As industrial environments increasingly rely on fleets of autonomous robots to transport goods and materials, efficiently allocating tasks while managing energy consumption has become a critical challenge for maintaining productivity.
Today, we proudly highlight the work of 𝘿𝙧. 𝘼𝙡𝙞 𝘿𝙟𝙚𝙣𝙖𝙙𝙞, whose scientific publication in Expert Systems with Applications contributes to advancing task allocation strategies for industrial multi-robot systems.
Co-authored with 𝘿𝙧. 𝙈𝙤𝙝𝙖𝙢𝙚𝙙 𝙀𝙨𝙨𝙖𝙞𝙙 𝙆𝙝𝙖𝙣𝙤𝙪𝙘he, also a teacher at ESTIN and Boubekeur Mendil, the study entitled “A Lexicographic Optimization-Based Approach for Efficient Task Allocation in Industrial Transportation Multi-Robot Systems” addresses the challenge of maximizing the number of transportation tasks completed while operating under energy constraints.
The research introduces the Lexicographic Optimization-based Multi-Robot Task Allocation (LO-MRTA) approach, a framework designed to improve productivity while limiting overall energy consumption. Unlike approaches that focus on energy management during task execution, the proposed method incorporates energy considerations directly into the task allocation process.
The main idea behind the approach is to consider the global state of the robots before task execution, enabling more informed allocation decisions that account for both task priorities and available energy resources.
To address the potentially conflicting objectives of maximizing productivity and minimizing energy consumption, the study models the task allocation problem as a multi-objective optimization problem and applies a lexicographic optimization method to solve it.
According to the evaluation scenarios presented in the study, the proposed approach demonstrated promising performance when compared with three existing baseline methods in terms of system productivity, overall energy consumption, and workload scaling effects.
This contribution reflects ongoing research efforts in optimization and intelligent systems, addressing challenges associated with resource allocation and energy management in industrial multi-robot environments.
Through achievements such as this, ESTIN continues to foster research that contributes to the advancement of intelligent and efficient robotic systems.