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13th International Conference on Computational Logistics (ICCL)

作者： Ye, Jinwen Pantuso, Giovanni Pisinger, David Univ Copenhagen DK-2100 Copenhagen Denmark Tech Univ Denmark DK-2800 Lyngby Denmark

ISBN: (纸本)9783031165795;9783031165788

The first-mile problem, which refers to the design of transport services that connect passengers to their nearby transit station, has attracted growing attention in recent years. In this paper we consider first-mile ride-sharing services and study the problem of optimally determining the fleet size and assigning vehicles to transport requests. We formulate the problem as a mixed-integer program and present a number of numerical experiments based on a small-scale system to analyse different configurations of the service, namely with and without fleet control (FC). Result shows that a configuration with FC is superior in terms of profits while service rates can be higher in a configuration without FC, depending on the revenue-sharing mechanism.

关键词： fleet control First-mile Ride-sharing

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Learning-Based Formation control of UAV-fleet 19

Learning-Based Formation Control of UAV-fleet

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19th International Conference on Network and Service Management (CNSM) - Network and Service Management in the Era of Generative AI and Digital Twins

作者： Abdulazeez, Abdulhakeem Zema, Nicola Roberto Ali-Yahiya, Tara Martin, Steven Univ Paris Saclay CNRS Lab Interdisciplinaire Sci Numer F-91190 Gif Sur Yvette France

ISBN: (纸本)9783903176591

In the context of wireless networked robotics, complex missions, such as autonomous Search and Rescue, may require the use of multiple Unmanned Aerial Vehicles (UAVs) to achieve higher efficiency and Quality of Service (QoS) while assuring mission-specific imperatives like the maximization of area covered in a single passage of the fleet over area subsections. In this paper, we present a learning-based formation control protocol that adapts the principle of Q-learning to pilot an autonomous fleet of networked UAVs to maintain formation throughout a mission where large quantities of data need to be exchanged. Also, the protocol tries to ensure rotational formation control by leveraging only the signal strength extrapolated from the UAV communications. A leader-follower model is used to control the fleet. One UAV serves as the leader, and the remaining as the followers. The followers use the Received Signal Strength Indicator (RSSI) values obtained from their neighbors to autonomously determine the leader's direction of movement and maintain formation orientation to avoid area coverage overlapping. We carried out several simulation experiments to evaluate the performance of the proposed scheme in terms of QoS and convergence time of the formation under varying velocities.

关键词： Wireless Networked Robots UAV formation control Q-learning fleet control

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Formation control Design for Remote Field Monitoring with IoT-enabled Mobile Robots 10

Formation Control Design for Remote Field Monitoring with Io...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Baucas, Marc Jayson Xu, Zhe Spachos, Petros Andrew Gadsden, S. University of Guelph School of Engineering GuelphON Canada McMaster University Department of Mechanical Engineering HamiltonON Canada

ISBN: (纸本)9798350373011

Incorporating technological advancements in the farming industry is a growing trend due to the increasing demand for production and resources in agricultural services. With the significant global population increasing yearly, farming services need improvements to cater to it. One approach the farming industry considers is utilizing the Internet of Things (IoT) networks and robotic control systems for better efficiency and sustainability. IoT technology, with its sensors and network devices, allows wireless communication for agricultural services to expand the coverage of their techniques and implement more convenient ways of data sensing and real-time analysis. Also, they introduce control systems and robotics to automate their tasks and procedures, making each service more sustainable and efficient. So, in this work, we take these two and propose an IoT-enabled formation control design to use the strengths of these technologies and present a viable design for more effective and sustainable remote field monitoring. With our IoT network arrangement with the cloud and its gateways, we present a means to expand the reach of the field monitoring service and its coverage to span wider farmlands. In addition, we performed simulation studies that demonstrate the feasibility of our proposed formation tracking control strategy with proven stability. Overall, we present a feasible approach for a more efficient and sustainable remote field monitoring system for farming services using a formation control design with IoTenabled mobile robots. © 2024 IEEE.

关键词： controller design fleet control Internet of robotic things Mobile field monitoring Path tracking Smart farming

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Decentralised Motion Planning for Autonomous Mining Haulage Trucks using Prioritised Multi-Agent MPPI 17

Decentralised Motion Planning for Autonomous Mining Haulage ...

