Predicting and controlling crowd dynamics in emergencies is one of the main objectives of simulated emergency exercises. However, during emergency exercises, there is often a lack of sense of danger by the actors invo...
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Anomaly Detection has been a significant field in Machine Learning since it began gaining traction. In the context of computer Vision, the increased interest is notorious as it enables the development of video process...
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The need for aerial platforms capable of sustained operation is critical in fields such as surveillance, agricultural monitoring, disaster response, and temporary telecommunication systems. Traditional unmanned aerial...
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This paper presents a novel Nonlinear Model Predictive Controller (NMPC) architecture for trajectory tracking of omnidirectional robots. The key innovation lies in the method of handling constraints on maximum velocit...
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Face recognition (FR) systems are vulnerable to morphing attacks, which refer to face images created by morphing the facial features of two different identities into one face image to create an image that can match bo...
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Multimodal perception systems enhance the robustness and adaptability of autonomous mobile robots by integrating heterogeneous sensor modalities, improving long-term localisation and mapping in dynamic environments an...
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ISBN:
(数字)9798331538606
ISBN:
(纸本)9798331538613
Multimodal perception systems enhance the robustness and adaptability of autonomous mobile robots by integrating heterogeneous sensor modalities, improving long-term localisation and mapping in dynamic environments and human-robot interaction. Current mobile platforms often focus on specific sensor configurations and prioritise cost-effectiveness, possibly limiting the flexibility of the user to extend the original robots further. This paper presents a methodology to integrate multimodal perception into a ground mobile platform, incorporating wheel odometry, 2D laser scanners, 3D Light Detection and Ranging (LiDAR), and RGBD cameras. The methodology describes the electronics design to power devices, firmware, computation and networking architecture aspects, and mechanical mounting for the sensory system based on 3D printing, laser cutting, and bending metal sheet processes. Experiments demonstrate the usage of the revised platform in 2D and 3D localisation and mapping and pallet pocket estimation applications. All the documentation and designs are accessible in a public repository.
This article presents an investigation into the classification of endoscopic capsule pathologies using Multiple Instance Learning (MIL) methods in conjunction with deep neural network architectures. The primary proble...
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Rising industrial complexity demands efficient mobile robots to drive automation and productivity. Effective navigation relies on perception, localization, mapping, path planning, and motion control, with path plannin...
ISBN:
(数字)9798331538606
ISBN:
(纸本)9798331538613
Rising industrial complexity demands efficient mobile robots to drive automation and productivity. Effective navigation relies on perception, localization, mapping, path planning, and motion control, with path planning being key. The Time Enhanced A * (TEA *) algorithm extends A * by adding time as a dimension to resolve temporal conflicts in multi-robot coordination. However, inconsistencies in edge lengths within the graph can hinder optimal path calculation. To address this, a Graph Decomposition submodule was developed to standardize edge lengths and temporal costs. Integrated into a ROS-based fleet coordination system, this approach significantly reduces execution time and improves coordination capacity.
Multi-robot coordination aims to synchronize robots for optimized, collision-free paths in shared environments, addressing task allocation, collision avoidance, and path planning challenges. The Time Enhanced A* (TEA*...
ISBN:
(数字)9798331538606
ISBN:
(纸本)9798331538613
Multi-robot coordination aims to synchronize robots for optimized, collision-free paths in shared environments, addressing task allocation, collision avoidance, and path planning challenges. The Time Enhanced A* (TEA*) algorithm addresses multi-robot pathfinding offering a centralized and sequential approach. However, its sequential nature can lead to order-dependent variability in solutions. This study enhances TEA* through multi-threading, using thread pooling and parallelization techniques via OpenMP, and a sensitivity analysis enabling parallel exploration of robot-solving orders to improve robustness and the likelihood of finding efficient, feasible paths in complex environments. The results show that this approach improved coordination efficiency, reducing replanning needs and simulation time. Additionally, the sensitivity analysis assesses TEA*'s scalability across various graph sizes and number of robots, providing insights into how these factors influence the efficiency and performance of the algorithm.
In recent decades, the concept of sustainability has taken on a key role in the agendas of political decision-makers, businesses, and society. Brands have incorporated attributes associated with the concept of sustain...
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