Towards Autonomous Task Allocation Using a Robot Team in a Food Factory

Scheduling of hygiene tasks in a food production environment is a complex challenge which is typically performed manually. Many factors must be considered during scheduling; this includes what training a hygiene operative (i.e. cleaning staff member) has undergone, the availability of hygiene operatives (holiday commitments, sick leave etc.) and the production constraints (how long does the oven take to cool, when does production begin again etc.). This paper seeks to apply multiagent task allocation (MATA) to automate and optimise the process of allocating tasks to hygiene operatives. The intention is that this optimization module will form one part of a proposed larger system. that we propose to develop. A simulation has been created to function as a digital twin of a factory environment, allowing us to evaluate experimentally a variety of task allocation methodologies. Trialled methods include Round Robin (RR), Sequential Single Item (SSI) auctions, Lowest Bid and Least Contested Bid

Towards the application of multi-agent task allocation to hygiene tasks in the food production industry.

The food production industry faces the complex challenge of scheduling both production and hygiene tasks. These tasks are typically scheduled manually. However, due to the increasing costs of raw materials and the regulations factories must adhere to, inefficiencies can be costly. This paper presents the initial findings of a survey, conducted to learn more about the hygiene tasks within the industry and to inform research on how multi-agent task allocation (MATA) methodologies could automate and improve the scheduling of hygiene tasks. A simulation of a heterogeneous human workforce within a factory environment is presented. This work evaluates experimentally different strategies for applying market-based mechanisms, in particular Sequential Single Item (SSI) auctions, to the problem of allocation hygiene tasks to a heterogeneous workforce

Multi-Agent Task Allocation in Complementary Teams: A Hunter and Gatherer Approach

Consider a dynamic task allocation problem, where tasks are unknowingly distributed over an environment. This paper considers each task comprised of two sequential subtasks: detection and completion, where each subtask can only be carried out by a certain type of agent. We address this problem using a novel nature-inspired approach called "hunter and gatherer". The proposed method employs two complementary teams of agents: one agile in detecting (hunters) and another skillful in completing (gatherers) the tasks. To minimize the collective cost of task accomplishments in a distributed manner, a game-theoretic solution is introduced to couple agents from complementary teams. We utilize market-based negotiation models to develop incentive-based decision-making algorithms relying on innovative notions of "certainty and uncertainty profit margins". The simulation results demonstrate that employing two complementary teams of hunters and gatherers can effectually improve the number of tasks completed by agents compared to conventional methods, while the collective cost of accomplishments is minimized. In addition, the stability and efficacy of the proposed solutions are studied using Nash equilibrium analysis and statistical analysis respectively. It is also numerically shown that the proposed solutions function fairly, i.e. for each type of agent, the overall workload is distributed equally.Comment: 15 pages, 12 figure

A Distributed Version of the Hungarian Method for Multi-Robot Assignment

In this paper, we propose a distributed version of the Hungarian Method to solve the well known assignment problem. In the context of multi-robot applications, all robots cooperatively compute a common assignment that optimizes a given global criterion (e.g. the total distance traveled) within a finite set of local computations and communications over a peer-to-peer network. As a motivating application, we consider a class of multi-robot routing problems with "spatio-temporal" constraints, i.e. spatial targets that require servicing at particular time instants. As a means of demonstrating the theory developed in this paper, the robots cooperatively find online, suboptimal routes by applying an iterative version of the proposed algorithm, in a distributed and dynamic setting. As a concrete experimental test-bed, we provide an interactive "multi-robot orchestral" framework in which a team of robots cooperatively plays a piece of music on a so-called orchestral floor

Task Allocation and Collaborative Localisation in Multi-Robot Systems

To utilise multiple robots, it is fundamental to know what they should do, called task allocation, and to know where the robots are, called localisation. The order that tasks are completed in is often important, and makes task allocation difficult to solve (40 tasks have 1047 different ways of completing them). Algorithms in literature range from fast methods that provide reasonable allocations, to slower methods that can provide optimal allocations. These algorithms work well for systems with identical robots, but do not utilise robot differences for superior allocations when robots are non-identical. They also can not be applied to robots that can use different tools, where they must consider which tools to use for each task. Robot localisation is performed using sensors which are often assumed to always be available. This is not the case in GPS-denied environments such as tunnels, or on long-range missions where replacement sensors are not readily available. A promising method to overcome this is collaborative localisation, where robots observe one another to improve their location estimates. There has been little research on what robot properties make collaborative localisation most effective, or how to tune systems to make it as accurate as possible. Most task allocation algorithms do not consider localisation as part of the allocation process. If task allocation algorithms limited inter-robot distance, collaborative localisation can be performed during task completion. Such an algorithm could equally be used to ensure robots are within communication distance, and to quickly detect when a robot fails. While some algorithms for this exist in literature, they provide a weak guarantee of inter-robot distance, which is undesirable when applied to real robots. The aim of this thesis is to improve upon task allocation algorithms by increasing task allocation speed and efficiency, and supporting robot tool changes. Collaborative localisation parameters are analysed, and a task allocation algorithm that enables collaborative localisation on real robots is developed. This thesis includes a compendium of journal articles written by the author. The four articles forming the main body of the thesis discuss the multi-robot task allocation and localisation research during the author’s candidature. Two appendices are included, representing conference articles written by the author that directly relate to the thesis.Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 201

