A preliminary study about multi-robot task allocation with energy constraints

Fleets of mixed robots equipped with sensors offer a promising solution to enhance the monitoring of industrial and urban areas. However, the limited energy autonomy of these robots poses a challenge, requiring visits to charging stations as needed. In this paper a clustering methodology based on th...

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Published inIEEE International Conference on Automation Science and Engineering (CASE) Vol. 18; no. 5; pp. 3057 - 3062
Main Authors Baccouche, Chaima, Ammar, Imen Iben, Lefebvre, Dimitri, Telmoudi, Achraf Jabeur
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.01.2024
Institution of Engineering and Technology
Subjects
Automatic Control Engineering
Batteries
Charging stations
Clustering algorithms
Collision avoidance
Computer Science
k-means algorithm
Mixed-Integer Linear Programming
Multi-Robot Task Allocation
Resource management
Robot sensing systems
Sensors
Service robots
Traveling salesman problems
Urban areas
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Summary:Fleets of mixed robots equipped with sensors offer a promising solution to enhance the monitoring of industrial and urban areas. However, the limited energy autonomy of these robots poses a challenge, requiring visits to charging stations as needed. In this paper a clustering methodology based on the k-means algorithm is employed to determine the optimal charging station locations and to ensure uninterrupted operation for mobile robots. Moreover, a novel approach to Multi-Robot Task Allocation (MRTA) is presented, integrating aspects of the multi Traveling Salesman Problem (mTSP) and the Electric Vehicle Routing Problem (E-VRP). By formulating this problem as a Mixed-Integer Linear Programming (MILP) model and employing standard solvers, we address these challenges. Preliminary simulation results demonstrate the practical effectiveness of our methodology in task assignment, planning, and energy resource management, within industrial environments.
ISSN:1751-8644
2161-8089
DOI:10.1109/CASE59546.2024.10711402