Flexible Object Manipulation

Flexible objects are a challenge to manipulate. Their motions are hard to predict, and the high number of degrees of freedom makes sensing, control, and planning difficult. Additionally, they have more complex friction and contact issues than rigid bodies, and they may stretch and compress. In this thesis, I explore two major types of flexible materials: cloth and string. For rigid bodies, one of the most basic problems in manipulation is the development of immobilizing grasps. The same problem exists for flexible objects. I have shown that a simple polygonal piece of cloth can be fully immobilized by grasping all convex vertices and no more than one third of the concave vertices. I also explored simple manipulation methods that make use of gravity to reduce the number of fingers necessary for grasping. I have built a system for folding a T-shirt using a 4 DOF arm and a fixed-length iron bar which simulates two fingers. The main goal with string manipulation has been to tie knots without the use of any sensing. I have developed single-piece fixtures capable of tying knots in fishing line, solder, and wire, along with a more complex track-based system for autonomously tying a knot in steel wire. I have also developed a series of different fixtures that use compressed air to tie knots in string. Additionally, I have designed four-piece fixtures, which demonstrate a way to fully enclose a knot during the insertion process, while guaranteeing that extraction will always succeed

물체 수송을 위한 협업 로봇의 행동 연구

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. 이범희.This dissertation presents two cooperative object transportation techniques according to the characteristics of objects: passive and active. The passive object is a typical object, which cannot communicate with and detect other robots. The active object, however, has abilities to communicate with robots and can measure the distance from other robots using proximity sensors. Typical areas of research in cooperative object transportation include grasping, pushing, and caging techniques, but these require precise grasping behaviors, iterative motion correction according to the object pose, and the real-time acquisition of the object shape, respectively. For solving these problems, we propose two new object transportation techniques by considering the properties of objects. First, this dissertation presents a multi-agent behavior to cooperatively transport an active object using a sound signal and interactive communication. We first developed a sound localization method, which estimates the sound source from an active object by using three microphone sensors. Next, since the active object cannot be recalled by only a single robot, the robots organized a heterogeneous team by themselves with a pusher, a puller, and a supervisor. This self-organized team succeeded in moving the active object to a goal using the cooperation of its neighboring robots and interactive communication between the object and robots. Second, this dissertation presents a new cooperative passive object transportation technique using cyclic shift motion. The proposed technique does not need to consider the shape or the pose of objects, and equipped tools are also unnecessary for object transportation. Multiple robots create a parallel row formation using a virtual electric dipole field and then push multiple objects into the formation. This parallel row is extended to the goal using cyclic motion by the robots. The above processes are decentralized and activated based on the finite state machine of each robot. Simulations and practical experiments are presented to verify the proposed techniques.Chapter 1 Introduction 1 1.1 Background and Motivation 1 1.2 Related Work 4 1.2.1 The Categories of Object Transportation Techniques 4 1.2.2 Sound Localization Techniques for Active Object Transportation 7 1.3 Contributions 8 1.4 Organization 10 Chapter 2 Object Transportation Problem 11 2.1 Passive Object versus Active Object 11 2.2 Problem Formulation 13 2.3 Assumptions 13 Chapter 3 Active Object Transportation using a Sound Signal and Interactive Communication 15 3.1 Overview of Active Object Transportation 16 3.2 Sound Vector Generation using Triple Microphones 17 3.2.1 Sound Isocontour Generation using ILD 18 3.2.2 Sound Circle Generation using Inverse-square Law 21 3.2.3 Sound Vector Generation 22 3.3 Cooperative Control Method using Interactive Communication 25 3.3.1 Role Assignment of Multi-robot Team 25 3.3.2 Position Assignment of Multi-robot Team 26 3.3.3 Transportation Process of an Active Object 29 Chapter 4 Passive Object Transportation using Cyclic Shift Motion 33 4.1 Overview of Passive Object Transportation 34 4.2 Multi-robot Team Organization 35 4.3 Row Formation Generation using Multiple Robots 37 4.3.1 Cyclic Shift Motion 37 4.3.2 Path Generation using Virtual Electric Dipole Field 39 4.3.3 Path Following using Bang-bang Controller 42 4.4 Multi-object Transportation by a Decentralized Multi-robot Team 45 4.4.1 Information Acquisition Methods for Finite State Machine 45 4.4.2 Finite State Machines (FSMs) 48 4.4.2.1 The FSM of Guider Robots 49 4.4.2.2 The FSM of a Pusher Robot 52 4.4.2.3 The FSM of a Leader Robot 54 4.4.3 Object Transportation Process 55 4.4.4 Formation Constraints for Curved Transportation Path 57 Chapter 5 Simulation Results 61 5.1 Simulation Environment 61 5.2 Simulation Result of Passive Object Transportation 63 5.3 Comparison Results with Other Passive Object Transportation Techniques 69 5.3.1 Simulation Result of Leader-Follower Technique 70 5.3.2 Simulation Result of Caging Technique 72 Chapter 6 Practical Experiments 77 6.1 Experimental Environment 77 6.2 Experimental Results of Active Object Transportation 81 6.2.1 Experimental Result of the SV Estimation 81 6.2.2 Experimental Result of Active Object Transportation 82 6.3 Experimental Results of Passive Object Transportation 86 6.3.1 Small-object Transportation with Straight Path 86 6.3.2 Small-object Transportation with Curved Path 91 6.3.3 Large-object Transportation 93 6.4 Comparison Result with Caging Technique 95 Chapter 7 Discussion 96 Chapter 8 Conclusions 99 Appendix A: The Approaching Phase of Passive Object Transportation 101 A.1 Approaching Phase 101 A.2 Experimental Result of Approaching Phase 107 Appendix B: Object Transportation in a Static Environment 109 B.1 Overview 109 B.2 Object Transportation Problem in a Static Environment 111 B.3 Multi-object Transportation using Hybrid System 112 B.4 New Finite State Machines 113 B.4.1 The States of Guider Robots 114 B.4.2 The States of a Pusher Robot 115 B.4.3 The States of a Leader Robot 116 B.5 Simulation Results 118 B.5.1 Simulation Result: An Obstacle 118 B.5.2 Simulation Result: Two Obstacles 120 B.6 Practical Experiment 122 Bibliography 124Docto

