On Grasping a Tumbling Debris Object with a Free-Flying Robot

The grasping and stabilization of a tumbling, non-cooperative target satellite by means of a free-flying robot is a challenging control problem, which has been addressed in increasing degree of complexity since 20 years. A novel method for computing robot trajectories for grasping a tumbling target is presented. The problem is solved as a motion planning problem with nonlinear optimization. The resulting solution includes a first maneuver of the Servicer satellite which carries the robot arm, taking account of typical satellite control inputs. An analysis of the characteristics of the motion of a grasping point on a tumbling body is used to motivate this grasping method, which is argued to be useful for grasping targets of larger size

Novel Dexterous Robotic Finger Concept with Controlled Stiffness

This paper introduces a novel robotic finger concept for variable impedance grasping in unstructured tasks. The novel robotic finger combines three key features: minimal actuation, variable mechanical compliance and full manipulability. This combination of features allows for a minimal component design, while reducing control complexity and still providing required dexterity and grasping capabilities. The conceptual properties (such as variable compliance) are studied in a port-Hamiltonian framework

Fast Graph-Based Object Segmentation for RGB-D Images

Object segmentation is an important capability for robotic systems, in particular for grasping. We present a graph- based approach for the segmentation of simple objects from RGB-D images. We are interested in segmenting objects with large variety in appearance, from lack of texture to strong textures, for the task of robotic grasping. The algorithm does not rely on image features or machine learning. We propose a modified Canny edge detector for extracting robust edges by using depth information and two simple cost functions for combining color and depth cues. The cost functions are used to build an undirected graph, which is partitioned using the concept of internal and external differences between graph regions. The partitioning is fast with O(NlogN) complexity. We also discuss ways to deal with missing depth information. We test the approach on different publicly available RGB-D object datasets, such as the Rutgers APC RGB-D dataset and the RGB-D Object Dataset, and compare the results with other existing methods

Grasping complexity: analysing risk for sustainable development

Sustainable development relies on our ability to make decisions today that will determine our tomorrow. Given that uncertainty is explicitly allowed to influence our view of what the future holds for us, most ex ante analyses of challenges for sustainable development can be viewed as analysing risk. Many frameworks for analysing risk exist today, but analysing risk for sustainable development entails different requirements. By applying a combination of traditional science and design science, this thesis presents six such requirements, informed by available theory and new empirical studies. The thesis also presents six criteria for scientifically developing frameworks for analysing risk for sustainable development

Grasping Force Prediction for Underactuated Multi-Fingered Hand by Using Artificial Neural Network

In this paper, the feedforward neural network with Levenberg-Marquardt backpropagation training algorithm is used to predict the grasping forces according to the multisensory signals as training samples for specific design of underactuated multifingered hand to avoid the complexity of calculating the inverse kinematics which is appeared through the dynamic modeling of the robotic hand and preparing this network to be used as part of a control system.Keywords: Grasping force, underactuated, prediction, Neural networ

The hydra hand: a mode-switching underactuated gripper with precision and power grasping modes

Human hands are able to grasp a wide range of object sizes, shapes, and weights, achieved via reshaping and altering their apparent grasping stiffness between compliant power and rigid precision. Achieving similar versatility in robotic hands remains a challenge, which has often been addressed by adding extra controllable degrees of freedom, tactile sensors, or specialised extra grasping hardware, at the cost of control complexity and robustness. We introduce a novel reconfigurable four-fingered two-actuator underactuated gripper—the Hydra Hand—that switches between compliant power and rigid precision grasps using a single motor, while generating grasps via a single hydraulic actuator—exhibiting adaptive grasping between finger pairs, enabling the power grasping of two objects simultaneously. The mode switching mechanism and the hand's kinematics are presented and analysed, and performance is tested on two grasping benchmarks: one focused on rigid objects, and the other on items of clothing. The Hydra Hand is shown to excel at grasping large and irregular objects, and small objects with its respective compliant power and rigid precision configurations. The hand's versatility is then showcased by executing the challenging manipulation task of safely grasping and placing a bunch of grapes, and then plucking a single grape from the bunch