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

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

A learning approach to the FOM problem

Hogan recently provided an heuristic technique called family of modes (FOM) to solve model predictive control (MPC) problems under hybrid constraints and underactuation. The goal of this study is to further develop this new method and to expand its usage in the robotics manipulation community. With that objective in mind, we address some of the method's weaknesses, we provide comparison tools to try to compare the method with traditional MPC solving techniques and we provide a simple and systematic technique to set-up the method's parameters. We conclude the study by presenting our the future lines of research, which consist in generalizing the method for more complex systems and testing it's robustness.Outgoin

A Reactive Planning Framework for Dexterous Robotic Manipulation

This thesis investigates a reactive motion planning and controller framework that enables robots to manipulate objects dexterously. We develop a robotic platform that can quickly and reliably replan actions based on sensed information. Robotic manipulation is subject to noise due to uncertainty in frictional contact information, and reactivity is key for robustness. The planning framework has been designed with generality in mind and naturally extends to a variety of robotic tasks, manipulators and sensors. This design is validated experimentally on an ABB IRB 14000 dual-arm industrial collaborative robot. In this research, we are interested in dexterous robot manipulation, where the key technology is to move an object from an initial location to a desired configuration. The robot makes use of a high resolution tactile sensor to monitor the progress of the task and drive the reactive behavior of the robot to counter mistakes or unaccounted environment conditions. The motion planning framework is integrated with a task planner that dictates the high-level manipulation behavior of the robot, as well as a low-level controller, that adapts robot motions based on measured tactile signaOutgoin

Motion Primitives and Planning for Robots with Closed Chain Systems and Changing Topologies

When operating in human environments, a robot should use predictable motions that allow humans to trust and anticipate its behavior. Heuristic search-based planning offers predictable motions and guarantees on completeness and sub-optimality of solutions. While search-based planning on motion primitive-based (lattice-based) graphs has been used extensively in navigation, application to high-dimensional state-spaces has, until recently, been thought impractical. This dissertation presents methods we have developed for applying these graphs to mobile manipulation, specifically for systems which contain closed chains. The formation of closed chains in tasks that involve contacts with the environment may reduce the number of available degrees-of-freedom but adds complexity in terms of constraints in the high-dimensional state-space. We exploit the dimensionality reduction inherent in closed kinematic chains to get efficient search-based planning. Our planner handles changing topologies (switching between open and closed-chains) in a single plan, including what transitions to include and when to include them. Thus, we can leverage existing results for search-based planning for open chains, combining open and closed chain manipulation planning into one framework. Proofs regarding the framework are introduced for the application to graph-search and its theoretical guarantees of optimality. The dimensionality-reduction is done in a manner that enables finding optimal solutions to low-dimensional problems which map to correspondingly optimal full-dimensional solutions. We apply this framework to planning for opening and navigating through non-spring and spring-loaded doors using a Willow Garage PR2. The framework motivates our approaches to the Atlas humanoid robot from Boston Dynamics for both stationary manipulation and quasi-static walking, as a closed chain is formed when both feet are on the ground

Coordination of Multirobot Teams and Groups in Constrained Environments: Models, Abstractions, and Control Policies

Robots can augment and even replace humans in dangerous environments, such as search and rescue and reconnaissance missions, yet robots used in these situations are largely tele-operated. In most cases, the robots\u27 performance depends on the operator\u27s ability to control and coordinate the robots, resulting in increased response time and poor situational awareness, and hindering multirobot cooperation. Many factors impede extended autonomy in these situations, including the unique nature of individual tasks, the number of robots needed, the complexity of coordinating heterogeneous robot teams, and the need to operate safely. These factors can be partly addressed by having many inexpensive robots and by control policies that provide guarantees on convergence and safety. In this thesis, we address the problem of synthesizing control policies for navigating teams of robots in constrained environments while providing guarantees on convergence and safety. The approach is as follows. We first model the configuration space of the group (a space in which the robots cannot violate the constraints) as a set of polytopes. For a group with a common goal configuration, we reduce complexity by constructing a configuration space for an abstracted group state. We then construct a discrete representation of the configuration space, on which we search for a path to the goal. Based on this path, we synthesize feedback controllers, decentralized affine controllers for kinematic systems and nonlinear feedback controllers for dynamical systems, on the polytopes, sequentially composing controllers to drive the system to the goal. We demonstrate the use of this method in urban environments and on groups of dynamical systems such as quadrotors. We reduce the complexity of multirobot coordination by using an informed graph search to simultaneously build the configuration space and find a path in its discrete representation to the goal. Furthermore, by using an abstraction on groups of robots we dissociate complexity from the number of robots in the group. Although the controllers are designed for navigation in known environments, they are indeed more versatile, as we demonstrate in a concluding simulation of six robots in a partially unknown environment with evolving communication links, object manipulation, and stigmergic interactions

Manipulating Objects using Compliant, Unactuated Tails: Modeling and Planning

Ropes and rope-like objects (e.g., chains, cords, lines, whips, or lassos) are comparatively cheap, simple, and useful in daily life. For a long time, humans have used such structures for manipulation tasks in a qualitatively different ways such as pulling, fastening, attaching, tying, knotting, and whipping. Nevertheless, these structures have received little attention in robotics. Because they are unactuated, such structures are regarded as difficult to model, plan and control. In this dissertation, we are interested in a mobile robot system using a flexible rope-like structure attached to its end akin to a ‘tail’. Our goal is to investigate how mobile robots can use compliant, unactuated structures for various manipulation tasks. Robots that use a tail to manipulate objects face challenges in modeling and planning of behaviors, dynamics, and combinatorial optimization. In this dissertation, we propose several methods to deal with the difficulties of modeling and planning. In addition, we solve variants of object manipulation problems wherein multiple classes of objects are to be transported by multiple cooperative robots using ropes. Firstly, we examine motion primitives, where the primitives are designed to simplify modeling and planning issues. We explore several sets of motion primitive where each primitive contributes some aspect lacking in the others. These primitives are forward models of the system’s behavior that predict the position and orientation of the object being manipulated within the workspace. Then, to solve manipulation problems, we design a planner that seeks a sequence of motion primitives by using a sampling-based motion planning approach coupled with a particle-based representation to treat error propagation of the motions. Our proposed planner is used to optimize motion sequences based on a specified preference over a set of objectives, such as execution time, navigation cost, or collision likelihood. The solutions deal with different preferences effectively, and we analyze the complementary nature of dynamic and quasi-static motions, showing that there exist regimes where transitions among them are indeed desirable, as reflected in the plans produced. Secondly, we explore a variety of interesting primitives that result in new approaches for object manipulation problems. We examine ways two robots can join the ends of their tails so that a pair of conjoined robots can encircle objects leading to the advantage of greater towing capacity if they work cooperatively. However, individual robots possess the advantage of allowing for greater concurrency if objects are distant from one another. We solve a new manipulation problem for the scenarios of moving a collection of objects to goal locations with multiple robots that may form conjoined pairs. To maximize efficiency, the robots balance working as a tightly-knit sub-team with individual operation. We develop heuristics that give satisfactory solutions in reasonable time. The results we report include data from physical robots executing plans produced by our planner, collecting objects both by individual action and by a coupled pair operation. We expect that our research results will help to understand how a flexible compliant appendage when added to a robot can be useful for more than just agility. The proposed techniques using simple motion models for characterizing the complicated system dynamics can be used to robotics research: motion planning, minimalist manipulators, behavior-based control, and multi-robot coordination. In addition, we expect that the proposed methods can enhance the performance of various manipulation tasks, efficient search, adaptive sampling or coverage in unknown, unstructured environments