Decentralised Multi-Robot Systems Towards Coordination in Real World Settings

In recent years, Multi-Robot Systems (MRS) have gained significant interest in research and in industry (Khandelwal and Stone, 2017; E. Schneider et al., 2016; Amato et al., 2015; Alonso-Mora et al., 2015b; Enright and Wurman, 2011). Manufacturers are moving away from large one-size-fits-all productions to more customisable on demand production, which result in smaller and smaller batch sizes. Additionally, in order to be able to increase productivity even further, more and more tasks in the production process have to be automated. To accommodate these changes, industry is facing major shifts in how the products are produced and in particular the role robotic platforms are playing. Previously, robots have mainly been used in a static manner, i.e. performing a singular repetitive task over and over again with high precision and speed. When multiple robots are employed in such a setup, each robot performs a dedicated task, with no interaction with the other robots. While this approach was suitable for large-scale productions, it cannot maintain the same productivity for highly customisable products. Additionally, many tasks in the production process require that the robots are mobile, since they are spatially distributed. One example is for instance retrieving items from different locations in a warehouse. Furthermore, another requirement is that every robot should be able to handle many different tasks and more importantly, many robots should work together in a team towards a common goal. These new requirements introduce various new challenges. As an example, since the robots are mobile, they should be able to perform the tasks alongside the human workers. Likewise, since multiple robots have to work together, a new challenge is to coordinate such MRS. The work presented in this thesis focuses on the core issues when deploying MRS in the physical world. We focus on the task of warehouse commissioning as a running example. The environment for this task is highly dynamic, adaptive and complex, since new orders can appear at any time and priorities might change. A major issue is to coordinate the robots, while taking current and possible future tasks into account. One solution is a centralised planning entity, which knows about all tasks and robots in the team and assigns the tasks accordingly. While in the case of a handful robots, a good assignment can usually be calculated in a straight forward manner, a problem with a centralised system arises when more and more robots are added to the system. The number of possible assignments rises exponentially with every additional robot. Thus, planning times increase and it might become infeasible to provide an optimal plan in time or to respond quickly to changes. On the other hand, in a decentralised solution, each robot decides on its own. Thus, it accumulates all necessary information, and calculates a plan based on this information. While the robots might not have all information available, this is in many cases not necessary. The planning robot is mainly interested in its own actions. While the robot should take the other robots into account, this effect can be approximated, and not every single action of the other robots is needed. This results in a much less complex planning problem, which allows the robot to re-plan online, as soon as the environment changes. In this thesis, we focus on such decentralised solutions for MRS that can run online on the robots. We investigate navigation, decision making and planning algorithms that are suitable for problems in which the tasks are highly dynamic and spatially distributed, such as the warehouse commissioning example. We explore how a team of robots can navigate safely in a shared environment with humans. We apply Monte Carlo sampling techniques and trajectory rollouts as used in the commonly used Dynamic Window Approach (DWA) (Fox et al., 1997), while taking the localisation uncertainty into account. We show that our resulting navigation method is robust and able to run decentralised on the robots. To facilitate formal evaluation of planning and decision making algorithms, a formal framework called Spatial Task Allocation Problems (SPATAPs) is introduced, that enables us to capture and analyse these problems in the well known Markov Decision Process (MDP) (Puterman, 1994) and Multi-Agent Markov Decision Process (MMDP) (Boutilier, 1996) frameworks. The commonly used MDP solution methods, i.e. value iteration and dynamic programming, fail to provide a solution, due to the large problem space. We investigate whether we can exploit the structure of these problems and introduce approximations to enable planning using the common solution methods. We further refine the framework to formally capture the warehouse commissioning task. A solution method based on Monte Carlo Tree Search (MCTS) (Kocsis and Szepesvári, 2006) is introduced, using computationally cheap greedy roll-out strategies. We show that the resulting approach can yield significantly higher performance than previous approaches, while still being able to plan within the magnitude of seconds, which allows for online re-planning on the robots. Finally, the decision making algorithm and the navigation approach are combined in a proof-of-concept application, in which three youBots are used in a physical warehouse commissioning setup

