Force exertion, object manipulation, and interaction are novel trending research topics of autonomous flying robots that can yield hoovering. Moreover, specifically with quadrotors, the vibration caused by the high natural frequency of rotating propellers exacerbates the problem of maintaining contact and exerting force against a rigidly fixed object. This contact vibration transfers back kinetic energy to the quadrotor that, in worst-case scenarios, surpasses its flying capabilities, which may lead to a crash. This paper studies the problem of aerial contact stabilization of a quadrotor equipped with a hemispherical deformable tip, which accommodates contact forces at a lower frequency. Thus two phenomena not studied in the literature arise: the rolling motion, and the deformation at contact. The contact force stabilization restores the effects of deformation while simultaneously endowing rolling by controlling a tangent constrained force. A model-free continuous attitude fractional controller to guarantee finite-time attitude stabilization is proposed. The residual coupled nonlinear dynamics yields the desired attitude corresponding to a given contact force; thus, force stabilization is achieved. Finally, experimental results are presented to assess the performance of the proposed approach.

Dexterous object manipulation with robotic hands (subject to holonomic constraints) usually requires the capacity of fingertips to roll onto the object, which is very difficult to achieve with frictionless point contact model . As an alternative, deformable contact model has been considered based on hemispherical shape end-effector to yield rolling. However, it entails dexterity at the expense of dealing with normal and tangential forces, as well as more elaborated dynamic models. In this paper, a passivity-based controller is proposed for redundant robotic fingers with hemi-spherical deformable fingertips to manipulate dexterously a circular dynamic object. Redundancy and rolling are exploited to control normal and tangent contact forces to yield an optimal grasping. The optimal grasping is based on to align dynamically the normal forces applied to the object that allow its manipulation with minimum tangent forces. Stability conditions are presented to guarantee local asymptotic convergence. Finally, numerical simulations show the performance of the forces toward desired pose. This approach can be extended to objects with arbitrary shape that admit a local decomposition by a circular curvature.

The success in real applications of PID regulator has thought us that it is desirable a simple controller with clear and intuitive policy for tuning of feedback gains, as well as minimum or null dependency on the dynamics of the system. In this paper, theoretical and experimental results of PD-like structures for tracking of robots subject to friction and disturbances is presented. The salient feature of our proposal is a neuro-fuzzy schemes used to a single gain that multiplies the regressor-free PID structure. This bounded time-varying gain plays the role of enforcing a dissipative mapping to obtain exponential stability. Stability is obtained in the sense of Lyapunov and experimental study is presented and discussed, which highlights the viability and feasibility for real applications.

Force-position control through one or multiple robots, or fingers, typically assumes a rigid endpoint without rolling nor slipping. However, there are some interesting tasks where rolling is involved, such as turning a knob (object is pivoting at a fixed rotational axis) or rolling a wheel (object rotational axis is moving). In such a case, rigid endpoint force control becomes very difficult if not impossible, even for us humans. This stems from two facts, firstly, infinitesimally small rigid point does not yield a tangent force, therefore it is very difficult to control it indirectly; and secondly, the pair robot-object stands for a highly non-linear constrained underactuated dynamical systems. In this paper, we aim at exploring rolling of a rigid dynamic circular object with hemispherical deformable fingertip, then with area, not point, contact. The dynamic model and a control scheme are presented inspired in previous works, but regulation of normal and tangential forces, as well as position and orientation of the object are synthesized. In particular, tangential force control proves instrumental to regulate posture, and displacement of the object with a simple transpose Jacobian Cartesian PDF+g control. Regulation of rolling angle and displacement with stable normal and tangential forces are obtained without force sensing, neither any model of the deformation nor any dynamic parameter of the object. To entertain these control objectives, a redundant configuration is required so as to yield local regulation, based on the stability-in-the-manifold criteria, whose dimension is greater than the operational space. Illustrative simulations are discussed that provide insight into the closed-loop numerical performance, and finally, remarks on the structure and potential applications are addressed.

Manipulation with soft-fingertips is proposed as a tool for interacting with rigid objects, based on an online bilateral teleoperation fuzzy controller, without time delay. The fuzzy inference engine tunes continuously the force feedback gain to increase awareness of physical interaction so as to grasping is achieved. In contrast to the case of rigid fingertip, wherein infinitely small contact point is assumed, it is argued that our scheme allows safe and intuitive interaction, which has proved effective in an experimental study with 19 subjects. Results indicate success due to feedback of slave contact force to the human user. Subjects consistently judge comfort and easiness of manipulation, with an experimental two-hand prototype.

In this paper, we are interested in a scheme that proves effective use of soft fingertips for human-in-the-loop telemanipulation. Motion planning is carried out by the human-in-the-loop to grasp and manipulate an object using viscoelastic soft fingertips. In addition, to grasp an object firmly, an estimated torque is synthesized based on fuzzy inference. Experimental results show that the user confidence presents small variations using soft fingertips. Then, the soft fingertips in pHRI induce a more user dexterity than rigid fingertips.

A neural network training method for identification in bounded time of nonlinear systems is presented in this paper. A sliding mode surface drives the adalines, perceptrons and multilayer perceptrons so as to a new second order sliding mode is enforced for all time. This neural network-based sliding mode enforces an invariant differential manifold, with a time-varying feedback gain to give rise to finite-time convergence, consequently, the chattering free sliding mode allows identification of the underlying system in finite-time, with zero error. Convergence characteristics of the algorithm are proven with Lyapunov stability theory and concepts drawn from variable structure systems. Numerical simulations for a full nonlinear nonlinear robot arm, subject to noise, show the validity of the proposed approach.

Cooperative robots have attracted the attention because they allow carrying out of tasks which cannot be done with a single robot. Though cooperative robot dynamics are quite complex to handle, regularly it is necessary to implement a nonlinear dynamics-based controller which guarantees fast tracking. A method for very fast constrained object maneuvering for non redundant rigid cooperative robot manipulators is proposed in this paper. The novelty of our approach lies in the fact that very fast decentralized Cartesian cooperative tracking is obtained without using the model of the robot nor exact knowledge of inverse Jacobian. The model-free sliding PD force controller, driven by second order position/force commuting sliding surfaces, is presented, such that approximate compensation of nonlinear dynamics of each robot arises, and the residual error dynamics is finally cancelled by a chattering-free Cartesian sliding mode to guarantee convergence of position and force tracking errors. Notice that inverse kinematics are avoided by synthesized Cartesian, rather than joint, error sliding surfaces, thus the commuting manifold does not depend on the Jacobian, therefore, the system is robust against Jacobian uncertainty, A simulation study of two cooperative robots manipulating a constrained object shows the expected performance.