A variational approach to multi-sensor fusion of images

Past research into multi-modality sensor data fusion has given rise to approaches that are generally heuristic and ad hoc. In this paper we utilize the calculus of variations as the underlying framework for fusing registered images of different modalities when models relating these modalities are available. The result is a mathematically rigorous method for improving the accuracy with which parameters can be estimated. Using both dense and sparse simulated range and intensity data, the proposed approach is demonstrated on the problem of estimating the surface representing the three dimensional structure of a scene. The results indicate that a four to five-fold increase in surface estimation accuracy with respect to the original input data can be realized. Furthermore, an 8%–250% increase in accuracy over surface estimation from each sensing modality alone (i.e., via shape from shading or surface reconstruction) can be realized.H. Pien is supported by Draper Laboratory under IR&D No. 451; J. Gauch is partially supported by the National Science Foundation under Grant IRI-9109431.     

A convergent dynamic window approach to obstacle avoidance

The dynamic window approach (DWA) is a well-known navigation scheme developed by Fox et al. and extended by Brock and Khatib. It is safe by construction, and has been shown to perform very efficiently in experimental setups. However, one can construct examples where the proposed scheme fails to attain the goal configuration. What has been lacking is a theoretical treatment of the algorithm's convergence properties. Here we present such a treatment by merging the ideas of the DWA with the convergent, but less performance-oriented, scheme suggested by Rimon and Koditschek. Viewing the DWA as a model predictive control (MPC) method and using the control Lyapunov function (CLF) framework of Rimon and Koditschek, we draw inspiration from an MPC/CLF framework put forth by Primbs to propose a version of the DWA that is tractable and convergent.     

A grammatical approach to self-organizing robotic systems

In this paper, we define a class of graph grammars that can be used to model and direct concurrent robotic self-assembly and similar self-organizing processes. We give several detailed examples of the formalism and then focus on the problem of synthesizing a grammar so that it generates a given, prespecified assembly. In particular, to generate an acyclic graph we synthesize a binary grammar (rules involve at most two parts), and for a general graph we synthesize a ternary grammar (rules involve at most three parts). In both cases, we characterize the number of concurrent steps required to achieve the assembly. We also show a general result that implies that no binary grammar can generate a unique stable assembly. We conclude the paper with a discussion of how graph grammars can be used to direct the self-assembly of robotic parts.     

A dynamical system approach to realtime obstacle avoidance

This paper presents a novel approach to real-time obstacle avoidance based on Dynamical Systems (DS) that ensures impenetrability of multiple convex shaped objects. The proposed method can be applied to perform obstacle avoidance in Cartesian and Joint spaces and using both autonomous and non-autonomous DS-based controllers. Obstacle avoidance proceeds by modulating the original dynamics of the controller. The modulation is parameterizable and allows to determine a safety margin and to increase the robot’s reactiveness in the face of uncertainty in the localization of the obstacle. The method is validated in simulation on different types of DS including locally and globally asymptotically stable DS, autonomous and non-autonomous DS, limit cycles, and unstable DS. Further, we verify it in several robot experiments on the 7 degrees of freedom Barrett WAM arm.     

A Bayesian approach to fusing uncertain,imprecise and conflicting information

The Dezert–Smarandache theory (DSmT) and transferable belief model (TBM) both address concerns with the Bayesian methodology as applied to applications involving the fusion of uncertain, imprecise and conflicting information. In this paper, we revisit these concerns regarding the Bayesian methodology in the light of recent developments in the context of the DSmT and TBM. We show that, by exploiting recent advances in the Bayesian research arena, one can devise and analyse Bayesian models that have the same emergent properties as DSmT and TBM. Specifically, we define Bayesian models that articulate uncertainty over the value of probabilities (including multimodal distributions that result from conflicting information) and we use a minimum expected cost criterion to facilitate making decisions that involve hypotheses that are not mutually exclusive. We outline our motivation for using the Bayesian methodology and also show that the DSmT and TBM models are computationally expedient approaches to achieving the same endpoint. Our aim is to provide a conduit between these two communities such that an objective view can be shared by advocates of all the techniques.     