Formal Modelling for Multi-Robot Systems Under Uncertainty

Purpose of Review: To effectively synthesise and analyse multi-robot behaviour, we require formal task-level models which accurately capture multi-robot execution. In this paper, we review modelling formalisms for multi-robot systems under uncertainty, and discuss how they can be used for planning, reinforcement learning, model checking, and simulation. Recent Findings: Recent work has investigated models which more accurately capture multi-robot execution by considering different forms of uncertainty, such as temporal uncertainty and partial observability, and modelling the effects of robot interactions on action execution. Other strands of work have presented approaches for reducing the size of multi-robot models to admit more efficient solution methods. This can be achieved by decoupling the robots under independence assumptions, or reasoning over higher level macro actions. Summary: Existing multi-robot models demonstrate a trade off between accurately capturing robot dependencies and uncertainty, and being small enough to tractably solve real world problems. Therefore, future research should exploit realistic assumptions over multi-robot behaviour to develop smaller models which retain accurate representations of uncertainty and robot interactions; and exploit the structure of multi-robot problems, such as factored state spaces, to develop scalable solution methods.Comment: 23 pages, 0 figures, 2 tables. Current Robotics Reports (2023). This version of the article has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://dx.doi.org/10.1007/s43154-023-00104-

Criterios para la planeación de centros de distribución. Revisión bibliométrica

This paper documents the main criteria that have been included for Distribution Centers planning and sizing, through a systematic review; based on the research articles in the SCOPUS bibliographic database. The results obtained show five trends. -The inclusion of automated robots and intelligent forklifts, -the multi-objective distribution centers, -lot sizing, -distribution design or layout, and -planning under uncertainty scenarios. Based on these, important decisions can be made to improve logistics processes, the distribution chain, and the organization’s profitability.Este artículo documenta los principales criterios que se han incluido para la planificación y dimensionamiento de Centros de Distribución, mediante una revisión sistemática; a partir de las publicaciones encontradas en la base de datos bibliográfica SCOPUS. Los resultados obtenidos arrojaron cinco tendencias: -La inclusión de robots automatizados y de montacargas inteligentes, -centros de distribución multiobjetivo, -diseño de la distribución o layout, -el dimensionamiento de lotes y -la planificación bajo escenarios de incertidumbre. Con base en estos, se pueden tomar decisiones importantes para la mejora de los procesos logísticos, la cadena de distribución y la rentabilidad de la organización

A distributed framework for the control and cooperation of heterogeneous mobile robots in smart factories.

Doctoral Degree. University of KwaZulu-Natal, Durban.The present consumer market is driven by the mass customisation of products. Manufacturers are now challenged with the problem of not being able to capture market share and gain higher profits by producing large volumes of the same product to a mass market. Some businesses have implemented mass customisation manufacturing (MCM) techniques as a solution to this problem, where customised products are produced rapidly while keeping the costs at a mass production level. In addition to this, the arrival of the fourth industrial revolution (Industry 4.0) enables the possibility of establishing the decentralised intelligence of embedded devices to detect and respond to real-time variations in the MCM factory. One of the key pillars in the Industry 4.0, smart factory concept is Advanced Robotics. This includes cooperation and control within multiple heterogeneous robot networks, which increases flexibility in the smart factory and enables the ability to rapidly reconfigure systems to adapt to variations in consumer product demand. Another benefit in these systems is the reduction of production bottleneck conditions where robot services must be coordinated efficiently so that high levels of productivity are maintained. This study focuses on the research, design and development of a distributed framework that would aid researchers in implementing algorithms for controlling the task goals of heterogeneous mobile robots, to achieve robot cooperation and reduce bottlenecks in a production environment. The framework can be used as a toolkit by the end-user for developing advanced algorithms that can be simulated before being deployed in an actual system, thereby fast prototyping the system integration process. Keywords: Cooperation, heterogeneity, multiple mobile robots, Industry 4.0, smart factory, manufacturing, middleware, ROS, OPC, framework

A framework to offer high value manufacturing through self-reconfigurable manufacturing systems

