Federico Renda

Dynamic Model of a Multibending Soft Robot Arm Driven by Cables

The new and promising field of soft robotics has many open areas of research such as the development of an exhaustive theoretical and methodological approach to dynamic modeling. To help contribute to this area of research, this paper develops a dynamic model of a continuum soft robot arm driven by cables and based upon a rigorous geometrically exact approach. The model fully investigates both dynamic interaction with a dense medium and the coupled tendon condition. The model was experimentally validated with satisfactory results, using a soft robot arm working prototype inspired by the octopus arm and capable of multibending. Experimental validation was performed for the octopus most characteristic movements: bending, reaching, and fetching. The present model can be used in the design phase as a dynamic simulation platform and to design the control strategy of a continuum robot arm moving in a dense medium.

A 3D steady-state model of a tendon-driven continuum soft manipulator inspired by the octopus arm.

Control and modelling of continuum robots are challenging tasks for robotic researchers. Most works on modelling are limited to piecewise constant curvature. In many cases they neglect to model the actuators or avoid a continuum approach. In particular, in the latter case this leads to a complex model hardly implemented. In this work, a geometrically exact steady-state model of a tendon-driven manipulator inspired by the octopus arm is presented. It takes a continuum approach, fast enough to be implemented in the control law, and includes a model of the actuation system. The model was experimentally validated and the results are reported. In conclusion, the model presented can be used as a tool for mechanical design of continuum tendon-driven manipulators, for planning control strategies or as internal model in an embedded system.

Design and development of a soft robot with crawling and grasping capabilities

This paper describes the design and development of a robot with six soft limbs, with the dual capability of pushing-based locomotion and grasping by wrapping around objects. Specifically, a central platform lodges six silicone limbs, radially distributed, with cables embedded. A new mechanism-specific gait, invariant regarding the number of limbs, has been implemented. Functionally, some limbs provide stability while others push and pull the robot to locomote in the desired direction. Once the robot is close to a target, one limb is elected to wrap around the object and, thanks to the particular limb structure and the soft material, a friction-based grasping is achieved. The robot is inspired by the octopus and implements the key principles of locomotion in this animal, without coping the full body structure. For this reason it works in water, but it is not restricted to this environment. The experiments show the effectiveness of the original solution in locomotion and grasping.

Neural Network and Jacobian Method for Solving the Inverse Statics of a Cable-Driven Soft Arm With Nonconstant Curvature

The solution of the inverse kinematics problem of soft manipulators is essential to generate paths in the task space. The inverse kinematics problem of constant curvature or piecewise constant curvature manipulators has already been solved by using different methods, which include closed-form analytical approaches and iterative methods based on the Jacobian method. On the other hand, the inverse kinematics problem of nonconstant curvature manipulators remains unsolved. This study represents one of the first attempts in this direction. It presents both a model-based method and a supervised learning method to solve the inverse statics of nonconstant curvature soft manipulators. In particular, a Jacobian-based method and a feedforward neural network are chosen and tested experimentally. A comparative analysis has been conducted in terms of accuracy and computational time.

A feed-forward neural network learning the inverse kinetics of a soft cable-driven manipulator moving in three-dimensional space

In this work we address the inverse kinetics problem of a non-constant curvature manipulator driven by three cables. An exact geometrical model of this manipulator has been employed. The differential equations of the mechanical model are non-linear, therefore the analytical solutions are difficult to calculate. Since the exact solutions of the mechanical model are not available, the elements of the Jacobian matrix can not be calculated. To overcome intrinsic problems of the methods based on the Jacobian matrix, we propose for the first time a neural network learning the inverse kinetics of the soft manipulator moving in three-dimensional space. After the training, a feed-forward neural network (FNN) is able to represent the relation between the manipulator tip position and the forces applied to the cables. The results show that a desired tip position can be achieved with a degree of accuracy of 1.36% relative average error with respect to the total arm length.

