5542960
Metrics for analysis and optimization of grasps and fixtures
We develop two quality measures for the analysis and optimization of grasps and fixtures. The first metric is a measure of stability and is obtained by analyzing the effect of small perturbations in the position of the grasped object or small disturbance forces on the grasped object The second metric is a measure of robustness to errors in the positions of the fixture elements. In addition, we explore the effect that the stiffness of the fixture elements has on these two quality measures and propose a design optimization approach to maximize stability and robustness to errors.
29516211
Control of Contact Interactions with Acatastatic Nonholonomic Constraints
A robot arm performs many tasks by interacting with objects or other robots in the environment. It is important for the robot arm to be able to exhibit controlled interactions with the objects and other robots. The contact motion between the robot arm and an object can be rolling, sliding, or separation. In this article, we address the problem of maintaining rolling contact between the robot arm and an external moving object. We assume that a nominal model of the motion of the moving object is available. The dynamic system that characterizes the motion of the robot arm and the moving object in this case is acatastatic and nonholonomic. We have designed a nonlinear feedback for the control of such a system that successfully maintains rolling contact. Simulation results are presented to demonstrate the effectiveness of the system.
7023425
A Paradigm for Dynamic Coordination of Multiple Robots
In this paper, we present a paradigm for coordinating multiple robots in the execution of cooperative tasks. The basic idea in the paper is to assign to each robot in the team, a role that determines its actions during the cooperation. The robots dynamically assume and exchange roles in a synchronized manner in order to perform the task successfully, adapting to unexpected events in the environment. We model this mechanism using a hybrid systems framework and apply it in different cooperative tasks: cooperative manipulation and cooperative search and transportation. Simulations and real experiments demonstrating the effectiveness of the proposed paradigm are presented.
6879835
Calibrating an air-ground control system from motion correspondences
In this paper we consider the problem of controlling the motion of a vehicle moving on a ground plane based on aerial imagery. In the course of this work we propose a novel analysis of the relationship between the velocity of the vehicle on the ground plane and the velocity of its projection in the image. We show that this relationship provides information about a subset of the parameters of the homography relating the ground plane to the aerial image plane and describe how we can recover this relationship from available measurements.
15738427
A potential field based approach to multi-robot manipulation
We describe a framework for controlling and coordinating a group of robots for cooperative manipulation tasks. The framework enables a decentralized approach to planning and control. It allows the robots to approach the object, organize themselves into a formation that will trap the object, and then transport the object to the desired destination. Our controllers and planners are derived from simple potential fields and the hierarchical composition of potential fields. We show how these potential field based controllers and planners benefit complex group interactions, specifically for manipulating and transporting objects in the plane. Theoretically, we show how we can derive results on formation stability with potential field based controllers in many cases. Simulation results demonstrate successful application to a wide range of examples without showing sensitivity to parameters. Because the framework is decentralized at both trajectory generation level and the estimation and control agent level, our framework can potentially scale to groups of tens and hundreds of robots.
18117520
Control and Coordination of Multiple Mobile Robots in Manipulation and Material Handling Tasks
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.
16892281
The algorithmics of folding proteins on lattices
It should be possible to predict the fold of a protein into its native conformation, once we are given the sequence of the constituent amino acids. This is known as the protein structure prediction problem and is sometimes referred to as the problem of deciphering the second half of the genetic code. While large proteins fold in nature in seconds, computational chemists and biologists have found that folding proteins to their minimum energy conformations is a challenging unsolved optimization problem. Computational complexity theory has been useful in explaining, at least partially, this (Levinthal's) paradox. The pedagogic cross-disciplinary survey by Ngo, Marks and Karplus (Computational Complexity, Protein Structure Prediction and the Levinthal Paradox, Birkhauser, Basel, 1994) provides an excellent starting point for nonbiologists to take a plunge into the problem of folding proteins. Since then, there has been remarkable progress in the algorithmics of folding proteins on discrete lattice models, an account of which is presented herein.
5947607
Enhancing data analysis with noise removal
Removing objects that are noisy is an important goal of data cleaning as noise hinders most types of data analysis. Most existing data cleaning methods focus on removing noise that is the product of low-level data errors that result from an imperfect data collection process, but data objects that are irrelevant or only weakly relevant can also significantly hinder data analysis. Thus, if the goal is to enhance the data analysis as much as possible, these objects should also be considered as noise, at least with respect to the underlying analysis. Consequently, there is a need for data cleaning techniques that remove both types of noise. Because data sets can contain large amounts of noise, these techniques also need to be able to discard a potentially large fraction of the data. This paper explores four techniques intended for noise removal to enhance data analysis in the presence of high noise levels. Three of these methods are based on traditional outlier detection techniques: distance-based, clustering-based, and an approach based on the local outlier factor (LOF) of an object. The other technique, which is a new method that we are proposing, is a hyperclique-based data cleaner (HCleaner). These techniques are evaluated in terms of their impact on the subsequent data analysis, specifically, clustering and association analysis. Our experimental results show that all of these methods can provide better clustering performance and higher quality association patterns as the amount of noise being removed increases, although HCleaner generally leads to better clustering performance and higher quality associations than the other three methods for binary data.
16578484
On the Complexity of Error Explanation
When a system fails to satisfy its specification, the model checker produces an error trace (or counter-example) that demonstrates an undesirable behavior, which is then used in debugging the system. Error explanation is the task of discovering errors in the system or the reasons why the system exhibits the error trace. While there has been considerable recent interest in automating this task and developing tools based on different heuristics, there has been very little effort in characterizing the computational complexity of the problem of error explanation. 
 
In this paper, we study the complexity of two popular heuristics used in error explanation. The first approach tries to compute the smallest number of system changes that need to be made in order to ensure that the given counter-example is no longer exhibited, with the intuition being that these changes are the errors that need fixing. The second approach relies on the observation that differences between correct and faulty runs of a system shed considerable light on the sources of errors. In this approach, one tries to compute the correct trace of the system that is closest to the counter-example. We consider three commonly used abstractions to model programs and systems, namely, finite state Mealy machines, extended finite state machines and pushdown automata. We show that the first approach of trying to find the fewest program changes is NP-complete no matter which of the three formal models is used to represent the system. Moreover we show that no polynomial factor approximation algorithm for computing the smallest set of changes is possible, unless P = NP. For the second approach, we present a polynomial time algorithm that finds the closest correct trace, when the program is represented by a Mealy machine or a pushdown automata. When the program is represented by an extended finite state machine, the problem is once again NP-complete, and no polynomial factor approximation algorithm is likely.
26507710
Graph Partitioning for Dynamic, Adaptive, and Multi-phase Computations
Algorithms that find good partitionings of highly unstructured graphs are critical in developing efficient algorithms for problems in a variety of domains such as scientific simulations that require solution to large sparse linear systems, VLSI design, and data mining. Even though this problem is NP-hard, efficient multi-lev el algorithms have been developed that can find good partitionings of static irregular meshes. The problem of graph partitioning becomes a lot more challenging when the graph is dynamically evolving (e.g., in adaptive computations), or if computation in multiple phases needs to be balanced simultaneously. This talk will discuss these challenges, and then describe some of our recent research in addressing them.
206849499
Recovery in Main Memory Database Systems
In this paper we present a recovery mechanism for main memory database, which does not treat volatile RAM as a buffer and uses a limited size non-volatile RAM for efficient logging and archiving. It allows database recovery in parallel to the execution of normal transactions, it combines Undo and Redo into a single operation in many situations and eliminates the need for checkpointing. Under partial recovery our algorithm manages to identify and recovers only dirty data for bringing the entire database into a consistent state.