The High Value Manufacturing (HVM) sector is vital for developed countries due to the creation of innovative products with advanced technology that cannot be reproduced at the same cost and time with traditional technology. The main challenge for HVM is to rapidly increase production volume from one-off products to low production volume. This requires highly flexible manufacturing systems that can produce new products at variable production volumes. Current manufacturing systems, classified as dedicated, flexible and reconfigurable systems, are limited to produce one type of product(s), within a production volume range and have fixed layouts of machines. Thus, there is a need for highly flexible systems that can rapidly adjust their production volume according to the production demand (i.e. main HVM challenge). Therefore, a novel manufacturing framework, called INTelligent REconfiguration for a raPID production change (INTREPID), is presented in this thesis. INTREPID consists of a user interface and communications platform, a job allocation system, a globally distributed network of Reconfigurable Manufacturing Centres (RMCs), consisting of interconnected factories, and Self-Reconfigurable Manufacturing Systems (S-RMSs). The highly flexible S-RMS consists of movable machines and Mobile Manufacturing Robots (MMRs). The novelty of the S-RMS is its capability of forming layouts bespoke to the current production needs. The vision of INTREPID is to offer global HVM services through the network of RMCs. The job allocation system determines the best possible RMCs or factories to perform a job by considering the complexity of the production requirements and the status of the available S-RMSs at each factory. The planning of the production with S-RMS is challenging due to its high flexibility. The main example of this flexibility is the possibility to create layouts bespoke to current production needs. Yet, this flexibility involves the challenges of determining allocations and schedules of tasks to robots and machines, positions to manufacture, and routes to reach those positions. In manufacturing systems with fixed layouts, production plans are determined by solving a sequence of problems. However, for the S-RMS, it is proposed to determine production plans with a single problem that covers the scheduling, machine layout and vehicle routing problems simultaneously. This novel problem is called the Scheduling, positions Assigning and Routing problem (SAR) problem. In order to determine the best possible production plan(s) for the S-RMS, it is necessary to use optimisation methods. Dozens of elements, characteristics and assumptions from the constituent problems might be included in the formulation of the SAR problem. Elements, characteristics and assumptions can be considered as decision variables on whether to include or not the elements and characteristics and under which assumptions in the formulation. There are two types of decision variables. Fundamental variables are natural to the SAR problem (e.g. manufacturing resources, factory design and operation), whilst auxiliary variables arise from the aim to simplify the formulation of the optimisation problem (i.e. time formulated as discrete or continuous). Due to the large number of decision variables, there might be millions of possible ways to formulate the SAR problem (i.e. the SAR problem space). Some of these variants are intractable to be solved with optimisation methods. Hence, before formulating the SAR problem, it is necessary to select a problem(s) that is realistic to industrial scenarios but solvable with optimisation methods. Existing selection methods work with pairwise comparisons of alternatives. However, for a space of millions of SAR problems, pairwise comparisons are intractable. Hence, in this thesis, a novel Decision Making Methodology (DMM) based on the controlled convergence method is presented. The DMM helps down-selecting one or a few SAR problems from millions of possible SAR problems. The DMM is demonstrated with a case study of the SAR problem and the results show a significant reduction of the reviewed SAR problems and the time to select them

Background, Systematic Review, Challenges and Outlook

Publisher Copyright: © 2013 IEEE. This research is supported by the Digital Manufacturing and Design Training Network (DiManD) project funded by the European Union through the Marie Skłodowska-Curie Innovative Training Networks (H2020-MSCA-ITN-2018) under grant agreement no. 814078The concept of smart manufacturing has attracted huge attention in the last years as an answer to the increasing complexity, heterogeneity, and dynamism of manufacturing ecosystems. This vision embraces the notion of autonomous and self-organized elements, capable of self-management and self-decision-making under a context-aware and intelligent infrastructure. While dealing with dynamic and uncertain environments, these solutions are also contributing to generating social impact and introducing sustainability into the industrial equation thanks to the development of task-specific resources that can be easily adapted, re-used, and shared. A lot of research under the context of self-organization in smart manufacturing has been produced in the last decade considering different methodologies and developed under different contexts. Most of these works are still in the conceptual or experimental stage and have been developed under different application scenarios. Thus, it is necessary to evaluate their design principles and potentiate their results. The objective of this paper is threefold. First, to introduce the main ideas behind self-organization in smart manufacturing. Then, through a systematic literature review, describe the current status in terms of technological and implementation details, mechanisms used, and some of the potential future research directions. Finally, the presentation of an outlook that summarizes the main results of this work and their interrelation to facilitate the development of self-organized manufacturing solutions. By providing a holistic overview of the field, we expect that this work can be used by academics and practitioners as a guide to generate awareness of possible requirements, industrial challenges, and opportunities that future self-organizing solutions can have towards a smart manufacturing transition.publishersversionpublishe