This paper proposes a novel decision-making framework for planning "when" and "where" to deploy robots based on prior data with the goal of persistently monitoring a spatio-temporal phenomenon in a...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
This paper proposes a novel decision-making framework for planning "when" and "where" to deploy robots based on prior data with the goal of persistently monitoring a spatio-temporal phenomenon in an environment. We specifically focus on large lake monitoring, where remote sensors, such as satellites, can provide a snapshot of the target phenomenon at regular cycles. Between these cycles, Autonomous Surface Vehicles (ASVs) can be deployed to maintain an up-to-date model of the phenomenon. However, deploying ASVs has a significant logistical overhead in terms of time and cost. It requires a team of people to go on site and spend typically a day to monitor the deployment. It is vital to not only be intentional about where to sample in the environment on a given day, but also determine the worth of deploying the ASVs that day at all. Therefore, we propose a persistent monitoring strategy that provides the days and locations of when and where to sample with the robots by leveraging Gaussian Process model estimates of future trends based on collected remote sensing and point measurement data. Our approach minimizes the number of days and locations for sampling, while preserving the quality of estimates. Through simulation experiments using realistic spatio-temporal datasets, we demonstrate the benefits of our approach over traditional deployment strategies, including significant savings on the effort and operational cost of deploying the ASVs.
In many intelligent transportation systems, predicting the future motion of heterogeneous traffic participants is a fundamental but challenging task due to various factors encompassing the agents’ dynamic states, int...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
In many intelligent transportation systems, predicting the future motion of heterogeneous traffic participants is a fundamental but challenging task due to various factors encompassing the agents’ dynamic states, interactions with neighboring agents and surrounding traffic infrastructures, and their stochastic and multi-modal natural behavior tendencies. However, existing approaches have limitations as they either focus solely on static, pairwise interactions, ignoring interactions of varied granularity, or fail to tackle agents’ heterogeneity. In this paper, instead of focusing solely on pairwise interactions, we propose a Heterogenous Hypergraph Graph Neural Network (HHGNN) based motion prediction model that leverages the nature of hypergraph to encode the groupwise interactions among traffic participants. Moreover, we propose the type-aware two-level hypergraph message passing module (TTHMS) with learnable hyperedge-type embeddings to model the intra-group and inter-group level interactions among heterogeneous traffic agents (e.g., vehicles, pedestrians, and cyclists). Besides, We integrate a scene context fusion layer in TTHMS to incorporate the scene context. Comparison and ablation experiments on the Waymo Open Motion Dataset (WOMD) demonstrate HHGNN’s effectiveness within the motion prediction task.
Traditional fault diagnosis methods often suffer from the uncertainty of characteristics under complex conditions and are overly dependent on human experience, leading to low diagnostic accuracy. This paper constructs...
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ISBN:
(数字)9798350366174
ISBN:
(纸本)9798350366181
Traditional fault diagnosis methods often suffer from the uncertainty of characteristics under complex conditions and are overly dependent on human experience, leading to low diagnostic accuracy. This paper constructs and improves an AlexNet network model by replacing the traditional local response normalization layer with a batch normalization layer to more effectively normalize the data, thus making the network easier to train. By combining time-frequency maps to enhance fault features, the diagnostic performance is significantly improved. Experimental results show that the improved TFECG-AlexNet model achieves a classification accuracy of 99.2% in bearing fault diagnosis, verifying the effectiveness and reliability of the method.
Object grasping in cluttered scenes is a widely investigated field of robot manipulation. Most of the current works focus on estimating grasp pose from point clouds based on an efficient single-shot grasp detection ne...
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ISBN:
(纸本)9781728190778
Object grasping in cluttered scenes is a widely investigated field of robot manipulation. Most of the current works focus on estimating grasp pose from point clouds based on an efficient single-shot grasp detection network. However, due to the lack of geometry awareness of the local grasping area, it may cause severe collisions and unstable grasp configurations. in this paper, we propose a two-stage grasp pose refinement network which detects grasps globally while fine-tuning low-quality grasps and filtering noisy grasps locally. Furthermore, we extend the 6-DoF grasp with an extra dimension as grasp width which is critical for collisionless grasping in cluttered scenes. It takes a single-view point cloud as input and predicts dense and precise grasp configurations. To enhance the generalization ability, we build a synthetic single-object grasp dataset including 150 commodities of various shapes, and a complex multi-object cluttered scene dataset including 100k point clouds with robust, dense grasp poses and mask annotations. Experiments conducted on Yumi IRB-1400 Robot demonstrate that the model trained on our dataset performs well in real environments and outperforms previous methods by a large margin.
In recent years, the field of facial expression recognition (FER) has become increasingly challenging and active. To improve recognition accuracy, facial expression recognition based on a deep learning model has attra...