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17th IFAC Symposium on control of Transportation Systems (CTS)

作者： Matsui, Kenta Adan, Fahmy Angeloudis, Panagiotis Imperial Coll London Transport Syst & Logist Lab Dept Civil & Environm Engn London SW7 2AZ England Komatsu Ltd 2-3-6AkasakaMinato ku Tokyo 107404 Japan

This paper introduces a decentralised motion planning method for Autonomous Haulage Systems (AHS) using Prioritised Multi-Agent Model Predictive Path Integral control (P-MA-MPPI). Our approach incorporates vehicle prioritisation, addressing a challenge that has previously limited decentralised MPPI frameworks, to enhance navigation safety and efficiency by exchanging future trajectories among agents. The experimental findings indicate a reduction in collision and deadlock rates when compared to an existing MPPI-based approach, alongside about a 20% increase in energy efficiency in complicated scenarios where three vehicles simultaneously enter an intersection. This research contributes to scalable and sustainable operation strategies, improving the efficiency and the environmental impact of future mine automation. Copyright (c) 2024 The Authors.

关键词： Transportation Systems fleet control Mining Autonomous Vehicles Decentralised Motion Planning

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Decentralised Motion Planning for Autonomous Mining Haulage Trucks using Prioritised Multi-Agent MPPI

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IFAC-PapersOnLine 2024年第10期58卷 154-161页

作者： Kenta Matsui Fahmy Adan Panagiotis Angeloudis Transport Systems and Logistics Laboratory Department of Civil and Environmental Engineering Imperial College London London SW7 2AZ U.K Komatsu Ltd. 2-3-6 Akasaka Minato-ku Tokyo 107-8414 Japan

关键词： Transportation Systems fleet control Mining Autonomous Vehicles Decentralised Motion Planning

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Evaluating prebooked on-demand mobility services using MATSim

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Procedia Computer Science 2024年 238卷 763-770页

作者： Sebastian Hörl Tarek Chouaki Oliver Ludwig Hannes Rewald Steffen Axer Institut de Recherche Technologique SystemX Palaiseau France Volkswagen AG Wolfsburg Germany

A common task in today’s transport simulation research is the assessment of on-demand mobility services on a large scale. A framework that is widely used for such tasks is MATSim, which has been applied frequently to study highly dynamic on-demand mobility services. To date, its efficient fleet management implementation does not yet support on-demand requests that are pre-booked in advance. This type of service, however, is seeing increased interest in the context of long-distance, rural, and inclusive mobility. Therefore, the present paper explores how prebooking can be integrated into MATSim and a use case for the city of Melun in France is presented.

关键词： on-demand mobility agent-based simulation prebooking dispatching fleet control MATSim

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Implementing reinforcement learning for on-demand vehicle rebalancing in MATSim

Implementing reinforcement learning for on-demand vehicle re...

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13th International Conference on Ambient Systems, Networks and Technologies, ANT 2022 / 5th International Conference on Emerging Data and Industry 4.0, EDI40 2022

作者： Chouaki, Tarek Hörl, Sebastian Puchinger, Jakob Université Paris-Saclay CentraleSupélec Laboratoire Génie Industriel Gif-sur-Yvette91190 France Institut de Recherche Technologique SystemX Palaiseau91120 France

In this paper, we present a software architecture that extends the MATSim mobility simulation framework by providing an external rebalancing server, which offers a set of rebalancing algorithms. This allows to implement complex rebalancing algorithms with minimal changes introduced into MATSim's code base. We are specifically interested in the potential of using algorithms based on reinforcement learning, we present and implement in our architecture a simple Q-learning rebalancing algorithm. Our initial results show the potential of this approach. Although they are not better than existing rebalancing algorithm in MATSim, this approach enables more developments in this area and the implementation of other more complex algorithms for mobility on-demand operation in MATSim. © 2022 Elsevier B.V.. All rights reserved.