A two dimensional inverse kinetics model of a cable driven manipulator inspired by the octopus arm

Control of soft robots remains nowadays a big challenge, as it does in the larger category of continuum robots. In this paper a direct and inverse kinetics models are described for a non-constant curvature structure. A major effort has been put recently in modelling and controlling constant curvature structures, such as cylindrical shaped manipulators. Manipulators with non-constant curvature, on the other hand, have been treated with a piecewise constant curvature approximation. In this work a non-constant curvature manipulator with a conical shape is built, taking inspiration from the anatomy of the octopus arm. The choice of a conical shape manipulator made of soft material is justified by its enhanced capability in grasping objects of different sizes. A different approach from the piecewise constant curvature approximation is employed for direct and inverse kinematics model. A continuum geometrically exact approach for direct kinetics model and a Jacobian method for inverse case are proposed. They are validated experimentally with a prototype soft robot arm moving in water. Results show a desired tip position in the task-space can be achieved automatically with a satisfactory degree of accuracy.

A general mechanical model for tendon-driven continuum manipulators

Recently, continuum manipulators have drawn a lot of interest and effort from the robotic community, nevertheless control and modeling of such manipulators are still a challenging task especially because they require a continuum approach. In this paper, a general mechanical model with a geometrically exact approach for tendon-driven continuum manipulators is presented. This model can be applied to a wide range of manipulators thanks to the generality of the parameters which can be set. The approach proposed could as well be a powerful tool for developing the control strategy. The model is also capable of properly simulating the coupled tendon drive, because it takes into account the torsion of the robot arm rather than neglecting it, as it is common practice in other existing models.

Locomotion and elastodynamics model of an underwater shell-like soft robot

This paper reports on the development and validation of the elastodynamics model of an innovative underwater soft-bodied robot inspired by cephalopods. The vehicle, for which the model is devised, is propelled by a discontinuous activation routine which entails the collapse of an elastic shell via cable transmission and its following passive re-inflation under the action of the elastic energy stored in the shell walls. Activation routine and thrust characterization have been determined to depend massively on the capability of the shell to elastically return to its unstrained state, hence an accurate description of the dynamics of the shell during all stages of actuation and at various degrees of deformation is essential. The model, based on a geometrically exact Cosserat theory, is validated against measurement achieved from an ad-hoc experimental apparatus, bringing evidence of its aptness at capturing the key parameters of the system. Eventually the model is employed for simulating a proper propulsion routine in water demonstrating that, upon suitable parametrization of the internal and external hydrodynamics, it can reliably be employed for the realistic quantitative characterization of the cephalopod-inspired robot.

Modelling cephalopod-inspired pulsed-jet locomotion for underwater soft robots

Cephalopods (i.e., octopuses and squids) are being looked upon as a source of inspiration for the development of unmanned underwater vehicles. One kind of cephalopod-inspired soft-bodied vehicle developed by the authors entails a hollow, elastic shell capable of performing a routine of recursive ingestion and expulsion of discrete slugs of fluids which enable the vehicle to propel itself in water. The vehicle performances were found to depend largely on the elastic response of the shell to the actuation cycle, thus motivating the development of a coupled propulsion-elastodynamics model of such vehicles. The model is developed and validated against a set of experimental results performed with the existing cephalopod-inspired prototypes. A metric of the efficiency of the propulsion routine which accounts for the elastic energy contribution during the ingestion/expulsion phases of the actuation is formulated. Demonstration on the use of this model to estimate the efficiency of the propulsion routine for various pulsation frequencies and for different morphologies of the vehicles are provided. This metric of efficiency, employed in association with the present elastodynamics model, provides a useful tool for performing a priori energetic analysis which encompass both the design specifications and the actuation pattern of this new kind of underwater vehicle.

Learning Global Inverse Statics Solution for a Redundant Soft Robot

This paper presents a learning model for obtaining global inverse statics solutions for redundant soft robots. Our motivation begins with the opinion that the inverse statics problem is analogous to the inverse kinematics problem in the case of soft continuum manipulators. A unique inverse statics formulation and data sampling method enables the learning system to circumvent the main roadblocks of the inverting problem. Distinct from previous researches, we have addressed static control of both position and orientation of soft robots. Preliminary tests were conducted on the simulated model of a soft manipulator. The results indicate that learning based approaches could be an effective method for modelling and control of complex soft robots, especially for high dimensional redundant robots.