
Evolutionary studies have unequivocally proven the transition of living organisms from water to land. Consequently, it can be deduced that locomotion strategies must have evolved from one environment to the other. However, the mechanism by which this transition happened and its implications on bio-mechanical studies and robotics research have not been explored in detail. This paper presents a unifying control strategy for locomotion in varying environments based on the principle of 'learning to stop'. Using a common reinforcement learning framework, deep deterministic policy gradient, we show that our proposed learning strategy facilitates a fast and safe methodology for transferring learned controllers from the facile water environment to the harsh land environment. Our results not only propose a plausible mechanism for safe and quick transition of locomotion strategies from a water to land environment but also provide a novel alternative for safer and faster training of robots
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First-Order Dynamic Modeling and Control of Soft Robots
Modeling of soft robots is typically performed at the static level or at a second-order fully dynamic level. Controllers developed upon these models have several advantages and disadvantages. Static controllers, based on the kinematic relations tend to be the easiest to develop, but by sacrificing accuracy, efficiency and the natural dynamics. Controllers developed using second-order dynamic models tend to be computationally expensive, but allow optimal control. Here we propose that the dynamic model of a soft robot can be reduced to first-order dynamical equation owing to their high damping and low inertial properties, as typically observed in nature, with minimal loss in accuracy. This paper investigates the validity of this assumption and the advantages it provides to the modeling and control of soft robots. Our results demonstrate that this model approximation is a powerful tool for developing closed-loop task-space dynamic controllers for soft robots by simplifying the planning and sensory feedback process with minimal effects on the controller accuracy
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Multi-modal Sensor Fusion for Learning Rich Models for Interacting Soft Robots
Soft robots are typically approximated as low-dimensional systems, especially when learning-based methods are used. This leads to models that are limited in their capability to predict the large number of deformation modes and interactions that a soft robot can have. In this work, we present a deep-learning methodology to learn high-dimensional visual models of a soft robot combining multimodal sensorimotor information. The models are learned in an end-to-end fashion, thereby requiring no intermediate sensor processing or grounding of data. The capabilities and advantages of such a modelling approach are shown on a soft anthropomorphic finger with embedded soft sensors. We also show that how such an approach can be extended to develop higher level cognitive functions like identification of the \textit{self} and the external environment and acquiring object manipulation skills. This work is a step towards the integration of soft robotics and developmental robotics architectures to create the next generation of intelligent soft robots