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Crack localization and segmentation are essential for infrastructure maintenance and safety assessments, enabling timely repairs and preventing structural failures. Despite advancements in deep learning, crack segment...
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ISBN:
(数字)9798331509644
ISBN:
(纸本)9798331509651
Crack localization and segmentation are essential for infrastructure maintenance and safety assessments, enabling timely repairs and preventing structural failures. Despite advancements in deep learning, crack segmentation remains challenging due to the need for real-time performance and computational efficiency. Existing methods often rely on large, resource-intensive models, limiting their practical deployment. We introduce CrackSegMamba, a novel model featuring Channel-wise Parallel Mamba (CPM) Modules, which achieves state-of-the-art performance with fewer than 0.23 million parameters and just 0.7 GFLOPs. CrackSegMamba reduces computational cost by 40-fold and parameter count by nearly 100-fold compared to existing models, while maintaining comparable accuracy. These features make CrackSegMamba ideal for real-time applications. Additionally, we present Crack20000, an annotated dataset of 20,000 concrete crack images to support further research and validation. Evaluations on the Crack500 [1] and Crack20000 datasets demonstrate that CrackSegMamba delivers comparable accuracy to leading methods, with significantly reduced computational requirements. Project page is available at: https://***/view/cracksegmamba.
Traditional data-driven voltage sensitivity analysis methods exhibit limited adaptability in response to network topology changes, which need to retrain the neural networks according to the updated topology informatio...
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ISBN:
(数字)9798331506797
ISBN:
(纸本)9798331506803
Traditional data-driven voltage sensitivity analysis methods exhibit limited adaptability in response to network topology changes, which need to retrain the neural networks according to the updated topology information. To address this issue, this paper proposes a voltage sensitivity perception method based on graph neural networks (GNN). By embedding prior topology knowledge for information propagation, the method can effectively capture the feature mapping patterns of various topologies, thereby demonstrating improved model generalization ability in unknown topology scenarios. Matlab/Simulation results show that, compared to conventional data-driven algorithms, the proposed method achieves faster and more accurate sensitivity calculations in time-varying topology scenarios.
Quadrotor drone control is a popular domain for control research and reinforcement learning applications. Existing control applications for quadrotor drones can be leveraged to improve the performance of reinforcement...
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ISBN:
(数字)9798350394245
ISBN:
(纸本)9798350394252
Quadrotor drone control is a popular domain for control research and reinforcement learning applications. Existing control applications for quadrotor drones can be leveraged to improve the performance of reinforcement learning agents. We propose methods for interfacing a reinforcement learning agent with a typical quadrotor drone flight controller. One method is to provide auxiliary rotor commands that adjust the output of a static PID controller. The other method is for an agent to identify continuous absolute controller parameters for the PID controller. These methods are used to train agents and evaluate their performance through simulation and compare against a typical reinforcement learning approach as well as a static PID controller. The results show that the trained agents are able to successfully mitigate wind disturbances and outperform both typical reinforcement learning agents and a typical PID controller.
Rigid joints have good position control accuracy due to their stiffness. On the other hand, elastic joints are advantageous when interacting with the environment due to their compliance characteristics. Most of the cu...
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ISBN:
(数字)9798350394245
ISBN:
(纸本)9798350394252
Rigid joints have good position control accuracy due to their stiffness. On the other hand, elastic joints are advantageous when interacting with the environment due to their compliance characteristics. Most of the current robot arms use all rigid joints or all elastic joints in their designs. This work presents a highly efficient approach to formulate both inverse and forward dynamics of robots with mixed rigid-elastic joints using recursive Newton-Euler algorithm. To simplify the modelling process, a unified rigid body is proposed, where the link and all motors attached to it are unified as a whole unyielding entity. In addition, the effect of gear ratio is considered in the development of this modelling approach. Successively, an inverse dynamics controller is presented. Finally, simulations are conducted based on the proposed modelling method and the inverse dynamics control algorithm.
The recycling of electronic waste (e-waste) presents significant challenges due to the diverse range of device models and conditions that need to be treated. This paper presents an application study that evaluates a r...
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ISBN:
(数字)9798350364194
ISBN:
(纸本)9798350364200
The recycling of electronic waste (e-waste) presents significant challenges due to the diverse range of device models and conditions that need to be treated. This paper presents an application study that evaluates a reconfigurable modular robotic workcell platform and adaptation at different levels to address these challenges. The performance and effectiveness of the approach are assessed through two common use cases from the e-waste recycling industry: heat cost allocator disassembly and smoke detector disassembly, with the goal of battery removal. The initial setup time (the definition of dismantling procedures for a new device type), reconfiguration times (changing the workcell layout to switch between processes for different known device types) and cycle times (for dismantling one device) were assessed in terms of their key performance indicators (KPIs). The evaluation demonstrated the flexibility and adaptability of the workcell, which enables streamlined process development and efficient disassembly of electronic devices in different scenarios.
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