Dense Simultaneous Localization and Mapping (SLAM) is crucial for robotics and augmented reality applications. In this paper, we optimized the SplaTAM using 3D Gaussian representations to achieve high-quality reconstr...
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Recent advances in Large Language models (LLMs) have been instrumental in autonomous robot control and human-robot interaction by leveraging their vast general knowledge and capabilities to understand and reason acros...
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Currently, the use of cyber-physical systems is becoming widespread in various fields, primarily, because they enable many innovative functionalities through their networking. These systems include complex computation...
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ISBN:
(纸本)9781665479332
Currently, the use of cyber-physical systems is becoming widespread in various fields, primarily, because they enable many innovative functionalities through their networking. These systems include complex computation elements that interacts with the physical environment through sensors or actuators. Their solutions are based on sharing of hardware equipment and software services, targeting to provide added value proposition for various customers. The research and development in this domain are going on not only in the technical direction, but is also targeting the value creation for customers, in a technical and business-oriented manner This paper presents a cyber-physical systems-based business model realized through services composition. The service composition framework integrates Web services which can be interconnected and can be accessed by mobile clients through smartphones, on mobile and wireless communications channels. The response time for multiuser requests for services access is evaluated, because it is an important quality of service performance criteria of our e-business model service composition framework. The results are promising.
Shape morphing of liquid droplets is important for advances in both medical and industrial applications. However current manipulation techniques lack methods to control shapes other than elliptical-shaped droplets. He...
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Magnetorheological dampers are intelligent shock absorbers that utilize the magneto rheological effect to achieve adaptable damping, which can effectively enhance vehicle riding comfort and handling stability. An init...
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In this research, the problem of predictive maintenance using the Industrial Internet of Things (IIoT) systems is investigated with the use of hybrid Convolutional Neural Networks (CNN) and Long Short Term Memory (LST...
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This paper studies a multi-robot visibility-based pursuit-evasion problem in which a group of pursuer robots are tasked with detecting an evader within a two dimensional polygonal environment. The primary contribution...
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ISBN:
(纸本)9781728196817
This paper studies a multi-robot visibility-based pursuit-evasion problem in which a group of pursuer robots are tasked with detecting an evader within a two dimensional polygonal environment. The primary contribution is a novel formulation of the pursuit-evasion problem that modifies the pursuers' objective by requiring that the evader still be detected, even in spite of the failure of any single pursuer robot. This novel constraint, whereby two pursuers are required to detect an evader, has the benefit of providing redundancy to the search, should any member of the team become unresponsive, suffer temporary sensor disruption/failure, or otherwise become incapacitated. Existing methods, even those that are designed to respond to failures, rely on the pursuers to replan and update their search pattern to handle such occurrences. In contrast, the proposed formulation produces plans that are inherently tolerant of some level of disturbance. Building upon this new formulation, we introduce an augmented data structure for encoding the problem state and a novel sampling technique to ensure that the generated plans are robust to failures of any single pursuer robot. An implementation and simulation results illustrating the effectiveness of this approach are described.
Multi-robot teams are becoming an increasingly popular approach for information gathering in large geographic areas, with applications in precision agriculture, surveying the aftermath of natural disasters or tracking...
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ISBN:
(纸本)9781728196817
Multi-robot teams are becoming an increasingly popular approach for information gathering in large geographic areas, with applications in precision agriculture, surveying the aftermath of natural disasters or tracking pollution. These robot teams are often assembled from untrusted devices not owned by the user, making the maintenance of the integrity of the collected samples an important challenge. Furthermore, such robots often operate under conditions of opportunistic, or periodic connectivity and are limited in their energy budget and computational power. In this paper, we propose algorithms that build on blockchain technology to address the data integrity problem, but also take into account the limitations of the robots' resources and communication. We evaluate the proposed algorithms along the perspective of the tradeoffs between data integrity, model accuracy, and time consumption.
In this paper, we present a novel convex optimization approach to address the minimum-time speed planning problem over a fixed path with dynamic obstacle constraints and point-wise speed and acceleration constraints. ...
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ISBN:
(纸本)9781728196817
In this paper, we present a novel convex optimization approach to address the minimum-time speed planning problem over a fixed path with dynamic obstacle constraints and point-wise speed and acceleration constraints. The contributions of this paper are three-fold. First, we formulate the speed planning as an iterative convex optimization problem based on space discretization. Our formulation allows imposing dynamic obstacle constraints and point-wise speed and acceleration constraints simultaneously. Second, we propose a modified vertical cell decomposition method to handle dynamic obstacles. It divides the freespace into channels, where each channel represents a homotopy of free paths and defines convex constraints for dynamic obstacles. Third, we demonstrate significant improvement over previous work on speed planning for typical driving scenarios such as following, merging, and crossing.
Geometric methods for solving open-world off-road navigation tasks, by learning occupancy and metric maps, provide good generalization but can be brittle in outdoor environments that violate their assumptions (e.g., t...
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ISBN:
(纸本)9781728196817
Geometric methods for solving open-world off-road navigation tasks, by learning occupancy and metric maps, provide good generalization but can be brittle in outdoor environments that violate their assumptions (e.g., tall grass). Learning-based methods can directly learn collision-free behavior from raw observations, but are difficult to integrate with standard geometry-based pipelines. This creates an unfortunate conflict - either use learning and lose out on well-understood geometric navigational components, or do not use it, in favor of extensively hand-tuned geometry-based cost maps. In this work, we reject this dichotomy by designing the learning and non-learning-based components in a way such that they can be effectively combined in a self-supervised manner. Both components contribute to a planning criterion: the learned component contributes predicted traversability as rewards, while the geometric component contributes obstacle cost information. We instantiate and comparatively evaluate our system in both in-distribution and out-of-distribution environments, showing that this approach inherits complementary gains from the learned and geometric components and significantly outperforms either of them.
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