This paper introduces a Model Predictive Control (MPC)-based approach for decentralized task allocation in fleets of mobile robots operating under industrial logistics constraints. The proposed framework optimally assigns tasks while accounting for external factors like operator requests. Each robot assesses its ability to perform a given task and resolves assignment conflicts through a structured decision-making process. The system is validated using ROS-based mobile robots in a laboratory setting that replicates an industrial application, showcasing the model’s adaptability and efficiency. The results highlight that constraint-based MPC improves the management and flexibility of multi-robot logistics operations in dynamic industrial environments.

Autonomous terrain leveling operations significantly impact project timelines, cost efficiency, and environmental sustainability within earthworks, which constitute a substantial portion of the public works market. This paper introduces the Optimal Transport Planner Framework(OTPF), an innovative framework for multi-robot systems designed to automate material transport and surface leveling tasks by leveraging the online Multi-Agent Pickup and Delivery (MAPD) problem paradigm. OTPF addresses critical operational layers, specifically an optimal transport (OT) layer for task modeling and a Task Allocation Layer employing heuristic and consensusbased algorithms for efficient agent coordination. To effectively manage dynamic task assignments and collision-free motion, we propose a hierarchical task allocation and motion planning approach. OTPF integrates strategies inspired by MAPD methodologies to dynamically assign robotic agents to tasks, balancing operational efficiency with conflict resolution.

This paper presents a distributed approach for Multi-Robot Task Allocation (MRTA). We consider assigning sequence of tasks to a group of robots with the aim of maximizing the total received reward while respecting resource constraints. For this, a distributed approach for multi-robot task allocation was developed modeling the problem as a Vehicle Routing Problem (VRP). The approach uses a combination of the Consensus Alternating Direction Method of Multipliers (C-ADMM) with Column Generation (CG) and branch-and-bound to arrive into final assignment through communicating a limited amount of information between the robots. The simulations performed demonstrate the efficiency of the approach by achieving a small relative error with respect to the centralized solution.

This paper presents a decentralised, bio-inspired approach task allocation for mobile robots in industrial logistics settings. Drawing on the collaborative and efficient behaviours observed in natural systems such as ant colonies, the study introduces a method whereby robots autonomously select and undertake tasks based on their individual capabilities. The central innovation of this work is the emphasis on consensus-building among the robots to resolve any conflicts that arise during task allocation. This is achieved through a mechanism that allows for decision-making, ensuring that the group of robots can adapt to changing circumstances and optimise task distribution. The practical applications of this research are demonstrated through simulations and real-world examples, showcasing significant improvements in operational efficiency and adaptability. This research marks a significant step forward in the quest for more autonomous, flexible, and efficient robotic systems in complex industrial environments, emphasising the power of consensus in achieving harmonious and effective collaboration.

This paper deals with the optimisation of a multi-robot inspection mission in an industrial area. We aim to solve a specific combinatorial optimisation problem where a team of sensing mobile robots must gather several measurement tasks distributed over the state space. Based on previous work where a Hybrid Filtered Beam Search (HFBS) approach solves task assignment and planning for this specific problem, we focus on the planning aspect to improve the computed solution. Therefore, this planning problem is modeled as a one-depot multiple Travelling Salesman Problem (mTSP). As the performance of HFBS depends on the challenging selection of its suitable parameters, and considering the coupling between task assignment and task planning problems, we propose a local search algorithm that improves the solution and deals with the optimality issue.

Spellchecking is one of the most fundamental and widely used search features. Correcting incorrectly spelled user queries not only enhances the user experience but is expected by the user. However, most widely available spellchecking solutions are either lower accuracy than state-of-the-art solutions or too slow to be used for search use cases where latency is a key requirement. Furthermore, most innovative recent architectures focus on English and are not trained in a multilingual fashion and are trained for spell correction in longer text, which is a different paradigm from spell correction for user queries, where context is sparse (most queries are 1-2 words long). Finally, since most enterprises have unique vocabularies such as product names, off-the-shelf spelling solutions fall short of users' needs.

This paper presents a decentralized method for aligning three robots without communication and without explicit localization. Each robot is assumed to be equipped with an omnidirectional camera and is assumed to be able to detect the two other robots in the images produced by its own camera. Each robot decides of its own movements based on the continuous measures of angles between itself and the others. We present both the algorithm that performs such a task and its theoretical analysis. We prove the correctness of the algorithm when the robots are reduced to points, when collisions are not considered and when the initial formation is not an equilateral triangle. When collisions are considered, the probability for the algorithm to be successful is greater than $1 - \displaystyle \frac {1}{36}$. We then present some simulation for which a security distance $d_{\sec }$ has been introduced. During the execution of the algorithm by each robot, if the distance between any two robots is lower than this security distance we suppose that a collision is likely to occur and consider that the Algorithm has failed. Simulation results report that the larger $d_{\sec }$, the bigger the percentage of failures. However runs are always successful when $d_{\sec } = 0$ which suggests that the theoretical bound is not tight and could be improved. In addition, simulations reveal that the main source of failures was not the one expected by the theoretical analysis.

This paper is devoted to multisource renewable energy systems. A modeling approach is proposed that brings a detailed understanding of the coupling and uncoupling of DC/DC power converters on a DC bus including the regulation of the DC bus voltage and the driving of current provided by each converter to the load. This approach is systematic and the resulting average state-space model depends only on the number and characteristics of the converters. The model has a generic expression the parameters of which switch according to the converters coupling and uncoupling on the DC bus. The model has been identified and validated with an experimental device developed by GREAH Research Group for the optimization of energies transfers for multisource renewable energy systems. Our approach can be used in the context of power management as support for performance evaluation (converter design, supervisory control design, and so on).