The innovative actuation concept presented in this paper allows the implementation of powerful, simple, compact, and light-weight tendon-based driving systems, using as actuators small-size dc motors characterized by high speed and low torque. Due to its properties, this actuation system is very well suited for implementation in highly integrated robotic devices. The basic working principle of this novel actuation system is introduced, and the constitutive equations of the system are given, together with their experimental validation. Driven by the necessity of controlling the actuation force in the robotic hand, the problem of tracking a desired force profile is tackled. With the aim of guaranteeing a high level of robustness against disturbances, a control algorithm based on a second-order sliding manifold has first been evaluated by means of simulations and then validated by experiments. The results obtained with this simple and compact actuation system demonstrate its suitability for use in robotic devices such as robotic hands.

This paper summarizes recent activities carried out for the development of an innovative anthropomorphic robotic hand called the DEXMART Hand. The main goal of this research is to face the problems that affect current robotic hands by introducing suitable design solutions aimed at achieving simplification and cost reduction while possibly enhancing robustness and performance. While certain aspects of the DEXMART Hand development have been presented in previous papers, this paper is the first to give a comprehensive description of the final hand version and its use to replicate human-like grasping. In this paper, particular emphasis is placed on the kinematics of the fingers and of the thumb, the wrist architecture, the dimensioning of the actuation system, and the final implementation of the position, force and tactile sensors. The paper focuses also on how these solutions have been integrated into the mechanical structure of this innovative robotic hand to enable precise force and displacement control of the whole system. Another important aspect is the lack of suitable control tools that severely limits the development of robotic hand applications. To address this issue, a new method for the observation of human hand behavior during interaction with common day-to-day objects by means of a 3D computer vision system is presented in this work together with a strategy for mapping human hand postures to the robotic hand. A simple control strategy based on postural synergies has been used to reduce the complexity of the grasp planning problem. As a preliminary evaluation of the DEXMART Hand’s capabilities, this approach has been adopted in this paper to simplify and speed up the transfer of human actions to the robotic hand, showing its effectiveness in reproducing human-like grasping.

In this paper, we deal with several aspects related to the control of tendon-based actuation systems for robotic devices. In particular, the problems that are considered in this paper are related to the modeling, identification, and control of tendons sliding on curved pathways, subject to friction and viscoelastic effects. Tendons made in polymeric materials are considered, and therefore, hysteresis in the transmission system characteristic must be taken into account as an additional nonlinear effect because of the plasticity and creep phenomena typical of these materials. With the aim of reproducing these behaviors, a viscoelastic model is used to model the tendon compliance. Particular attention has been given to the friction effects arising from the interaction between the tendon pathway and the tendon itself. This phenomenon has been characterized by means of a LuGre-like dynamic friction model to consider the effects that cannot be reproduced by employing a static friction model. A specific setup able to measure the tendon's tension in different points along its path has been designed in order to verify the tension distribution and identify the proper parameters. Finally, a simple control strategy for the compensation of these nonlinear effects and the control of the force that is applied by the tendon to the load is proposed and experimentally verified.

The replication of the human hand's functionality and appearance is one of the main reasons for the development of robot hands. Despite 40 years of research in the field [1], the reproduction of human capabilities, in terms of dexterous manipulation, still seems unachievable by the state-of-the-art technologies. From a design perspective, even defining the optimal functionalities of a robotic end-effector is quite a challenging task since possible applications of these devices span industrial robotics, humanoid robotics, rehabilitation medicines, and prosthetics, to name a few. Therefore, it is reasonable to think that the design solutions, which are well suited to a single domain, might not be readily taken as general guidelines. For example, industrial manipulators are often equipped with basic grippers, which are conceived so as to increase the throughput and the reliability, and are assumed to operate in structured environments. In this case, the enhanced manipulation skills and the subsequent cost increases must be carefully motivated by the application requirements.

Autonomous underwater vehicles (AUVs) are routinely used to survey areas of interest in seas and oceans all over the world. However, those operations requiring intervention capabilities are still reserved to manned submersibles or remotely operated vehicles (ROVs). In the recent years, few research projects have demonstrated the viability of a new type of submersible, the intervention AUV (I-AUV), which can perform underwater missions involving manipulations in a completely autonomous way. The EU FP7 TRIDENT project is one of the most recent examples of such technological concept. This paper describes the different mechatronic components that constitute the I-AUV developed for the TRIDENT project, their hardware and software integration, and the performance of the vehicle during the project trials.