This article takes a step to provide humanoid robots with adaptive morphology abilities. We present a systematic approach for enabling robotic covers to morph their shape, with an overall size fitting the anthropometric dimensions of a humanoid robot. More precisely, we present a cover concept consisting of two main components: askeleton, which is a repetition of a basic element callednode, and a softmembrane, which encloses the cover and deforms with its motion. This article focuses on the cover skeleton and addresses the challenging problems of node design, system modeling, motor positioning, and control design of the morphing system. The cover modeling focuses on kinematics, and a systematic approach for defining the system kinematic constraints is presented. Then, we apply genetic algorithms to find the motor locations so that the morphing cover is fully actuated. Finally, we present control algorithms that allow the cover to morph into a time-varying shape. The entire approach is validated by performing kinematic simulations with four different covers of square dimensions and having$3\times 3$,$4\times 8$,$8\times 8$, and$20\times 20$nodes, respectively. For each cover, we apply the genetic algorithms to choose the motor locations and perform simulations for tracking a desired shape. The simulation results show that the presented approach ensures the covers to track a desired shape withgoodtracking performances.

Robots can grasp, even manipulate, objects with different shape, weight and size thanks to the their end-effectors. These are mostly constituted by two fingers, and are known as grippers. However, despite being quite simple for human beings, manipulation is not so straightforward to carry out on robotic systems. One of the main obstacles is the lack of reliable control methods: this is especially true for pneumatic grippers. Such devices are often mounted on industrial robots, though their behavior does not go beyond basic fully-open or fully-closed operations. This happens also as a consequence of the incapability of taking into account frictional effects limiting the pneumatic gripper performance. In this article, a new control strategy is delivered to solve this issue. The proposed strategy allows controlling the grasping force of a pneumatic gripper, without performance degradation due to friction. A pneumatic gripper was built and instrumented with several sensors to experimentally validate the proposed control strategy. The gripper was mechanically connected to a robotic arm and tested with different desired force profiles upon a wide range of force.

Mechanical systems are typically composed of a number of contacting surfaces that move against each other. Such surfaces are subject to friction forces. These dissipate part of the actuation energy and cause an undesired effect on the overall system functioning. Therefore, a suitable model of friction is needed to elide its action. The choice of such a model is not always straightforward, as it is influenced by the system properties and dynamics. In this paper, we show the identification of different friction models and evaluate their prediction capability on an experimental dataset. Despite being state-of-the-art models, some modifications were introduced to improve their performance. A pneumatic gripper was used to collect the data for the models evaluation. Two experimental setups were mounted to execute the experiments: information from two pressure sensors, a load cell and a position sensor was employed for the identification. During the experiments, the gripper was actuated at different constant velocities. Results indicate that all the identified models offer a proper prediction of the real friction force.

Jumping can be an effective way of locomotion to overcome small terrain gaps or obstacles. In this paper we propose two different approaches to perform jumps with a humanoid robot. Specifically, starting from a pre-defined CoM trajectory we develop the theory for a velocity controller and for a torque controller based on an optimization technique for the evaluation of the joints input. The controllers have been tested both in simulation and on the humanoid robot iCub. In simulation the robot was able to jump using both controllers, while the real system jumped with the velocity controller only. The results highlight the importance of controlling the centroidal angular momentum and they suggest that the joint performances, namely maximum power, of the legs and torso joints, and the low level control performances are fundamental to achieve acceptable results.

Robotic arms can perform grasping actions thanks to their “dexteorus” part, i.e. the gripper. Among the various categories, nowadays pneumatic grippers became the most employed in industry, as they have low cost and little bulkiness. Despite their simplicity, controlling the force applied by these grippers is not straightforward due to the dependence of such a force on the air pressure in the gripper chambers. As a result, it is still tricky to implement closed-loop force control for pneumatic grippers. This paper intends to deliver a control scheme relying on the force measurement to control pneumatic grippers. The force might be measured through a commercial sensor (e.g. a load cell) and fed back to close the control loop. This includes a calibration which maps the force-pressure relation taking into account both desired force and length of the gripper fingers. The control scheme exploits two different pressure regulators to precisely adjust the air pressure inside the gripper chambers (i.e. opening and closing chambers). To this aim, a quadratic programming algorithm is employed. The control scheme performance revealed to be good: results will be shown in terms of gripper response to sinusoidal and step inputs, along with the pressure-force characterization.

A common approach to the generation of walking patterns for humanoid robots consists in adopting a layered control architecture. This paper proposes an architecture composed of three nested control loops. The outer loop exploits a robot kinematic model to plan the footstep positions. In the mid layer, a predictive controller generates a Center of Mass trajectory according to the well-known table-cart model. Through a whole-body inverse kinematics algorithm, we can define joint references for position controlled walking. The outcomes of these two loops are then interpreted as inputs of a stack-of-task QP-based torque controller, which represents the inner loop of the presented control architecture. This resulting architecture allows the robot to walk also in torque control, guaranteeing higher level of compliance. Real world experiments have been carried on the humanoid robot iCub.

The torso of a humanoid robot is a fundamental part of its kinematic structure because it defines the reachable workspace, supports the entire upper-body and can be used to control the position of the center of mass. The majority of the torso joints are designed exploiting serial or differential mechanisms, while parallel kinematic structures are less used mainly because of their greater design complexity. This paper describes the design and construction of a 4 degrees of freedom (DoF) torso for our new humanoid robot. Three degrees of freedom, namely roll, pitch and heave, have been implemented using a 3 DoF parallel kinematic structure, while the fourth DoF, namely yaw, has been implemented with a rotational joint on top of the parallel structure. The design has been optimized to reduce the cost and the volume of the system. A first prototype of the torso has been constructed and validated with respect to our design requirements. Eventually, experimental tests have been conducted to assess the functionality of the proposed system.

In recent years the robotics field has witnessed an interesting new trend. Several companies started the production of service robots whose aim is to cooperate with humans. The robots developed so far are either rather expensive or unsuitable for manipulation tasks. This article presents the result of a project which wishes to demonstrate the feasibility of an affordable humanoid robot. R1 is able to navigate, and interact with the environment (grasping and carrying objects, operating switches, opening doors etc). The robot is also equipped with a speaker, microphones and it mounts a display in the head to support interaction using natural channels like speech or (simulated) eye movements. The final cost of the robot is expected to range around that of a family car, possibly, when produced in large quantities, even significantly lower. This goal was tackled along three synergistic directions: use of polymeric materials, light-weight design and implementation of novel actuation solutions. These lines, as well as the robot with its main features, are described hereafter.

In this paper we tackle the problem of estimating the local compliance of tactile arrays exploiting global measurements from a single force and torque sensor. The proposed procedure exploits a transformation matrix (describing the relative position between the local tactile elements and the global force/torque measurements) to define a linear regression problem on the unknown local stiffness. Experiments have been conducted on the foot of the iCub robot, sensorized with a single force/torque sensor and a tactile array of 250 tactile elements (taxels) on the foot sole. Results show that a simple calibration procedure can be employed to estimate the stiffness parameters of virtual springs over a tactile array and to use these model to predict normal forces exerted on the array based only on the tactile feedback. Leveraging on previous works [1] the proposed procedure does not necessarily need a-priori information on the transformation matrix of the taxels which can be directly estimated from available measurements.