On the role and opportunities in teamwork design for advanced multi-robot search systems

Intelligent robotic systems are becoming ever more present in our lives across a multitude of domains such as industry, transportation, agriculture, security, healthcare and even education. Such systems enable humans to focus on the interesting and sophisticated tasks while robots accomplish tasks that are either too tedious, routine or potentially dangerous for humans to do. Recent advances in perception technologies and accompanying hardware, mainly attributed to rapid advancements in the deep-learning ecosystem, enable the deployment of robotic systems equipped with onboard sensors as well as the computational power to perform autonomous reasoning and decision making online. While there has been significant progress in expanding the capabilities of single and multi-robot systems during the last decades across a multitude of domains and applications, there are still many promising areas for research that can advance the state of cooperative searching systems that employ multiple robots. In this article, several prospective avenues of research in teamwork cooperation with considerable potential for advancement of multi-robot search systems will be visited and discussed. In previous works we have shown that multi-agent search tasks can greatly benefit from intelligent cooperation between team members and can achieve performance close to the theoretical optimum. The techniques applied can be used in a variety of domains including planning against adversarial opponents, control of forest fires and coordinating search-and-rescue missions. The state-of-the-art on methods of multi-robot search across several selected domains of application is explained, highlighting the pros and cons of each method, providing an up-to-date view on the current state of the domains and their future challenges

Multilayered skill learning and movement coordination for autonomous robotic agents

With advances in technology expanding the capabilities of robots, while at the same time making robots cheaper to manufacture, robots are rapidly becoming more prevalent in both industrial and domestic settings. An increase in the number of robots, and the likely subsequent decrease in the ratio of people currently trained to directly control the robots, engenders a need for robots to be able to act autonomously. Larger numbers of robots present together provide new challenges and opportunities for developing complex autonomous robot behaviors capable of multirobot collaboration and coordination. The focus of this thesis is twofold. The first part explores applying machine learning techniques to teach simulated humanoid robots skills such as how to move or walk and manipulate objects in their environment. Learning is performed using reinforcement learning policy search methods, and layered learning methodologies are employed during the learning process in which multiple lower level skills are incrementally learned and combined with each other to develop richer higher level skills. By incrementally learning skills in layers such that new skills are learned in the presence of previously learned skills, as opposed to individually in isolation, we ensure that the learned skills will work well together and can be combined to perform complex behaviors (e.g. playing soccer). The second part of the thesis centers on developing algorithms to coordinate the movement and efforts of multiple robots working together to quickly complete tasks. These algorithms prioritize minimizing the makespan, or time for all robots to complete a task, while also attempting to avoid interference and collisions among the robots. An underlying objective of this research is to develop techniques and methodologies that allow autonomous robots to robustly interact with their environment (through skill learning) and with each other (through movement coordination) in order to perform tasks and accomplish goals asked of them. The work in this thesis is implemented and evaluated in the RoboCup 3D simulation soccer domain, and has been a key component of the UT Austin Villa team winning the RoboCup 3D simulation league world championship six out of the past seven years.Computer Science

Interleaving Allocation, Planning, and Scheduling for Heterogeneous Multi-Robot Coordination through Shared Constraints

In a wide variety of domains, such as warehouse automation, agriculture, defense, and assembly, effective coordination of heterogeneous multi-robot teams is needed to solve complex problems. Effective coordination is predicated on the ability to solve the four fundamentally intertwined questions of coordination: what (task planning), who (task allocation), when (scheduling), and how (motion planning). Owing to the complexity of these four questions and their interactions, existing approaches to multi-robot coordination have resorted to defining and solving problems that focus on a subset of the four questions. Notable examples include Task and Motion Planning (what and how), Multi-Agent Planning (what and who), and Multi-Agent Path Finding (who and how). In fact, a holistic problem formulation that fully integrates the four questions lies beyond the scope of prior literature. This dissertation focuses on examining the use of shared constraints on tasks and robots to interleave algorithms for task planning, task allocation, scheduling, and motion planning and investigating the hypothesis that a framework that interleaves algorithms to these four sub-problems will lead to solutions with lower makespans, greater computational efficiency, and the ability to solve larger problems. To support this claim, this dissertation contributes: (i) a novel temporal planner that interleaves task planning and scheduling layers, (ii) a trait-based time-extended task allocation framework that interleaves task allocation, scheduling, and motion planning, (iii) the formulation of holistic heterogeneous multi-robot coordination problem that simultaneously considers all four questions, (iv) a framework that interleaves layers for all four questions to solve this holistic heterogeneous multi-robot coordination problem, (v) a scheduling algorithm that reasons about temporal uncertainty, provides a theoretical guarantee on risk, and can be utilized within our framework, and (vi) a learning-based scheduling algorithm that reasons about deadlines and can be utilized within our framework.Ph.D