Multi-Robot Task Allocation for Inspection Problems with Cooperative Tasks Using Hybrid Genetic Algorithms

In dieser Dissertation werden Methoden zur optimalen Aufgabenverteilung in Multirobotersystemen (engl. Multi-Robot Task Allocation – MRTA) zur Inspektion von Industrieanlagen untersucht. MRTA umfasst die Verteilung und Ablaufplanung von Aufgaben für eine Gruppe von Robotern unter Berücksichtigung von operativen Randbedingungen mit dem Ziel, die Gesamteinsatzkosten zu minimieren. Dank zunehmendem technischen Fortschritt und sinkenden Technologiekosten ist das Interesse an mobilen Robotern für den Industrieeinsatz in den letzten Jahren stark gestiegen. Viele Arbeiten konzentrieren sich auf Probleme der Mobilität wie Selbstlokalisierung und Kartierung, aber nur wenige Arbeiten untersuchen die optimale Aufgabenverteilung. Da sich mit einer guten Aufgabenverteilung eine effizientere Planung erreichen lässt (z. B. niedrigere Kosten, kürzere Ausführungszeit), ist das Ziel dieser Arbeit die Entwicklung von Lösungsmethoden für das aus Inspektionsaufgaben mit Einzel- und Zweiroboteraufgaben folgende Such-/Optimierungsproblem. Ein neuartiger hybrider Genetischer Algorithmus wird vorgestellt, der einen teilbevölkerungbasierten Genetischen Algorithmus zur globalen Optimierung mit lokalen Suchheuristiken kombiniert. Zur Beschleunigung dieses Algorithmus werden auf die fittesten Individuen einer Generation lokale Suchoperatoren angewendet. Der vorgestellte Algorithmus verteilt die Aufgaben nicht nur einfach und legt den Ablauf fest, sondern er bildet auch temporäre Roboterverbünde für Zweiroboteraufgaben, wodurch räumliche und zeitliche Randbedingungen entstehen. Vier alternative Kodierungsstrategien werden für den vorgestellten Algorithmus entworfen: Teilaufgabenbasierte Kodierung: Hierdurch werden alle möglichen Lösungen abgedeckt, allerdings ist der Suchraum sehr groß. Aufgabenbasierte Kodierung: Zwei Möglichkeiten zur Zuweisung von Zweiroboteraufgaben wurden implementiert, um die Effizienz des Algorithmus zu steigern. Gruppierungsbasierte Kodierung: Zeitliche Randbedingungen zur Gruppierung von Aufgaben werden vorgestellt, um gute Lösungen innerhalb einer kleinen Anzahl von Generationen zu erhalten. Zwei Umsetzungsvarianten werden vorgestellt. Dekompositionsbasierte Kodierung: Drei geometrische Zerlegungen wurden entworfen, die Informationen über die räumliche Anordnung ausnutzen, um Probleme zu lösen, die Inspektionsgebiete mit rechteckigen Geometrien aufweisen. In Simulationsstudien wird die Leistungsfähigkeit der verschiedenen hybriden Genetischen Algorithmen untersucht. Dazu wurde die Inspektion von Tanklagern einer Erdölraffinerie mit einer Gruppe homogener Inspektionsroboter als Anwendungsfall gewählt. Die Simulationen zeigen, dass Kodierungsstrategien, die auf der geometrischen Zerlegung basieren, bei einer kleinen Anzahl an Generationen eine bessere Lösung finden können als die anderen untersuchten Strategien. Diese Arbeit beschäftigt sich mit Einzel- und Zweiroboteraufgaben, die entweder von einem einzelnen mobilen Roboter erledigt werden können oder die Zusammenarbeit von zwei Robotern erfordern. Eine Erweiterung des entwickelten Algorithmus zur Behandlung von Aufgaben, die mehr als zwei Roboter erfordern, ist möglich, würde aber die Komplexität der Optimierungsaufgabe deutlich vergrößern.

Enhancing the Dexterity of a Robot Hand Using Controlled Slip

Humans can effortlessly manipulate objects in their hands, dexterously sliding and twisting them within their grasp. Robots, however, have none of these capabilities, they simply grasp objects rigidly in their end effectors. To investigate this common form of human manipulation, an analysis of controlled slipping of a grasped object within a robot hand was performed. The Salisbury robot hand demonstrated many of these controlled slipping techniques, illustrating many results of this analysis. First, the possible slipping motions were found as a function of the location, orientation, and types of contact between the hand and object. Second, for a given grasp, the contact types were determined as a function of the grasping force and the external forces on the object. Finally, by changing the grasping force, the robot modified the constraints on the object and affect controlled slipping slipping motions.

