Showing papers by "Mongi A. Abidi published in 1991"

Autonomous robotic inspection and manipulation using multisensor feedback

Abstract: A six-degree-of-freedom industrial robot to which was added a number of sensors-vision, range, sound, proximity, force/torque, and touch-to enhance its inspection and manipulation capabilities is described. The work falls under the scope of partial autonomy. In teleoperation mode, the human operator prepares the robotic system to perform the desired task. Using its sensory cues, the system maps the workspace and performs its operations in a fully autonomous mode. Finally, the system reports back to the human operator on the success or failure of the task and resumes its teleoperation mode. The feasibility of realistic autonomous robotic inspection and manipulation tasks using multisensory information cues is demonstrated. The focus is on the estimation of the three-dimensional position and orientation of the task panel and the use of other nonvision sensors for valve manipulation. The experiment illustrates the need for multisensory information to accomplish complex, autonomous robotic inspection and manipulation tasks. >

Nondeterministic approaches in data fusion: a review

Abstract: Information gathered by different knowledge sources from the same scene are often uncertain, imprecise, fuzzy, vague, or incomplete. Numerous papers have appeared in the literature dealing with the fusion of this kind of information using different frameworks. In this paper, we review a number of non-deterministic methods for solving the fusion problem. The use of Bayes’ rules in resolving ambiguities and conflicts associated with given bodies of evidence is examined. We also present the theory of belief (i.e., Dempster’s rule of combination) and its use in evidence fusion. The theory of possibility, which has emerged from the theory of fuzzy sets, the sym metric sums, and other hybrid’ techniques are also examined. A meaningful comparison among all these methods is carried out using the same set of synthetic data presented in various frameworks. This example is inspired from a real robotic experiment. The strengths and weaknesses of these techniques are discussed in some detail. Based upon the performance of each method on this particular fusion problem, general comparative remarks are given.

A regularized multi-dimensional data fusion technique

Abstract: An uncertainty and data fusion approach was developed and tested. This fusion algorithm is based on the interaction between two constraints: (1) the principle of knowledge source corroboration, which tends to maximize the final belief in a given proposition (often modeled by a probability density function or fuzzy membership distribution), if either of the knowledge sources supports the occurrence of this proposition, and (2) the principle of belief enhancement/withdrawal, which adjusts the belief of one knowledge source according to the belief of the second knowledge source by maximizing the similarity between the two source outputs. This method has been tested using various features from synthetic and real data of various types of many dimensionalities, resulting fused data which satisfy both principles. >

Evaluation of a pose estimation algorithm using single perspective view

Abstract: In [1 we have introduced a new pose eslimalion algoriihm based on a ierahedravolume measuremeni meihod. In This paper we preseni an exiended iheorelical and experinienal evalualion of This algorithm. The pose esimaion iechnique exploits ihe redundancy in the georneric informaiion inherent in the daia io minimize errors in Ihe recovery of the pose of a vision system. The resuUs of This study show among other Things ha errors in the pose recovery are sensiiive io he exact coordinales of image poinis. These errors increase drasiically as the camera is moved awayfrom the argeL To correciforihis sensiiviy an enhanced version ofihis algoriihm based on image/shape resoraion is introduced. This enhancement ulilizes a Conjugate Gradieni based lechnique io minimize the effect of errors inhereni in he image daia. This enhanced technique is lesied on images of synihelic daia and of real world scenes. These experimenis show Thai a significant improvemeni of he solulion o The pose can be achieved by this technique. 1

Parallel data fusion on a hypercube multiprocessor

Abstract: A new parallel analytic data fusion method has been developed and tested on real image pairs. This fusion algorithm is based on the interaction between two analytically formulated constraints: (1) the principle of Knowledge Source Aggregation, and (2) the principle of Belief Enhancement/Withdrawal. In this paper, we discuss ways in which a message-passing multiprocessor employing the hypercube interconnection topology is exploited in order to achieve optimal speed-up in the parallel data fusion algorithm. Image parallelism is optimized by having multiple processors execute the same task but operate on different subsets of the data. Two numerical methods used to solve a system of partial differential equations resulting from the use of the Euler-Lagrange equation for the fusion process are compared. Tests conducted on an NCUBE/4 parallel computer have resulted in an effective implementation of the complete fusion process.

Range image-based object detection and localization for HERMIES III mobile robot

Abstract: Range images can provide very useful information for operating robotic systems in cluttered and dynamic environments. Development of an integrated mobile robotic system which can acquire, process, analyze and perform actions based upon such analysis, in real time, involves the resolution of many complex issues. In this paper we describe research activities to demonstrate development and operation of an autonomous mobile robotic system capable of detecting 55 gallon drums in range images, and then performing complex radiation survey tasks on the detected drums. The robot must perform tasks such as precision navigation with crabbing, object identification and localization, docking with the located object, and scanning of the object for radiation. The processing of range data involved five steps: (1) correction of the cosine effect, (2) image filtering, (3) calculation of surface normals, (4) image segmentation, and (5) object recognition and localization. The results of the range processing procedures are presented. The novelty of the techniques is that they are being adapted, combined, and applied to real data obtained form a sensor-based mobile robot in a cluttered environment. Experimental results demonstrating the robustness of the approach on real range data are presented. We also present some experimental results of a Geometric Modeling and Sensor Simulation System for generating synthetic range imagery using 3D world models.