Stiffness Control of Deformable Robots Using Finite Element Modeling

International audienceDue to the complexity of modeling deformable materials and infinite degrees of freedom, the rich background of rigid robot control has not been transferred to soft robots. Thus, most model-based control techniques developed for soft robots and soft haptic interfaces are specific to the particular device. In this paper, we develop a general method for stiffness control of soft robots suitable for arbitrary robot geometry and many types of actuation. Extending previous work that uses finite element modeling for position control, we determine the relationship between end-effector and actuator compliance, including the inherent device compliance, and use this to determine the appropriate controlled actuator stiffness for a desired stiffness of the end-effector. Such stiffness control, as the first component of impedance control, can be used to compensate for the natural stiffness of the deformable device and to control the robot's interaction with the environment or a user. We validate the stiffness projection on a deformable robot and include this stiffness projection in a haptic control loop to render a virtual fixture

Control of Elastic Soft Robots based on Real-Time Finite Element Method

International audienceIn this paper, we present a new method for the control of soft robots with elastic behavior, piloted by several actuators. The central contribution of this work is the use of the Finite Element Method (FEM), computed in real-time, in the control algorithm. The FEM based simulation computes the nonlinear deformations of the robots at interactive rates. The model is completed by Lagrange multipliers at the actuation zones and at the end-effector position. A reduced compliance matrix is built in order to deal with the necessary inversion of the model. Then, an iterative algorithm uses this compliance matrix to find the contribution of the actuators (force and/or position) that will deform the structure so that the terminal end of the robot follows a given position. Additional constraints, like rigid or deformable obstacles, or the internal characteristics of the actuators are integrated in the control algorithm. We illustrate our method using simulated examples of both serial and parallel structures and we validate it on a real 3D soft robot made of silicon

Virtual spring damper method for nonholonomic robotic swarm self-organization and leader following

In this paper, we demonstrate a method for self-organization and leader following of nonholonomic robotic swarm based on spring damper mesh. By self-organization of swarm robots we mean the emergence of order in a swarm as the result of interactions among the single robots. In other words the self-organization of swarm robots mimics some natural behavior of social animals like ants among others. The dynamics of two-wheel robot is derived, and a relation between virtual forces and robot control inputs is defined in order to establish stable swarm formation. Two cases of swarm control are analyzed. In the first case the swarm cohesion is achieved by virtual spring damper mesh connecting nearest neighboring robots without designated leader. In the second case we introduce a swarm leader interacting with nearest and second neighbors allowing the swarm to follow the leader. The paper ends with numeric simulation for performance evaluation of the proposed control method

Modeling and grasping of thin deformable objects

Deformable modeling of thin shell-like and other objects have potential application in robot grasping, medical robotics, home robots, and so on. The ability to manipulate electrical and optical cables, rubber toys, plastic bottles, ropes, biological tissues, and organs is an important feature of robot intelligence. However, grasping of deformable objects has remained an underdeveloped research area. When a robot hand applies force to grasp a soft object, deformation will result in the enlarging of the finger contact regions and the rotation of the contact normals, which in turn will result in a changing wrench space. The varying geometry can be determined by either solving a high order differential equation or minimizing potential energy. Efficient and accurate modeling of deformations is crucial for grasp analysis. It helps us predict whether a grasp will be successful from its finger placement and exerted force, and subsequently helps us design a grasping strategy. The first part of this thesis extends the linear and nonlinear shell theories to describe extensional, shearing, and bending strains in terms of geometric invariants including the principal curvatures and vectors, and the related directional and covariant derivatives. To our knowledge, this is the first non-parametric formulation of thin shell strains. A computational procedure for the strain energy is then offered for general parametric shells. In practice, a shell deformation is conveniently represented by a subdivision surface. We compare the results via potential energy minimization over a couple of benchmark problems with their analytical solutions and the results generated by two commercial softwares ABAQUS and ANSYS. Our method achieves a convergence rate an order of magnitude higher. Experimental validation involves regular and freeform shell-like objects (of various materials) grasped by a robot hand, with the results compared against scanned 3-D data (accuracy 0.127mm). Grasped objects often undergo sizable shape changes, for which a much higher modeling accuracy can be achieved using the nonlinear elasticity theory than its linear counterpart. The second part numerically studies two-finger grasping of deformable curve-like objects under frictional contacts. The action is like squeezing. Deformation is modeled by a degenerate version of the thin shell theory. Several differences from rigid body grasping are shown. First, under a squeeze, the friction cone at each finger contact rotates in a direction that depends on the deformable object\u27s global geometry, which implies that modeling is necessary for grasp prediction. Second, the magnitude of the grasping force has to be above certain threshold to achieve equilibrium. Third, the set of feasible finger placements may increase significantly compared to that for a rigid object of the same shape. Finally, the ability to resist disturbance is bounded in the sense that increasing the magnitude of an external force may result in the breaking of the grasp