Analysis and Control of Robot Manipulators with Kinematic Redundancy

A closed-form solution formula for the kinematic control of manipulators with redundancy is derived, using the Lagrangian multiplier method. Differential relationship equivalent to the Resolved Motion Method has been also derived. The proposed method is proved to provide with the exact equilibrium state for the Resolved Motion Method. This exactness in the proposed method fixes the repeatability problem in the Resolved Motion Method, and establishes a fixed transformation from workspace to the joint space. Also the method, owing to the exactness, is demonstrated to give more accurate trajectories than the Resolved Motion Method. In addition, a new performance measure for redundancy control has been developed. This measure, if used with kinematic control methods, helps achieve dexterous movements including singularity avoidance. Compared to other measures such as the manipulability measure and the condition number, this measure tends to give superior performances in terms of preserving the repeatability property and providing with smoother joint velocity trajectories. Using the fixed transformation property, Taylor's Bounded Deviation Paths Algorithm has been extended to the redundant manipulators.

Modeling Robot Dynamic Performance for Endpoint Force Control

This research aims to understand the fundamental dynamic behavior of servo-controlled machinery in response to various types of sensory feedback. As an example of such a system, we study robot force control, a scheme which promises to greatly expand the capabilities of industrial robots by allowing manipulators to interact with uncertain and dynamic tasks. Dynamic models are developed which allow the effects of actuator dynamics, structural flexibility, and workpiece interaction to be explored in the frequency and time domains. The models are used first to explain the causes of robot force control instability, and then to find methods of improving this performance.

Task-Level Robot Learning

We are investigating how to program robots so that they learn from experience. Our goal is to develop principled methods of learning that can improve a robot's performance of a wide range of dynamic tasks. We have developed task-level learning that successfully improves a robot's performance of two complex tasks, ball-throwing and juggling. With task- level learning, a robot practices a task, monitors its own performance, and uses that experience to adjust its task-level commands. This learning method serves to complement other approaches, such as model calibration, for improving robot performance.

Design and Implementation of a Flexible Robot

This robot has low natural frequencies of vibration. Insights into the problems of designing joint and link flexibility are discussed. The robot has three flexible rotary actuators and two flexible, interchangeable links, and is controlled by three independent processors on a VMEbus. Results from experiments on the control of residual vibration for different types of robot motion are presented. Impulse prefiltering and slowly accelerating moves are compared and shown to be effective at reducing residual vibration.

On Probabilistic Strategies for Robot Tasks

Robots must act purposefully and successfully in an uncertain world. Sensory information is inaccurate or noisy, actions may have a range of effects, and the robot's environment is only partially and imprecisely modeled. This thesis introduces active randomization by a robot, both in selecting actions to execute and in focusing on sensory information to interpret, as a basic tool for overcoming uncertainty. An example of randomization is given by the strategy of shaking a bin containing a part in order to orient the part in a desired stable state with some high probability. Another example consists of first using reliable sensory information to bring two parts close together, then relying on short random motions to actually mate the two parts, once the part motions lie below the available sensing resolution. Further examples include tapping parts that are tightly wedged, twirling gears before trying to mesh them, and vibrating parts to facilitate a mating operation.

Passive and Active Grasping with a Prehensile Robot End-Effector

This report presents a design of a new type of robot end-effector with inherent mechanical grasping capabilities. Concentrating on designing an end-effector to grasp a simple class of objects, cylindrical, allowed a design with only one degree of actuation. The key features of this design are high bandwidth response to forces, passive grasping capabilities, ease of control, and ability to wrap around objects with simple geometries providing form closure. A prototype of this mechanism was built to evaluate these features.

Pi: A Parallel Architecture Interface for Multi-Model Execution

This thesis defines Pi, a parallel architecture interface that separates model and machine issues, allowing them to be addressed independently. This provides greater flexibility for both the model and machine builder. Pi addresses a set of common parallel model requirements including low latency communication, fast task switching, low cost synchronization, efficient storage management, the ability to exploit locality, and efficient support for sequential code. Since Pi provides generic parallel operations, it can efficiently support many parallel programming models including hybrids of existing models. Pi also forms a basis of comparison for architectural components.