关键词： fleet control MATSim mobility-on-demand reinforcement learning

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Integrating parcel deliveries into a ride-pooling service-An agent-based simulation study

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TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE 2023年第1期169卷

作者： Fehn, Fabian Engelhardt, Roman Dandl, Florian Bogenberger, Klaus Busch, Fritz Tech Univ Munich Chair Traff Engn & Control Arcisstr 21 D-80333 Munich Germany

This paper examines the integration of freight delivery into the passenger transport of an on-demand ride-pooling service. The goal of this research is to use existing passenger trips for logistics services and thus reduce additional vehicle kilometers for freight delivery and the total number of vehicles on the road network. This is achieved by merging the need for two separate fleets into a single one by combining the services. This research provides an extensive literature review and discusses policy measures supporting such a service. To evaluate the potential of such a mobility-on-demand service, this paper uses an agent-based simulation framework and integrates three heuristic parcel assignment strategies into a ride-pooling fleet control algorithm. Two integration scenarios (moderate and full) are set up. While in both scenarios passengers and parcels share rides in one vehicle, in the moderate scenario no stops for parcel pick-up and delivery are allowed during a passenger ride to decrease customer inconvenience. Using real-world demand data for a case study of Munich, Germany, the two integration scenarios together with the three assignment strategies are compared to the status quo, which uses two separate vehicle fleets for passenger and logistics transport. The results indicate that the integration of logistics services into a ride-pooling service is possible and can exploit unused system capacities without deteriorating passenger transportation. Depending on the assignment strategies nearly all parcels can be served until a parcel to passenger demand ratio of 1:10 while the overall fleet kilometers can be decreased compared to the status quo.

关键词： Integration of passengers and freight Mobility-on-demand Ride-pooling fleet control Parcel delivery Agent-based simulation City logistics

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A Cooperative Platooning controller for Connected Vehicles 7

A Cooperative Platooning Controller for Connected Vehicles

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7th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS)

作者： Bichiou, Youssef Rakha, Hesham Abdelghaffar, Hossam M. Virginia Tech Virginia Tech Transportat Inst Ctr Sustainable Mobil Blacksburg VA 24061 USA Virginia Tech Charles E Via Jr Dept Civil & Environm Engn Blacksburg VA 24061 USA Mansoura Univ Engn Fac Dept Comp Engn & Syst Mansoura 35516 Egypt

ISBN: (纸本)9789897585135

One of the key priorities of technologies is performance. In the area of transportation, performance is typically intertwined with increased mobility and reduced costs. Congestion alleviation which is a persistent challenge faced by many cities is a priority. The use of infrastructure is inherently inefficient, resulting in higher vehicle fuel consumption and pollution. This in turn burdens commuters and businesses. Therefore, solving this issue is of prime significance because of the potential benefit. Many technologies have been and are being developed. These include adaptive traffic signals and various dynamic traffic control strategies. This paper introduces a platooning controller that keeps relatively small time gaps between consecutive vehicles to increase mobility, and eventually reduce travel costs. This controller also accounts for complex dynamic and kinematic restrictions controlling vehicle motion. The controller is tested in a virtual environment on highways in downtown Los Angeles. A drop-in travel time, delay, fuel consumption was observed across the area for connected automated vehicles (CAVs) and non-connected vehicles, at various market penetration rates (MPRs). Reductions of up to 5%. 9.4%. and 8.17% in travel time, delay, and fuel consumption, respectively are observed. These observations are observed for all vehicles platooned and non-platooned.

关键词： Connected and Automated Vehicles Platooning fleet control

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Implementing reinforcement learning for on-demand vehicle rebalancing in MATSim

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Procedia Computer Science 2022年 201卷 134-141页

作者： Tarek Chouaki Sebastian Hörl Jakob Puchinger Université Paris-Saclay CentraleSupélec Laboratoire Génie Industriel 91190 Gif-sur-Yvette France Institut de Recherche Technologique SystemX Palaiseau 91120 France

In this paper, we present a software architecture that extends the MATSim mobility simulation framework by providing an external rebalancing server, which offers a set of rebalancing algorithms. This allows to implement complex rebalancing algorithms with minimal changes introduced into MATSim’s code base. We are specifically interested in the potential of using algorithms based on reinforcement learning, we present and implement in our architecture a simple Q-learning rebalancing algorithm. Our initial results show the potential of this approach. Although they are not better than existing rebalancing algorithm in MATSim, this approach enables more developments in this area and the implementation of other more complex algorithms for mobility on-demand operation in MATSim.

关键词： MATSim mobility-on-demand reinforcement learning fleet control

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