Validating the kinematic feasibility of a planned robot motion and finding corresponding inverse solutions are time-consuming processes, especially for long-horizon manipulation tasks. Most existing approaches are bas...
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ISBN:
(纸本)9781728190778
Validating the kinematic feasibility of a planned robot motion and finding corresponding inverse solutions are time-consuming processes, especially for long-horizon manipulation tasks. Most existing approaches are based on solving iterative gradient-based optimization, so the processes are time-consuming and have a high risk of falling in local minima In this work, we propose a unified framework to learn a kinematic feasibility model and a one-shot inverse mapping model for a redundant robot manipulator. Once they are trained, the models can compute the kinematic reachability of a target pose and its inverse solutions without iterative process. We validate our approach using a 7-DOF robot arm with an object grasping application.
The contribution sketches the emergence of the present days robotic art as the result and reflection of activities in fields of art creativity and science fiction (both literature and cinematography) in the first half...
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Traditional approaches to diagnosis of manufacturing systems have yielded to artificial intelligence approaches over recent years, as system complexity has increased;but results have been mixed. Expert systems, using ...
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ISBN:
(纸本)0818634529
Traditional approaches to diagnosis of manufacturing systems have yielded to artificial intelligence approaches over recent years, as system complexity has increased;but results have been mixed. Expert systems, using symptom-based (shallow reasoning) approaches have been too limited, while structural-based (deep reasoning) approaches have required excessive computational resources. This paper presents a hybrid model for diagnostics that is computationally efficient, and at the same time incorporates the potential to improve its performance with use through a two-phase learning scheme.
With the increasingly serious haze disaster, the development of dehazing technology is highly concerned. But the current software dehazing algorithm suffers from too high computational complexity. We selects the algor...
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ISBN:
(纸本)9781728160832
With the increasingly serious haze disaster, the development of dehazing technology is highly concerned. But the current software dehazing algorithm suffers from too high computational complexity. We selects the algorithm based on dark channel prior with sky preservation [1] as the base software algorithm, and a hardware accelerator is designed to simplify the data transmission process and optimize the data processing. The whole operation simulation of the accelerator is realized in NC-Verilog hardware design environment. The accelerator can achieve 75fps@640x480 video dehazing under 0.18 mu m CMOS technology. It can be applied to driving assistance and surveillance system.
There has been a recent interest in utilizing contextual knowledge to improve multi-label visual recognition for intelligent agents like robots. Natural Language Processing (NLP) can give us labels, the correlation of...
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ISBN:
(纸本)9781467356411;9781467356435
There has been a recent interest in utilizing contextual knowledge to improve multi-label visual recognition for intelligent agents like robots. Natural Language Processing (NLP) can give us labels, the correlation of labels, and the ontological knowledge about them, so we can automate the acquisition of contextual knowledge. In this paper we show how to use tools from NLP in conjunction with Vision to improve visual recognition. There are two major approaches: First, different language databases organize words according to various semantic concepts. Using these, we can build special purpose databases that can predict the labels involved given a certain context. Here we build a knowledge base for the purpose of describing common daily activities. Second, statistical language tools can provide the correlations of different labels. We show a way to learn a language model from large corpus data that exploits these correlations and propose a general optimization scheme to integrate the language model into the system. Experiments conducted on three multi-label everyday recognition tasks support the effectiveness and efficiency of our approach, with significant gains in recognition accuracies when correlation information is used.
As environments involving both robots and humans become increasingly common, so does the need to account for people during planning. To plan effectively, robots must be able to respond to and sometimes influence what ...
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ISBN:
(纸本)9781728190778
As environments involving both robots and humans become increasingly common, so does the need to account for people during planning. To plan effectively, robots must be able to respond to and sometimes influence what humans do. This requires a human model which predicts future human actions. A simple model may assume the human will continue what they did previously;a more complex one might predict that the human will act optimally, disregarding the robot;whereas an even more complex one might capture the robot's ability to influence the human. These models make different trade-offs between computational time and performance of the resulting robot plan. Using only one model of the human either wastes computational resources or is unable to handle critical situations. In this work, we give the robot access to a suite of human models and enable it to assess the performance-computation trade-off online. By estimating how an alternate model could improve human prediction and how that may translate to performance gain, the robot can dynamically switch human models whenever the additional computation is justified. Our experiments in a driving simulator showcase how the robot can achieve performance comparable to always using the best human model, but with greatly reduced computation.
A bacteria-immunity system model is formulated to describe the interaction between bacteria and immune cells with quorum sensing. Time delay is introduced to study this system. The stability and the conditions for the...
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Modeling the Coulomb Friction Cone in trajectory optimization is typically done by linearizing it along a series of vectors. Often, these vectors define the edges of polyhedral estimations of the cone. This article pr...
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ISBN:
(纸本)9781728190778
Modeling the Coulomb Friction Cone in trajectory optimization is typically done by linearizing it along a series of vectors. Often, these vectors define the edges of polyhedral estimations of the cone. This article provides an alternate approach that samples the cone along a vector that satisfies the Maximum Dissipation Principle, which is shown to be significantly more computationally tractable. The proposed technique uses the polar representation of the relative velocity of a contact point on a surface to determine the direction of the resultant friction force and linearizes the friction cone along this vector. This study describes the development of the proposed model. Thereafter, a trajectory optimization experiment was conducted to compare the traditional four-sided polyhedral estimation of the friction cone with the novel method. Compute time and computational complexity were used as performance metrics in this study. Results from these experiments indicate that the proposed method reduced the compute time by 39.13% and a 57.00% reduction in inequality constraints when compared to the 4-sided polyhedral estimation.
We describe a different approach to robotics in which emotions are at center-stage playing a much larger role than just that of facilitating emotional expression. Drawing inspiration from work in Psychology, Neuroscie...
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We describe a different approach to robotics in which emotions are at center-stage playing a much larger role than just that of facilitating emotional expression. Drawing inspiration from work in Psychology, Neuroscience, and Ethology, we have developed a computational framework that captures important aspects of emotional processing and integrates these with other models of perception, attention, motivation, behavior, and motor control. We have followed this approach to control several autonomous agents, including a physical robot that is capable of emotional expression while exhibiting robust and effective behavior.
This paper deals with the integrated production planning and scheduling over a finite horizon for synchronous automobile assembly lines. We present a mixed integer programming model such that it can facilitate to deve...
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