The PHD: A Planar, Harmonic Drive Robot for Joint Torque Control

This thesis details the development of a model of a seven degree of freedom manipulator for position control. Then, it goes on to discuss the design and construction of a the PHD, a robot built to serve two purposes: first, to perform research on joint torque control schemes, and second, to determine the important dynamic characteristics of the Harmonic Drive. The PHD, is a planar, three degree of freedom arm with torque sensors integral to each joint. Preliminary testing has shown that a simple linear spring model of the Harmonic Drive's flexibility is suitable in many situations.

Robot Motion Vision by Fixation

In many motion-vision scenarios, a camera (mounted on a moving vehicle) takes images of an environment to find the "motion'' and shape. We introduce a direct-method called fixation for solving this motion-vision problem in its general case. Fixation uses neither feature-correspondence nor optical-flow. Instead, spatio-temporal brightness gradients are used directly. In contrast to previous direct methods, fixation does not restrict the motion or the environment. Moreover, fixation method neither requires tracked images as its input nor uses mechanical tracking for obtaining fixated images. The experimental results on real images are presented and the implementation issues and techniques are discussed.

Parallel Methods for Synthesizing Whole-Hand Grasps from Generalized Prototypes

This report addresses the problem of acquiring objects using articulated robotic hands. Standard grasps are used to make the problem tractable, and a technique is developed for generalizing these standard grasps to increase their flexibility to variations in the problem geometry. A generalized grasp description is applied to a new problem situation using a parallel search through hand configuration space, and the result of this operation is a global overview of the space of good solutions. The techniques presented in this report have been implemented, and the results are verified using the Salisbury three-finger robotic hand.

Heterogeneous Multi-Robot Cooperation

This report addresses the problem of achieving cooperation within small- to medium- sized teams of heterogeneous mobile robots. I describe a software architecture I have developed, called ALLIANCE, that facilitates robust, fault tolerant, reliable, and adaptive cooperative control. In addition, an extended version of ALLIANCE, called L-ALLIANCE, is described, which incorporates a dynamic parameter update mechanism that allows teams of mobile robots to improve the efficiency of their mission performance through learning. A number of experimental results of implementing these architectures on both physical and simulated mobile robot teams are described. In addition, this report presents the results of studies of a number of issues in mobile robot cooperation, including fault tolerant cooperative control, adaptive action selection, distributed control, robot awareness of team member actions, improving efficiency through learning, inter-robot communication, action recognition, and local versus global control.

Thread Scheduling Mechanisms for Multiple-Context Parallel Processors

Scheduling tasks to efficiently use the available processor resources is crucial to minimizing the runtime of applications on shared-memory parallel processors. One factor that contributes to poor processor utilization is the idle time caused by long latency operations, such as remote memory references or processor synchronization operations. One way of tolerating this latency is to use a processor with multiple hardware contexts that can rapidly switch to executing another thread of computation whenever a long latency operation occurs, thus increasing processor utilization by overlapping computation with communication. Although multiple contexts are effective for tolerating latency, this effectiveness can be limited by memory and network bandwidth, by cache interference effects among the multiple contexts, and by critical tasks sharing processor resources with less critical tasks. This thesis presents techniques that increase the effectiveness of multiple contexts by intelligently scheduling threads to make more efficient use of processor pipeline, bandwidth, and cache resources. This thesis proposes thread prioritization as a fundamental mechanism for directing the thread schedule on a multiple-context processor. A priority is assigned to each thread either statically or dynamically and is used by the thread scheduler to decide which threads to load in the contexts, and to decide which context to switch to on a context switch. We develop a multiple-context model that integrates both cache and network effects, and shows how thread prioritization can both maintain high processor utilization, and limit increases in critical path runtime caused by multithreading. The model also shows that in order to be effective in bandwidth limited applications, thread prioritization must be extended to prioritize memory requests. We show how simple hardware can prioritize the running of threads in the multiple contexts, and the issuing of requests to both the local memory and the network. Simulation experiments show how thread prioritization is used in a variety of applications. Thread prioritization can improve the performance of synchronization primitives by minimizing the number of processor cycles wasted in spinning and devoting more cycles to critical threads. Thread prioritization can be used in combination with other techniques to improve cache performance and minimize cache interference between different working sets in the cache. For applications that are critical path limited, thread prioritization can improve performance by allowing processor resources to be devoted preferentially to critical threads. These experimental results show that thread prioritization is a mechanism that can be used to implement a wide range of scheduling policies.