A bayesian approach to real-time obstacle avoidance for a mobile robot

Real-time obstacle avoidance is essential for the safe operation of mobile robots in a dynamically changing environment. This paper investigates how an industrial mobile robot can respond to unexpected static obstacles while following a path planned by a global path planner. The obstacle avoidance problem is formulated using decision theory to determine an optimal response based on inaccurate sensor data. The optimal decision rule minimises the Bayes risk by trading between a sidestep maneuver and backtracking to follow an alternative path. Real-time implementation is emphasised here as part of a framework for real world applications. It has been successfully implemented both in simulation and in reality using a mobile robot.     

A sonar approach to obstacle detection for a vision-based autonomous wheelchair

An advanced prototype Computer Controlled Power Wheelchair Navigation System or CCPWNS has been developed to provide autonomy for highly disabled users, whose mix of disabilities makes it difficult or impossible to control their own power chairs in their homes. The working paradigm is “teach and repeat” a mode of control for typical industrial holonomic robots. Ultrasound sensors, which during subsequent autonomous tracking will be used to detect obstacles, also are active during teaching. Based upon post-processed data collected during this teaching event, elaborate trajectories–which may involve multiple direction changes, pivoting and so on, depending upon the requirements of the typically restricted spaces within which the chair must operate–will later be called upon by the disabled rider. An off-line postprocessor assigns an ultrasound profile to the sequence of poses of any taught trajectory. Use of this profile during tracking obviates most of the inherent problems of using ultrasound to avoid obstacles while retaining the ability to near solid objects, such as when passing through a narrow doorway, where required by the environment and trajectory objectives. The work in this article describes a procedure to obtain consistent maps of sonar boundaries during the teaching process, and a preliminary approach to use this information during the tracking phase. The approach is illustrated by results obtained by using the CCPWNS prototype.     

A two-phase analytic approach to robotic system design

A two-phase analytic approach to robotic system design is presented. The first phase evaluates the robotic technological classes according to their functional adequacy; the next phase specifies the desired robotic configuration. The methodology developed here is demonstrated for the case of installing a robot in an automated investment casting shelling production line.     

A robotic approach to fault-tolerant, precision pointing

Future spaceborne optical interferometers and laser satellite communication systems are two space applications that require a precision pointing function in order to meet mission goals. Spaceborne interferometers provide a promising means to discover Earth-like planets in other solar-like systems. The laser communication systems provide a low-power, low-cost, lightweight means of data relay between ground and space and for deep-space communications to interplanetary probes. Both applications share the need to acquire and track a target. The interferometry application requires pointing errors to be submicroradian while the laser communication application requires microradian-level errors. In order to meet the precision pointing requirements found in these applications, a precision pointing strategy has been developed. The strategy employs a hexapod as the pointing platform to reject vibrations from a noisy spacecraft bus over all frequencies: at low frequency using 2- or 3-axis pointing and at high frequency using 6-axis vibration isolation. The benefits include broadband pointing stability without a high-bandwidth pointing sensor or destabilizing excitation of high-frequency structural modes, as well as tolerance to failures. This article outlines this approach to pointing     

Rendezvous-guidance trajectory planning for robotic dynamic obstacle avoidance and interception.

This correspondence presents a novel online trajectory-planning method for the autonomous robotic interception of moving targets in the presence of dynamic obstacles, i.e., position and velocity matching (also referred to as rendezvous). The proposed time-optimal interception method is a hybrid algorithm that augments a novel rendezvous-guidance (RG) technique with the velocity-obstacle approach, for obstacle avoidance, first reported by Fiorini and Shiller. The obstacle-avoidance algorithm itself could not be used in its original form and had to be modified to ensure that the online planned path deviates minimally from the one generated by the RG algorithm. Extensive simulation and experimental analyses, some of which are reported in this correspondence, have clearly demonstrated the tangible time efficiency of the proposed interception method.     

