Control of Cooperating Mobile Manipulators

We describe a framework and control algorithms for coordinating multiple mobile robots with manipulators focusing on tasks that require grasping, manipulation and transporting large and possibly flexible objects without special purpose fixtures. Because each robot has an independent controller and is autonomous, the coordination and synergy are realized through sensing and communication. The robots can cooperatively transport objects and march in a tightly controlled formation, while also having the capability to navigate autonomously. We describe the key aspects of the overall hierarchy and the basic algorithms, with specific applications to our experimental testbed consisting of three robots. We describe results from many experiments that demonstrate the ability of the system to carry flexible boards and large boxes as well as the system’s robustness to alignment and odometry errors

Control of Cooperating Mobile Manipulators

We describe a framework and control algorithms for coordinating multiple mobile robots with manipulators focusing on tasks that require grasping, manipulation and transporting large and possibly flexible objects without special purpose fixtures. Because each robot has an independent controller and is autonomous, the coordination and synergy are realized through sensing and communication. The robots can cooperatively transport objects and march in a tightly controlled formation, while also having the capability to navigate autonomously. We describe the key aspects of the overall hierarchy and the basic algorithms, with specific applications to our experimental testbed consisting of three robots. We describe results from many experiments that demonstrate the ability of the system to carry flexible boards and large boxes as well as the system’s robustness to alignment and odometry errors

Bimanual robotic manipulation based on potential fields

openDual manipulation is a natural skill for humans but not so easy to achieve for a robot. The presence of two end effectors implies the need to consider the temporal and spatial constraints they generate while moving together. Consequently, synchronization between the arms is required to perform coordinated actions (e.g., lifting a box) and to avoid self-collision between the manipulators. Moreover, the challenges increase in dynamic environments, where the arms must be able to respond quickly to changes in the position of obstacles or target objects. To meet these demands, approaches like optimization-based motion planners and imitation learning can be employed but they have limitations such as high computational costs, or the need to create a large dataset. Sampling-based motion planners can be a viable solution thanks to their speed and low computational costs but, in their basic implementation, the environment is assumed to be static. An alternative approach relies on improved Artificial Potential Fields (APF). They are intuitive, with low computational, and, most importantly, can be used in dynamic environments. However, they do not have the precision to perform manipulation actions, and dynamic goals are not considered. This thesis proposes a system for bimanual robotic manipulation based on a combination of improved Artificial Potential Fields (APF) and the sampling-based motion planner RRTConnect. The basic idea is to use improved APF to bring the end effectors near their target goal while reacting to changes in the surrounding environment. Only then RRTConnect is triggered to perform the manipulation task. In this way, it is possible to take advantage of the strengths of both methods. To improve this system APF have been extended to consider dynamic goals and a self-collision avoidance system has been developed. The conducted experiments demonstrate that the proposed system adeptly responds to changes in the position of obstacles and target objects. Moreover, the self-collision avoidance system enables faster dual manipulation routines compared to sequential arm movements

A Hand-Eye-Arm Coordinated System

In this paper we present the description and experiments with a tightly coupled Hand-Eye-Arm manipulatory system. We explain the philosophy and the motivation for building a tightly coupled system that actually consists of very autonomous modules that communicate with each other via a central coordinator. We describe each of the modules in the system and their interactions with each other. We highlight the need for sensory driven manipulation, and explain how the above system, where the hand is equipped with multiple tactile sensors, is capable of both manipulating unknown objects, but also detecting and complying in the case of collisions. We explain the partition of the control of the system into various closed loops, representing coordination both at the level of gross manipulator motions as well as fine motions. We describe the various modes that the system can work in, as well as some of the experiments that are being currently performed using this system

Learning to reach and reaching to learn: a unified approach to path planning and reactive control through reinforcement learning

