The study of perception, an essential function for autonomous robotics, has led to new developments in the fields of modeling, stochastic data processing, and control. The authors describe a surveillance robot dedicat...
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The study of perception, an essential function for autonomous robotics, has led to new developments in the fields of modeling, stochastic data processing, and control. The authors describe a surveillance robot dedicated to heterogeneous data fusionalgorithms' implementation and real-size real-time testing, which has become necessary to validate these emerging techniques in a realistic environment. This robot holds various sensors mounted on rotational units and provides a multi-degree-of-freedom command. The design includes original distributed hardware and software architectures encompassing the specific data processing, fusion, and feedback control. Modularity and a user-friendly interface allow easy design of perception control applications.< >
A method to fully diagnose energy losses in plasma devices with a reduced experimental setup is presented. This technique is based on the use of single movable detectors instead of full detector arrays, and it is espe...
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A method to fully diagnose energy losses in plasma devices with a reduced experimental setup is presented. This technique is based on the use of single movable detectors instead of full detector arrays, and it is especially suitable for devices with large toroidal and/or poloidal asymmetries, such as the TJ-ii flexible heliac, that would require a large number of measurements at different positions. Local distributions of the plasma losses are deduced from the bolometer signals by using general tomography algorithms. The number of lines of sight is only limited by the sensitivity of the detector and the number of reproducible shots in the device, and, as a consequence, the possibility of obtaining a well-defined mapping of the plasma energy losses can be even better than in the standard case based on the use of detector arrays with a fixed number of detectors. The method has been tested in the TJ-I tokamak and the results obtained are presented, together with a simulation of its application to the TJ-ii heliac.< >
Two multi-sensor integration algorithms useful in mobile robotics applications are reviewed. A minimal set of utilities are then developed which enable implementation of these algorithms on a distributed memory concur...
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Two multi-sensor integration algorithms useful in mobile robotics applications are reviewed. A minimal set of utilities are then developed which enable implementation of these algorithms on a distributed memory concurrent computer.
This research is concerned with the intelligent control of autonomous robot systems in unstructured, highly uncertain environments. We show that the theory of fractal sets is a useful tool for approximate machine reas...
This research is concerned with the intelligent control of autonomous robot systems in unstructured, highly uncertain environments. We show that the theory of fractal sets is a useful tool for approximate machine reasoning at multiple levels of precision. We first derive an algorithm for the classification and combination of incomplete and imprecise patterns of evidence. We also derive a new algorithm for belief propagation across incompatible frames. These algorithms are then applied to the sensorimotor control of robotic systems. The first application is to a sensorfusion problem, involving the recognition of 3D objects from a combination of vision and touch data. The second application is to sensor based grasping with multifingered hands. These applications integrate high and low level representations of (i) irregular and sparse sensory patterns and (ii) preshaped grasp control under uncertainty.
Multi- sensorfusion, at the most basic level, can be cast into a concise, elegant model. Reality demands, however, that this model be modified and augmented. These modifications often result in software systems that ...
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The strategic and tactical applications for autonomous submersibles place great demands on the platforms' passive sonar signal and data processing abilities. It is necessary to overcome the limited acoustic apertu...
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The strategic and tactical applications for autonomous submersibles place great demands on the platforms' passive sonar signal and data processing abilities. It is necessary to overcome the limited acoustic aperture and lack of human supervision by exploiting synergism between front-end signal processing functions and back-end data fusionalgorithms. An Information Management System Architecture is presented which represents the relationship between the Information Extraction (signal processing and data fusion), Situation Assessment, and Mission Planner. The information extraction requirements (essentially the emulation of manned platform detection, classification, and localization) drive the design of the signal and data processing architectures. The signal processing architecture is partitioned into a generic, multifunction (e.g. sonar, comm, navigation) processor complemented by an ancillary signal processor which supports the knowledge-based signal interpretation function of information fusion.
The proceedings contain 33 papers. The topics discussed include: signal processing computational needs;the use of pivoting to improve the numerical performance of toeplitz solvers;stability, strong stability, and weak...
The proceedings contain 33 papers. The topics discussed include: signal processing computational needs;the use of pivoting to improve the numerical performance of toeplitz solvers;stability, strong stability, and weak stability of algorithms for solving linear equations;a conjugate gradient method for the solution of equality constrained least squares problems;parallel QR decomposition of toeplitz matrices;on the implementation of a fully parallel algorithm for the symmetric eigenvalue problem;systolic array computation of the SVD of complex matrices;highly parallel eigenvector update methods with applications to signal processing;a systolic array for linearly constrained least-squares problems;analysis of a recursive least squares signal processing algorithm;and a subspace approach to determining sensor gain and phase with applications to array processing.
A new approach to the quantization of discrete cosine transformed subimage data is discussed. It is shown that physical modeling of the sensor in combination with a power spectrum model of the scene leads to a direct ...
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Most industrial robots today have little or no sensory capability. Feedback is limited to information about joint positions, combined with a few interlock and timing signals. These robots can function only in an envir...
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ISBN:
(数字)9783642755309
ISBN:
(纸本)9783642755323
Most industrial robots today have little or no sensory capability. Feedback is limited to information about joint positions, combined with a few interlock and timing signals. These robots can function only in an environment where the objects to be manipulated are precisely located in the proper position for the robot to grasp (i. e. , in a structured environment). For many present industrial applications, this level of performance has been adequate. With the increasing demand for high performance sensor-based robot manipulators in assembly tasks, meeting this demand and challenge can only be achieved through the consideration of: 1) efficient acquisition and processing of intemaVextemal sensory information, 2) utilization and integration of sensory information from various sensors (tactile, force, and vision) to acquire knowledge in a changing environment, 3) exploitation of inherent robotic parallel algorithms and efficient VLSI architectures for robotic computations, and finally 4) system integration into a working and functioning robotic system. This is the intent of the Workshop on sensor-Based Robots: algorithms and architectures - to study the fundamental research issues and problems associated with sensor-based robot manipulators and to propose approaches and solutions from various viewpoints in improving present day robot manipula tors in the areas of sensorfusion and integration, sensory information processing, and parallel algorithms and architectures for robotic computations.
Autonomous driving represents a significant advancement in the transportation industry, enhancing vehicle intelligence, optimizing traffic management, and improving user experiences. Central to these innovations is de...
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Autonomous driving represents a significant advancement in the transportation industry, enhancing vehicle intelligence, optimizing traffic management, and improving user experiences. Central to these innovations is deep learning, which enables systems to handle complex data and make informed decisions. Our survey explores critical applications of deep learning in autonomous driving, such as perception and detection, localization and mapping, and decision-making and control. We investigate specialized deep learning techniques, including convolutional neural networks, recurrent neural networks, self-attention transformers, and their variants, among others. These methods are applied within various learning paradigms—supervised, unsupervised and reinforcement learning—to suit the specific needs of autonomous driving. Our analysis evaluates the effectiveness, benefits, and limitations of these technologies, focusing on their integration with other intelligent algorithms to enhance system performance. Furthermore, we examine the architectures of autonomous systems, analyzing how knowledge and information are organized from modular, pipeline-based frameworks to comprehensive end-to-end models. By presenting an exhaustive overview of the progressing domain of autonomous driving and bridging various research areas, our survey aims to synthesize diverse research threads into a unified narrative. This effort not only aims to enhance our understanding but also pushes the boundaries of what is achievable in this interdisciplinary field.
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