Collision-free allocation of temporally constrained tasks in multi-robot systems

D’Emidio, Mattia; Khan, Imran

doi:10.1016/j.robot.2019.07.002

Cited by 16 publications

(4 citation statements)

References 19 publications

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“…We have showcased implementation, deployment, and testing efforts related the algorithmic components of the mentioned realworld system that takes care of computing solutions to instances of the pickup and delivery multi-vehicle problem with time windows that arise in such system. Concerning possible future investigation following this work, perhaps the most interesting one is that dedicated to adapting the system to handle entirely dynamic scenarios and to exploit cooperation among vehicles to perform more refined strategies of reoptimization (see, e.g., policies for task re-allocation that have been studied for systems of agents [2]).…”

Section: Discussionmentioning

confidence: 99%

“…For this variant, they provided 6 + 𝜀 and 3 approximation algorithms on graphs and trees respectively. In the case when all time windows have length in [1,2], Frederickson and Wittman [23] showed that OPTW admits a 10-approximation algorithm.…”

Section: Maximum Prize Problemsmentioning

confidence: 99%

“…Such vehicles are integrated and interact with IoT devices, computational units, data centers, satellites, and various sensing technologies to form a heterogeneous and distributed infrastructure, unifying hardware, networks, and software. The synergy between the fleet and the infrastructure forms a cohesive system designed to let vehicles perform complex tasks, often requiring coordination (e.g., pickup or delivery operations, search and rescue activities, traffic engineering [2,3]), while achieving the superior efficiency, safety, and adaptability of transportation within diverse and dynamic environments.…”

Section: Introductionmentioning

confidence: 99%

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Route Planning Algorithms for Fleets of Connected Vehicles: State of the Art, Implementation, and Deployment

D’Emidio,

Delfaraz,

Di Stefano

et al. 2024

Applied Sciences

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The introduction of 5G technologies has enabled the possibility of designing and building several new classes of networked information systems that were previously impossible to implement due to limitations on data throughput or the reliability of transmission channels. Among them, one of the most interesting and successful examples with a highly positive impact in terms of the quality of urban environments and societal and economical welfare is a system of semi-autonomous connected vehicles, where IoT devices, data centers, and fleets of smart vehicles equipped with communication and computational resources are combined into a heterogeneous and distributed infrastructure, unifying hardware, networks, and software. In order to efficiently provide various services (e.g., patrolling, pickup and delivery, monitoring), these systems typically rely on collecting and broadcasting large amounts of data (e.g., sensor data, GPS traces, or maps), which need to be properly collected and processed in a timely manner. As is well documented in the literature, one of the most effective ways to achieve this purpose, especially in a real-time context, is to adopt a graph model of the data (e.g., to model communication networks, roads, or interactions between vehicles) and to employ suitable graph algorithms to solve properly defined computational problems of interest (e.g., shortest paths or distributed consensus). While research in this context has been extensive from a theoretical perspective, works that have focused on the implementation, deployment, and evaluation of the practical performance of graph algorithms for real-world systems of autonomous vehicles have been much rarer. In this paper, we present a study of this kind. Specifically, we first describe the main features of a real-world information system employing semi-autonomous connected vehicles that is currently being tested in the city of L’Aquila (Italy). Then, we present an overview of the computational challenges arising in the considered application domain and provide a systematic survey of known algorithmic results for one of the most relevant classes of computational problems that have to be addressed in said domain, namely, pickup and delivery problems. Finally, we discuss implementation issues, adopted software tools, and the deployment and testing phases concerning one of the algorithmic components of the mentioned real-world system dedicated to handling a specific problem of the above class, namely, the pickup and delivery multi-vehicle problem with time windows.

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Section: Discussionmentioning

confidence: 99%

Section: Maximum Prize Problemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Route Planning Algorithms for Fleets of Connected Vehicles: State of the Art, Implementation, and Deployment

D’Emidio,

Delfaraz,

Di Stefano

et al. 2024

Applied Sciences

Self Cite

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“…The multi-robot system (MRS) is one of the most extensive research areas in robotics, which deploys multiple collaborative robots in a coordinated manner to perform and complete tasks [1,2]. It has promising applications in rescue search, resource allocation, intelligent warehouse [3], and other fields. The key to multi-robot systems is rational task assignment to individuals in the system [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Robot Task Assignment Scheme Based on a Multi-Angle K-Means Clustering Algorithm and a Two-Stage Load-Balancing Strategy

You,

Jia,

Pang

et al. 2023

Electronics

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A practical task assignment is one of the core issues of a multi-robot system. In this paper, a multi-robot task assignment strategy based on load balancing is proposed to effectively balance and plan out the execution cost of each robot when it has a large number of working task points. Considering the variability of the execution task cost in practical situations with different task point categories, the multi-robot task assignment (MRTA) problem is transformed into a multiple traveling salesman problem (MTSP) using a multi-angle K-means clustering algorithm. To solve the problem of unbalanced cost consumed by each robot after clustering assignment, which leads to low efficiency of system completion, a two-stage load-balancing strategy is presented. The first stage of this strategy makes a large adjustment to the unbalanced task set, and the second stage achieves a small fine-tuning to the unbalanced task set. The experimental results show that the standard deviation of the cost ratio of each set decreases when four robots perform the task between 100 and 550 work points using the load-balancing strategy. The reduction in standard deviation is between 3.53% and 83.44%. The maximum cost of individual robots decreases between 0.18% and 14.27%. The proposed method can effectively solve the uneven execution cost of each robot in the task assignment process and improve the efficiency of the system in completing tasks.

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Strategic Decisions in Mobile Robot Automation

Yildirim

Reefke²,

Aktaş³

2023

Mobile Robot Automation in Warehouses

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Collision-free allocation of temporally constrained tasks in multi-robot systems

Cited by 16 publications

References 19 publications

Route Planning Algorithms for Fleets of Connected Vehicles: State of the Art, Implementation, and Deployment

Route Planning Algorithms for Fleets of Connected Vehicles: State of the Art, Implementation, and Deployment

A Novel Multi-Robot Task Assignment Scheme Based on a Multi-Angle K-Means Clustering Algorithm and a Two-Stage Load-Balancing Strategy

Strategic Decisions in Mobile Robot Automation

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