A belief-based sequential fusion approach for fusing manual signs and non-manual signals

Most of the research on sign language recognition concentrates on recognizing only manual signs (hand gestures and shapes), discarding a very important component: the non-manual signals (facial expressions and head/shoulder motion). We address the recognition of signs with both manual and non-manual components using a sequential belief-based fusion technique. The manual components, which carry information of primary importance, are utilized in the first stage. The second stage, which makes use of non-manual components, is only employed if there is hesitation in the decision of the first stage. We employ belief formalism both to model the hesitation and to determine the sign clusters within which the discrimination takes place in the second stage. We have implemented this technique in a sign tutor application. Our results on the eNTERFACE’06 ASL database show an improvement over the baseline system which uses parallel or feature fusion of manual and non-manual features: we achieve an accuracy of 81.6%.     

A robotic intelligent wheelchair system based on obstacle avoidance and navigation functions

Abstract. David Chalmers argues that a materialist theory of mind is in principle unattainable because consciousness does not logically supervene on the physical. In order to demonstrate this, he needs to make a convincing case for the conceptual possibility of zombies. His position, however, is rather tenuous due to the paradoxical nature of phenomenal judgments (among other things). That is, the fact that zombies would have to talk about consciousness and conscious experience may render them inconceivable. It is argued that this paradox is indeed fatal to his position, because zombies will never make meaningful phenomenal judgments.     

A modifed optical flow approach for robotic tracking and acquisition

A robotic system that can visually track and intercept an arbitrary object which is traveling in 2-D at an unknown velocity on a conveyor is to be presented. An eye-in-hand vision system developed by the Robotics and Intelligent Systems Laboratory at NCSU is used as an integral part of the entire tracking and acquisition system. The eye-in-hand system is employed to characterize the object trajectory in real time via a modified optical flow approach. A control strategy has been developed which utilizes the kinematic data that is extracted by the tracking algorithm to intercept the moving object. An overall system configuration and its basic principles are described. The demonstration of the experimental results is presented.     

An algorithmic approach to coordinate transformation for robotic manipulators

Coordinate transformation is one of the most important issues in robotic manipulator control. Robot tasks are naturally specified in work space coordinates, usually a Cartesian frame, while control actions are developed on joint coordinates. Effective inverse kinematic solutions are analytical in nature; they exist only for special manipulator geometries and geometric intuition is usually required. Computational inverse kinematic algorithms have recently been proposed; they are based on general closed-loop schemes which perform the mapping of the desired Cartesian trajectory into the corresponding joint trajectory. The aim of this paper is to propose an effective computational scheme to the inverse kinematic problem for manipulators with spherical wrists. First an insight into the formulation of kinematics is given in order to detail the general scheme for this specific class of manipulators. Algorithm convergence is then ensured by means of the Lyapunov direct method. The resulting algorithm is based on the hand position and orientation vectors usually adopted to describe motion in the task space. The analysis of the computational burden is performed by taking the Stanford arm as a reference. Finally a case study is developed via numerical simulations.     

Neural network approach to trajectory synthesis for robotic manipulators

The paper deals with the collision free trajectory synthesis for industrial robotic manipulators. A new efficient method is proposed that is based on a neural network collision model. The developed iterative transformation procedure provides small computing times for the C-space synthesis and yields sufficiently precise configuration space map for the manipulators with many degrees of freedom. A topologically ordered neural network model is proposed to find the path in the configuration space. The stability of this model is proved using the Lyapunov function technique. To generate the collision model, a modification of the Radial Basis Function Network (RBFN) is used. The developed technique is illustrated by an application example of designing a robotic manufacturing cell for the automotive industry.     

Pepper to fall: a perception method for sweet pepper robotic harvesting

Intelligent Service Robotics - In this paper we propose a robotic system for picking peppers in a structured robotic greenhouse environment. A commercially available robotic manipulator is equipped...