6883825
Integrated Task Planning and Control for Mobile Manipulators
In this paper we present an approach to decoupled force/position control of the end-effector along the same direction for redundant robots, and an approach to nonholonomic cart pushing with mobile manipulators. The mobile manipulator is considered as a redundant robot, and a unified dynamic model for an integrated mobile platform and on-board manipulator is developed. The dynamic model is decoupled and linearized using the nonlinear feedback technique in a unified frame. Combining the event-based planning and control method with singularity analysis of the robot arm, a task level action controller is designed and an online kinematic redundancy resolution scheme is developed. The system is stable during normal operation as well as at the occurrence of unexpected obstacles. In addition, explicit force/position control along the same task direction for redundant robots is proposed. The kinematic redundancy of mobile manipulators enables independent control of force and position along the same task directions. To verify the decoupled force/postion scheme, an integrated task planning and control approach is further proposed for the mobile manipulator to complete complicated tasks by regulating its output force. A cart pushing task, which requires both force and position control along the same task direction, is discussed. The cart manipulation task fully integrates trajectory and force planning of the cart, and planning and control of the mobile manipulators. The approaches have been tested on a mobile manipulator consisting of a Nomadic XR4000 and a Puma 560 robot arm. The experimental results demonstrate the efficacy of the approach for the mobile manipulation of a nonholonomic cart.
18268847
Interactive model identification for nonholonomic cart pushed by a mobile manipulator
A model identification method for unknown environments has been developed. By the interactions between a mobile manipulator and the unknown object, a nonholonomic cart, sensory information has been collected to estimate the model parameters of the cart, which are used to control the cart. Since the raw data are contaminated by noise they can not be modeled statistically, a wavelet based least square method is proposed to estimate these parameters for the cart. The raw signal has been decomposed into a certain bandwidth to generate a series of new signals, which are used to estimate the parameters. The new signal, which has the minimal estimation residual in least square sense is adopted as the best estimation. The error convergence of the estimation approach is given. The experimental results indicate that the estimation accuracy can be significantly improved by the use of the proposed method.
15352869
Error Resilient Video Coding using Redundant Pictures
Coding of redundant pictures is supported in the latest international video coding standard H.264 (also known as MPEG-4 part 10 or AVC). This paper proposes a standard-compliant way to encode and decode redundant pictures for improved error resilience. The method is based on a combination of picture-level reference picture selection, reference picture list ordering, and a hierarchical allocation of redundant pictures, which can efficiently prevent temporal error propagation without relying on feedback information. Simulation results show that the method outperforms the optimal loss-aware rate-distortion optimized intra refresh method. The proposed algorithm has been adopted into the H.264 joint model.
6142906
Multiple attribute decision making based on fuzzy preference information on alternatives: Ranking and weighting
This paper investigates the multiple attribute decision making (MADM) problem with fuzzy preference information on alternatives and proposes an eigenvector method to rank them. Three optimization models are introduced to assess the relative importance weights of attributes in a MADM problem, which integrate subjective fuzzy preference relations and objective information in different ways. Since the decisions these models lead to are based on both subjective and objective information, they are more balanced and credible than those that consider only one type of information. An example is provided to illustrate the application of the proposed models.
12928468
Distributed goal recognition algorithms for modular robots
Modular robots are systems composed of a number of independent units that can be reconfigured to fit the task at hand. When the modules are computationally independent, they form a large distributed system with no central controller. We are concerned with the ability of such modular robots to easily recognize the achievement (or lack thereof) of a given goal configuration. We present algorithms for a class of 2D and 3D modular robots, along with correctness and running time analysis. We have successfully implemented the 2D algorithm on the second-generation Crystalline Atomic robot, a self-reconfigurable modular robot under development in our laboratory and we present implementation details and experimental results.
1211237
High Resolution Acquisition, Learning and Transfer of Dynamic 3D Facial Expressions
Synthesis and re-targeting of facial expressions is central to facial animation and often involves significant manual work in order to achieve realistic expressions, due to the difficulty of capturing high quality dynamic expression data. In this paper we address fundamental issues regarding the use of high quality dense 3-D data samples undergoing motions at video speeds, e.g. human facial expressions. In order to utilize such data for motion analysis and re-targeting, correspondences must be established between data in different frames of the same faces as well as between different faces. We present a data driven approach that consists of four parts: 1) High speed, high accuracy capture of moving faces without the use of markers, 2) Very precise tracking of facial motion using a multi-resolution deformable mesh, 3) A unified low dimensional mapping of dynamic facial motion that can separate expression style, and 4) Synthesis of novel expressions as a combination of expression styles. The accuracy and resolution of our method allows us to capture and track subtle expression details. The low dimensional representation of motion data in a unified embedding for all the subjects in the database allows for learning the most discriminating characteristics of each individual's expressions as that person's “expression style”. Thus new expressions can be synthesized, either as dynamic morphing between individuals, or as expression transfer from a source face to a target face, as demonstrated in a series of experiments. 
 
 
 
Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Animation; I.3.5 [Computer Graphics]: Curve, surface, solid, and object representations; I.3.3 [Computer Graphics]: Digitizing and scanning; I.2.10 [Artificial intelligence]: Motion ; I.2.10 [Artificial intelligence]: Representations, data structures, and transforms; I.2.10 [Artificial intelligence]: Shape; I.2.6 [Artificial intelligence]: Concept learning
18364887
An approach to enhancing mobile applications with situation-awareness capabilities
In ubiquitous computing environments, mobile applications reside in devices that move arbitrarily and communicate with each other through wireless means. They are presented with fast changing situations, such as the location variation, service availability change, and resource fluctuation. Under these circumstances, situation-awareness is a desirable feature for mobile applications. Being situation-aware, a mobile application is capable of detecting situation changes and responding to it on-the-fly without bothering the user all the times. This paper presents an approach to enhancing mobile applications with situation-aware capabilities using middleware service.