The synergic orchestration of the cognitive and psychological dimensions characterizes human intelligence. Accordingly, carefully designing this mechanism in artificial intelligence can be a successful strategy to inc...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
The synergic orchestration of the cognitive and psychological dimensions characterizes human intelligence. Accordingly, carefully designing this mechanism in artificial intelligence can be a successful strategy to increase human likeness in a robot, enhancing mutual understanding and building a more natural and intuitive interaction. For this purpose, the main contribution of this work is a psychological and cognitive architecture tailored for HRI based on the interplay between robotic personality and memory-based cognitive processes. Indeed, the artificial personality manifests itself not only in various aspects of the behavior but also within the action selection process, which is closely intertwined with personality-dependent hedonic experiences linked to memories. Within this paper, we propose a task- and platform-independent framework, evaluated in a multiparty collaborative scenario. Obtained results show that a robot connected to our proposed framework is perceived as a cognitive agent capable of manifesting perceivable and distinguishable personality traits.
As collaborative robots become more common in manufacturing scenarios and adopted in hybrid human-robot teams, we should develop new interaction and communication strategies to ensure smooth collaboration between agen...
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ISBN:
(数字)9798350375022
ISBN:
(纸本)9798350375039
As collaborative robots become more common in manufacturing scenarios and adopted in hybrid human-robot teams, we should develop new interaction and communication strategies to ensure smooth collaboration between agents. In this paper, we propose a novel communicative interface that uses Mixed Reality as a medium to perform Kinesthetic Teaching (KT) on any robotic platform. We evaluate our proposed approach in a user study involving multiple subjects and two different robots, comparing traditional physical KT with holographic-based KT through user experience questionnaires and task-related metrics.
Recent advancements in naval technology have introduced agile and stealthy anti-ship drones. Characterized by rapid movements and compact above-water hulls, these drones often exhibit small radar cross sections (RCS),...
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Roundabout entry is a complex manoeuvre that must be efficiently managed to prevent traffic congestion, particularly when multiple vehicles approach the roundabout simultaneously. This paper proposes a distributed pre...
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ISBN:
(数字)9798350358513
ISBN:
(纸本)9798350358520
Roundabout entry is a complex manoeuvre that must be efficiently managed to prevent traffic congestion, particularly when multiple vehicles approach the roundabout simultaneously. This paper proposes a distributed predictive control employing the Alternating Direction Method of Multipliers (ADMM) to form a virtual platoon and to determine the roundabout entry order, even in the presence of Human-Driven Vehicles (HDVs). The integration of HDVs introduces complexities in the dynamics of information exchange among the various elements that make up the platoon. To comprehensively assess the efficacy of the proposed control methodology, two distinct case studies are conducted, each exploring scenarios wherein an HDV occupies a different position within the virtual platoon, allowing for an evaluation of the impact of HDV position on platoon string stability. Results confirm that the controller is able to handle the insertion of all elements approaching the roundabout safely and efficiently.
The Dangerous Goods Transportation (DGT) presents several challenges, requiring a comprehensive risk assessment to ensure the safety and efficiency of the transport process. Dangerous goods risk assessment involves th...
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This paper presents the development and implementation of a novel meta-heuristic algorithm for fuel distribution in a leading energy company. The algorithm addresses challenges such as the multi-depot problem and clus...
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ISBN:
(数字)9798350358513
ISBN:
(纸本)9798350358520
This paper presents the development and implementation of a novel meta-heuristic algorithm for fuel distribution in a leading energy company. The algorithm addresses challenges such as the multi-depot problem and clustering scenarios to optimize fuel distribution efficiency. The preliminary phase of the study focuses on comparing the performance of the heuristic algorithm traditionally employed by the company and the novel simulated annealing meta-heuristic to solve the Vehicle Routing Problem (VRP). The findings reveal substantial enhancements offered by the new algorithm compared to its predecessor, aligning with company expectations and enhancing operational efficiency.
This paper addresses an open-shop scheduling problem within a manufacturing system modeled using Mixed Integer Linear Programming, which accounts for the progressive wear of machinery after job processing. The open-sh...
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ISBN:
(数字)9798350365917
ISBN:
(纸本)9798350365924
This paper addresses an open-shop scheduling problem within a manufacturing system modeled using Mixed Integer Linear Programming, which accounts for the progressive wear of machinery after job processing. The open-shop problem is transformed into a flow-shop problem, involving the enumeration of all possible job paths and the selection of paths that minimize the makespan. The research provides a comparison among an offline algorithm, unable to perform machinery maintenance and disregarding increases in processing times; an offline algorithm subject to machinery maintenance constraints after processing times exceed a certain threshold; and a dynamic algorithm utilizing Model Predictive Control concepts to reroute jobs at each relevant event, including unexpected job arrivals, as well as the initiation and completion of machine maintenance. The dynamic algorithm also considers the potential unavailability of machinery due to maintenance and deterioration. Its effectiveness is assessed through a simulated case study of a flexible manufacturing system, revealing a notable improvement in makespan – approximately a 30% reduction compared to the offline scenario without maintenance. This showcases its potential for real-world applications in the manufacturing industry.
This paper focuses on a network framework where a team of elements endeavors to traverse from an origin to a destination, aiming to minimize the maximum risk along their path. Within this network, each link is charact...
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ISBN:
(数字)9798350358513
ISBN:
(纸本)9798350358520
This paper focuses on a network framework where a team of elements endeavors to traverse from an origin to a destination, aiming to minimize the maximum risk along their path. Within this network, each link is characterized by a specific exposure to risk, with the potential for risk amplification when encountering another team of diverse elements seeking to inflict damage on the same link. The study employs the method of successive averages to optimize the path choice, emphasizing risk mitigation and control strategies. Results are demonstrated on an exemplificative network, showcasing the convergence of the chosen path through the iterative application of the method of successive averages. This approach provides valuable insights into navigating complex networks while considering dynamic risk factors, offering practical implications for system-of-systemsengineering and risk-aware decision-making.
The behaviour of drivers is significantly influenced by their perception of risk, which can have a profound impact on the transportation environment. This can potentially undermine road safety and efficiency. This stu...
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ISBN:
(数字)9798350365917
ISBN:
(纸本)9798350365924
The behaviour of drivers is significantly influenced by their perception of risk, which can have a profound impact on the transportation environment. This can potentially undermine road safety and efficiency. This study addresses this crucial concern by introducing an algorithm that forecasts driver-perceived risk using data obtained from electroencephalogram (EEG). The algorithm employs a Support Vector Machine (SVM) to develop a strong and predictive model that can forecast perceived risk levels. This model can then be used to inform the implementation of preventive safety measures. The efficacy of the algorithm was evaluated through the use of driving simulations, which involved three participants utilising the SCANeR Studio driving simulator. The simulations involved traversing a two-lane roundabout filled with vehicles and allowed the participants to make decisions during the entry and navigation stages. The results demonstrated the effectiveness of this approach even with a limited dataset with respect to a Pattern Recognition Neural Network (PRNN). This research offers valuable insights into the potential for neurobiological data-driven strategies to enhance driver safety.
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