A Bioinspired Bidirectional Stiffening Soft Actuator for Multimodal, Compliant, and Robust Grasping

The stiffness modulation mechanism for soft robotics has gained considerable attention to improve deformability, controllability, and stability. However, for the existing stiffness soft actuator, high lateral stiffness and a wide range of bending stiffness are hard to be provided at the same time. This paper presents a bioinspired bidirectional stiffening soft actuator (BISA) combining the air-tendon hybrid actuation (ATA) and a bone-like structure (BLS). The ATA is the main actuation of the BISA, and the bending stiffness can be modulated with a maximum stiffness of about 0.7 N/mm and a maximum magnification of 3 times when the bending angle is 45 deg. Inspired by the morphological structure of the phalanx, the lateral stiffness can be modulated by changing the pulling force of the BLS. The lateral stiffness can be modulated by changing the pulling force to it. The actuator with BLSs can improve the lateral stiffness about 3.9 times compared to the one without BLSs. The maximum lateral stiffness can reach 0.46 N/mm. And the lateral stiffness can be modulated decoupling about 1.3 times (e.g., from 0.35 N/mm to 0.46 when the bending angle is 45 deg). The test results show the influence of the rigid structures on bending is small with about 1.5 mm maximum position errors of the distal point of actuator bending in different pulling forces. The advantages brought by the proposed method enable a soft four-finger gripper to operate in three modes: normal grasping, inverse grasping, and horizontal lifting. The performance of this gripper is further characterized and versatile grasping on various objects is conducted, proving the robust performance and potential application of the proposed design method

Quantifying grasp quality using an inverse reinforcement learning algorithm

This thesis considers the problem of using a learning algorithm to recognize when a mechanical gripper and sensor combination has achieved a robust grasp. Robotic hands are continuously evolving with finer motor control and higher degrees of freedom which can complicate the ability of an operator to determine if a gripper has achieved a successful grasp. Robots working in hazardous environments especially need confirmation of a successful grasp as the cost of failure is often higher than in traditional factory environments. The object set found in a nuclear environment is the focus of this effort. Objects in this environment are typically expensive (or one-of-a-kind), rigid, radioactive (or toxic), dense, and susceptible to dents, scratches, and oxidation. To validate the robustness of a grasp option, an online inverse reinforcement learning approach is evaluated as a method to quantify grasp quality. This approach is applied to an industrial-grade under-actuated robotic hand equipped with 36 pressure sensors. An expert trains the inverse reinforcement learning algorithm to generate a reward function which scores each grasp so - when combined with fuzzy logic - provides a general success or fail along with a confidence level. Utilizing the trained inverse reinforcement learning algorithm in a glovebox environment reduces the number of potential failing and untrustworthy grasps by scoring executed grasps and rejecting grasps that are similar to prior failed grasps while allowing further execution of movement when a grasp has been scored highly. The trained algorithm incorrectly classified grasps of insufficient quality less than 5% of the time in experimental hardware tests, showing that the algorithm can be applied to the glovebox environment to improve grasp safety. Thus the combination of grasp selection and pressure sensor validation provides a more efficient, robust, and redundant method to assure items can be safely handled during remote automation processes.Mechanical Engineerin