The next generation of intelligent robots will need to be able to plan reaches. Not just ballistic point to point reaches, but reaches around things such as the edge of a table, a nearby human, or any other known object in the robot’s workspace. Planning reaches may seem easy to us humans, because we do it so intuitively, but it has proven to be a challenging problem, which continues to limit the versatility of what robots can do today. In this document, I propose a novel intrinsically motivated RL system that draws on both Path/Motion Planning and Reactive Control. Through Reinforcement Learning, it tightly integrates these two previously disparate approaches to robotics. The RL system is evaluated on a task, which is as yet unsolved by roboticists in practice. That is to put the palm of the iCub humanoid robot on arbitrary target objects in its workspace, start- ing from arbitrary initial configurations. Such motions can be generated by planning, or searching the configuration space, but this typically results in some kind of trajectory, which must then be tracked by a separate controller, and such an approach offers a brit- tle runtime solution because it is inflexible. Purely reactive systems are robust to many problems that render a planned trajectory infeasible, but lacking the capacity to search, they tend to get stuck behind constraints, and therefore do not replace motion planners. The planner/controller proposed here is novel in that it deliberately plans reaches without the need to track trajectories. Instead, reaches are composed of sequences of reactive motion primitives, implemented by my Modular Behavioral Environment (MoBeE), which provides (fictitious) force control with reactive collision avoidance by way of a realtime kinematic/geometric model of the robot and its workspace. Thus, to the best of my knowledge, mine is the first reach planning approach to simultaneously offer the best of both the Path/Motion Planning and Reactive Control approaches. By controlling the real, physical robot directly, and feeling the influence of the con- straints imposed by MoBeE, the proposed system learns a stochastic model of the iCub’s configuration space. Then, the model is exploited as a multiple query path planner to find sensible pre-reach poses, from which to initiate reaching actions. Experiments show that the system can autonomously find practical reaches to target objects in workspace and offers excellent robustness to changes in the workspace configuration as well as noise in the robot’s sensory-motor apparatus

Proceedings of the NASA Conference on Space Telerobotics, volume 2

These proceedings contain papers presented at the NASA Conference on Space Telerobotics held in Pasadena, January 31 to February 2, 1989. The theme of the Conference was man-machine collaboration in space. The Conference provided a forum for researchers and engineers to exchange ideas on the research and development required for application of telerobotics technology to the space systems planned for the 1990s and beyond. The Conference: (1) provided a view of current NASA telerobotic research and development; (2) stimulated technical exchange on man-machine systems, manipulator control, machine sensing, machine intelligence, concurrent computation, and system architectures; and (3) identified important unsolved problems of current interest which can be dealt with by future research

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

The Development of a Multi-arm Mobile Robot System for Nuclear Decommissioning Applications.

This PhD thesis is based in the field of robotics and introduces a case study of the design and development of a multi-arm mobile robot system for nuclear decommissioning (MARS-ND). A key premise underlying the research was to develop intelligence in the robot that is similar to the cooperation and communication between the human brain and its two arms; hence the human body was adopted as the starting point to establish the size and functionality of the proposed system. The approach adopted for this research demonstrates the development, integration and configuration of a multi-arm robot system which consists of two human armlike off-the-shelf manipulators whose joints are controlled using potentiometer sensors and hydraulic actuators. Using the manipulators' sensor feedback, a wide variety of complex tasks found in the rapidly expanding field of nuclear decommissioning can be undertaken. The thesis also considers the issue of collaboration, collision detection and collision avoidance between the two arms of MARS-ND. As part of the final stage of this research the author participated in a collaborative research project with the Sugano Laboratory at Waseda University, Tokyo, Japan. The three major research issues addressed in this thesis are: 1. The selection and integration of off-the-shelf hardware in the development of MARS-ND using the latest technology available for robotic systems 2. The creation of a suitable control system for the robot arms; and the building of an advanced, user-friendly interface between the robot system and the host computer 3. The investigation and implementation of collaboration, coordinated motion control and collision detection & avoidance techniques for the robot arms The hardware and software integration for the whole robotic system is explained with the proposed software architecture and the use of National Instruments (NI) functions and tools to control the movement of the arm joints and the performance of a selected decommissioning task. This thesis also examines the operational software applied within the research through its discussion of four interlinked areas: 1. The control software and hardware interface for the MARS-ND and the controller architecture 2. The application of an NI Compact FieldPoint controller and FieldPoint I/O modules to facilitate wireless communication between the Multi-Arm Mobile Robot system and the user interface in the host PC 3. The use of Measurement and Automation Explorer (MAX) and LabVIEW software tools for calibration and the building of user interfaces required for sending and receiving the signals needed to control the robot arm joints accurately 4. The application of a PID toolkit in LabVIEW for the design of a simple PID controller for the individual arm joints with a potentiometer sensor fitted inside each joint in order to provide a feedback signal to the controller The thesis concludes that MARS-ND is a good example of a robotic system specifically designed for hazardous nuclear decommissioning applications. It demonstrates the complexity of such a system from a number of aspects such as the need for mobility, control, sensor and system design, and integration using modem tools that are available off-the-shelf. In addition the use of these modern tools allows a single mechatronics engineer to design, integrate, interface and build a motion control system for MARS-ND as compared to the traditional way of building a similar robot by a team of specialised engineers. The contribution this research makes to the design and building of multi-arm robot system for nuclear decommissioning industry concerns its size and mobility using a mobile platform to transport the multi-arm robot system. In addition links have been made between Lancaster University and Waseda University in the context of the development of multi-arm robot systems