Design, development and control of a soft robot for object manipulation in Amazon factory-like environment.

In last years robotics took advantage of a strong improvement in hardware design which produced a new technology, called soft robotics, based on new actuators that can modulate their own compliance and have a safe interaction with unstructured environments. Soft robots compete in DARPA Robotic challenge where humanoid robots attempted to execute operations, in a simulated and real scenarios, WALK-man by IIT and University of Pisa, but are also used in industry, ABB company introduced a new soft gripper to handle fragile things. The Soft Robotics certainly has potential but also posed new challenges at planning and control level, researchers propose different approaches to solve problem but, at moment, it represent an open issue. In this thesis we investigated one of most interesting rising approach take advantage of softness which regard the exploitation of environment constrain (EC) like an help on performing grasp and/or manipulation tasks. We designed a \textit{Pick 'n Place} manipulator composed of Variable Stiffness Actuators (VSA) and an end-effector call Pisa/IIT SoftHand, an underactuated anthropomorphic hand with 19 DOF but only one motor. Moreover we provide a three-dimensional depth sensor mounted on top of mechanical structure which gives us geometry of actual scene. In the first part of the thesis, we analyzed how Pisa/IIT SoftHand works in specific situations through empirical experiments, we used Handle device to approach objects in a human-like way and gathered informations in a grasp database, in next step we elaborated manipulation strategies based on previous result and a set of objects with various dimensions. After a study on observed strategies we summarize them in subgroup which depends on objects properties. In last stage, we tested new approached in a constrained environment which is represented by the Amazon shelf and present result. As a case of study we participate at \textit{Amazon Picking Challenge}, in this competition the robot challenged other teams in a \textit{Pick 'n Place} task

Precision Grasp Planning for Integrated Arm-Hand Systems

The demographic shift has caused labor shortages across the world, and it seems inevitable to rely on robots more than ever to fill the widening gap in the workforce. The robotic replacement of human workers necessitates the ability of autonomous grasping as the most natural but rather a vital part of almost all activities. Among different types of grasping, fingertip grasping attracts much attention because of its superior performance for dexterous manipulation. This thesis contributes to autonomous fingertip grasping in four areas including hand-eye calibration, grasp quality evaluation, inverse kinematics (IK) solution of robotic arm-hand systems, and simultaneous achievement of grasp planning and IK solution. To initiate autonomous grasping, object perception is the first needed step. Stereo cameras are well-embraced for obtaining an object\u27s 3D model. However, the data acquired through a camera is expressed in the camera frame while robots only accept the commands encoded in the robot frame. This dilemma necessitates the calibration between the robot (hand) and the camera (eye) with the main goal is of estimating the camera\u27s relative pose to the robot end-effector so that the camera-acquired measurements can be converted into the robot frame. We first study the hand-eye calibration problem and achieve accurate results through a point set matching formulation. With the object\u27s 3D measurements expressed in the robot frame, the next step is finding an appropriate grasp configuration (contact points + contact normals) on the object\u27s surface. To this end, we present an efficient grasp quality evaluation method to calculate a popular wrench-based quality metric which measures the minimum distance between the wrench space origin ($\vec{0}_{6\times 1}$) to the boundary of grasp wrench space (GWS). The proposed method mathematically expresses the exact boundary of GWS, which allows to evaluate the quality of the grasp with the speed that is desirable in most robotic applications. Having obtained a suitable grasp configuration, an accurate IK solution of the arm-hand system is required to perform the planned grasp. Conventionally, the IK of the robotic hand and arm are solved sequentially, which often affects the efficiency and accuracy of the IK solutions. To overcome this problem, we kinematically integrate the robotic arm and hand and propose a human-inspired Thumb-First strategy to narrow down the search space of the IK solution. Based on the Thumb-First strategy, we propose two IK solutions. Our first solution follows a hierarchical IK strategy, while our second solution formulates the arm-hand system as a hybrid parallel-serial system to achieve a higher success rate. Using these results, we propose an approach to integrate the process of grasp planning and IK solution by following a special-designed coarse-to-fine strategy to improve the overall efficiency of our approach