Optimal grasping of soft objects with two robotic fingers

Robot grasping of deformable objects is an under-researched area. The difficulty comes from both mechanics and computation. First, deformation caused by the grasp operations changes object\u27s global geometry. Second, under deformation, an object\u27s contacts with the fingers grow from points into areas. Inside such a contact area, points that stick to the finger may later slide while points that slide may later stick. The torques exerted by the grasping fingers vary, in contrast with rigid body grasping whose torques are invariant under forces. In this thesis the object\u27s deformation and configuration of contact with fingers and the plane are tracked with finite element method(FEM) in an event-driven manner based on the contact displacements induced by the finger movements. The first part of the thesis analyzes two-finger squeeze grasping of deformable objects with a focus on two special classes: stable squeezes, which minimize the potential energy of the object among squeezes of the same depth, and pure squeezes, which eliminate all euclidean motions from the resulting deformations. Based on them an algorithm to characterize the best resistance by a grasp to an adversary finger is proposed which minimizes the work done by the grasping fingers. An optimization scheme is offered to handle the general case of frictional segment contact. Simulations and multiple experiments with a Barrett Hand on a rubber foam object are presented. The second part of this thesis describes a strategy for a two-finger robot hand to grasp and lift a 3D deformable object resting on the plane. Inspired by the human hand grasping, the strategy employs two rounded fingers to squeeze the object until a secure grasp is achieved under contact friction. And then lift it by translating upward to pick up the object. During the squeeze, a lift test is repeatedly conducted until it is successful based on the metrics and then trigger the upward translation. The gravitational force acting on the object is accounted for. Simulation is presented and shows some good promise for the sensorless grasping approach for deformable objects

Interlocking structure design and assembly

Many objects in our life are not manufactured as whole rigid pieces. Instead, smaller components are made to be later assembled into larger structures. Chairs are assembled from wooden pieces, cabins are made of logs, and buildings are constructed from bricks. These components are commonly designed by many iterations of human thinking. In this report, we will look at a few problems related to interlocking components design and assembly. Given an atomic object, how can we design a package that holds the object firmly without a gap in-between? How many pieces should the package be partitioned into? How can we assemble/extract each piece? We will attack this problem by first looking at the lower bound on the number of pieces, then at the upper bound. Afterwards, we will propose a practical algorithm for designing these packages. We also explore a special kind of interlocking structure which has only one or a small number of movable pieces. For example, a burr puzzle. We will design a few blocks with joints whose combination can be assembled into almost any voxelized 3D model. Our blocks require very simple motions to be assembled, enabling robotic assembly. As proof of concept, we also develop a robot system to assemble the blocks. In some extreme conditions where construction components are small, controlling each component individually is impossible. We will discuss an option using global controls. These global controls can be from gravity or magnetic fields. We show that in some special cases where the small units form a rectangular matrix, rearrangement can be done in a small space following a technique similar to bubble sort algorithm

Capture and generalisation of close interaction with objects

Robust manipulation capture and retargeting has been a longstanding goal in both the fields of animation and robotics. In this thesis I describe a new approach to capture both the geometry and motion of interactions with objects, dealing with the problems of occlusion by the use of magnetic systems, and performing the reconstruction of the geometry by an RGB-D sensor alongside visual markers. This ‘interaction capture’ allows the scene to be described in terms of the spatial relationships between the character and the object using novel topological representations such as the Electric Parameters, which parametrise the outer space of an object using properties of the surface of the object. I describe the properties of these representations for motion generalisation and discuss how they can be applied to the problems of human-like motion generation and programming by demonstration. These generalised interactions are shown to be valid by demonstration of retargeting grasping and manipulation to robots with dissimilar kinematics and morphology using only local, gradient-based planning