In this work, we deal with the problem of planning a manipulation task for a robotic system composed of at least one dexterous arm and a dexterous multi-fingered hand. The goal of the local planner is to include both, the arm and the hand, in the execution of the task in a coordinated way. This is achieved by using the workspace of the hand which is computed offline. During the online planning, the current in-hand manipulation capability is evaluated taking advantage of the dimensions of the hand workspace and considering the task itself. Dynamic weights enable the computation of the instantaneous contributions of the two subsystems on the motion of the manipulated object. The method is evaluated in simulation as well as in several experiments on the real robot.

In this paper, the design and experimental evaluation of a cable driven robotic gripper for underwater applications is presented. The gripper has three fingers and is characterised by a large workspace if compared with other similar devices reported in literature. Its kinematic configuration allows to execute both parallel and precision grasps on objects with very different dimensions. The gripper has 8 degrees of freedom actuated by only three motors by means of a suitable coupling of the joints obtained through the cable transmission. Moreover, in order to facilitate the execution of complex tasks, special force/torque sensors are mounted on the fingertips. The paper reports the main specifications deriving from the particular tasks in which the gripper is involved, and illustrates the proposed design solutions. Results obtained from real underwater experiments are provided as well, in order to demonstrate the capabilities of the gripper.

In this paper, a novel force/torque sensor is presented. The sensor is based on optoelectronic components and therefore its design is relatively simple and reliable. The sensor design make it suitable for the integration in different robotic systems, such as e.g. the fingers of robotic hands. The basic principle and the design of the sensor are described in this paper, along with a specific prototype implemented for underwater applications. Experimental data are presented and discussed to illustrate the main features of the proposed sensor, and its use as an intrinsic tactile sensor is evaluated.

We present an automated solution for the acquisition, processing and classification of electroencephalography (EEG) signals in order to remotely control a remotely located robotic hand executing communicative gestures. The Brain-Computer Interface (BCI) was implemented using the Steady State Visual Evoked Potential (SSVEP) approach, a low-latency and low-noise method for reading multiple non-time-locked states from EEG signals. As EEG sensor, the low-cost commercial Emotiv EPOC headset was used to acquire signals from the parietal and occipital lobes. The data processing chain is implemented in OpenViBE, a dedicated software platform for designing, testing and applying Brain-Computer Interfaces. Recorded commands were communicated to an external server through a Virtual Reality Peripheral Network (VRPN) interface. During the training phase, the user controlled a local simulation of a dexterous robot hand, allowing for a safe environment in which to train. After training, the user's commands were used to remotely control a real dexterous robot hand located in Bologna (Italy) from Plymouth (UK). We report on the robustness, accuracy and latency of the setup.

This video presents the evolution of the robotic hands, called UB Hands (University of Bologna Hands), developed at the Laboratory of Automation and Robotics of the University of Bologna during more than 25 years of research in this field. Starting from the UB Hand I, the first robot hand prototype developed in our labs, the different design solutions and philosophies that have been followed toward the innovative UB Hand IV, also called DEXMART Hand, recently developed within the DEXMART project are presented. Remarkable characteristics of the UB Hands are also the whole hand manipulation capabilities, the ability of reconstructing the contact forces over the whole hand surface as in the case of the UB Hand II, and the presence of force/tactile sensors as in the UB Hand IV. Moreover, the use of soft covers for the emulation of the human tissue characteristics has been studied, and the adoption of innovative design concepts based on compliant structures has been introduced in the UB Hand III and IV.