Operating a large fleet of mobile robots using the plan-merging paradigm

The MARTHA project objectives are the control and the management of a fleet of autonomous mobile robots for transshipment tasks in harbors, airports and marshalling yards. One of the most challenging and key problems of the MARTHA project is multi-robot cooperation. A general concept for the control of a large fleet of autonomous mobile robots has been developed, implemented and validated in the framework of the MARTHA project. This is the first study in the autonomous mobile robot field to add multi-robot cooperation capabilities to such a large fleet of robots.

Industrial maintenance is undergoing a process of digitalization and automation through the use of Industry 4.0 technologies. An example is the employment of digital twins which allow virtualizing assets such as a fleet of Autonomous Mobile Robots (AMR) . In this paper, we analyze two local navigation algorithms for AMRs navigation in a digitized environment: Dynamic Window Approximation (DWA) and Time Elastic Band (TEB) . For this purpose, a digital twin is implemented in the ROS ecosystem. In this environment, a combined mission of a fleet of three AMRs is simulated where they navigate together in order to compare the behavior of the algorithms in dynamic industrial environments. As a result, the trajectories of each AMR that are key for production cost planning are analyzed. Finally, we introduce in this work a web server to interact with the developed digital twin facilitating its use.

Controlling a fleet of autonomous mobile robots (AMR) is a complex problem of optimization. Many approached have been conducted for solving this problem. They range from heuristics, which usually do not find an optimum, to mathematical models, which are limited due to their high computational effort. Machine Learning (ML) methods offer another potential trajectory for solving such complex problems. The focus of this brief survey is on Reinforcement Learning (RL) as a particular type of ML. Due to the reward-based optimization, RL offers a good basis for the control of fleets of AMR. In the context of this survey, different control approaches are investigated and the aspects of fleet control of AMR with respect to RL are evaluated. As a result, six fundamental key problems should be put on the current research agenda to enable a broader application in industry: (1) overcoming the “sim-to-real gap”, (2) increasing the robustness of algorithms, (3) improving data efficiency, (4) integrating different fields of application, (5) enabling heterogeneous fleets with different types of AMR and (6) handling of deadlocks.

This paper presents an implemented system which allows one to run a fleet of autonomous mobile robots in a route network with a very limited centralized activity. The robots are provided with all the necessary ingredients for planning and executing navigation missions in a multi-robot context. Multi-robot cooperation is based on a generic paradigm called plan-merging paradigm, where robots incrementally merge their plans into a set of already coordinated plans. The robot architecture is derived from the generic architecture developed at LAAS. A 3D graphic environment system allows one to display a complete system composed of a dozen (or more) robots, each running on an independent workstation. An example is presented together with numerical results on the system behavior.

Systems composed of a fleet of autonomous mobile robots are among the most complex control systems. This control complexity is at a high level especially when those robots navigate in hazardous and dynamic environments such as chemical analysis laboratories. These systems include different dangerous and harmful products (toxic, flammable, explosive...) with different quantity. In order to perform its mission on a regular basis, this multi-robot system can be controlled according to multiple architectures. We propose, firstly, to apply the System Theoretic Process Analysis (STPA) on two selected control architectures, namely distributed and hybrid architectures in order to obtain a set of loss scenarios for each kind of architecture. For further assessment, the Analytic Hierarchy Process (AHP) is used to choose the best architecture. The proposed approach provides a risk analysis and a more practical comparison between the two control architectures of a mobile multi-robot system and facilitates decision-making, even in complex situations.

Many robotics applications involve navigating to positions specified in terms of their semantic significance. A robot operating in a hotel may need to deliver room service to a named room. In a hospital, it may need to deliver medication to a patient's room. The Building-Wide Intelligence Project at UT Austin has been developing a fleet of autonomous mobile robots, called BWIBots, which perform tasks in the computer science department. Tasks include guiding a person, delivering a message, or bringing an object to a location such as an office, lecture hall, or classroom. The process of constructing a map that a robot can use for navigation has been simplified by modern SLAM algorithms. The attachment of semantics to map data, however, remains a tedious manual process of labeling locations in otherwise automatically generated maps. This paper introduces a system called PRISM to automate a step in this process by enabling a robot to localize door signs – a semantic markup intended to aid the human occupants of a building – and to annotate these locations in its map.

A fleet of autonomous mobile robots are used in the hospital for the delivery service. To increase the efficiency of using multiple robots, an appropriate task allocation algorithm is required. This paper explains the task allocation procedure for multiple robots and introduces two multi-task allocation (MT) algorithms to be applied in the hospital delivery service. The algorithms are tested via simulation, and the performance are compared according to the criteria. The result shows that proposing combinatorial search algorithm with limited capacity performed better among others.

The commercially available battery management and mission scheduling systems for fleets of autonomous mobile robots use different algorithms to calculate the current state of charge of the robot's battery. This information alone cannot be used to predict whether it will be possible for a single robot in the fleet to execute all of the scheduled missions. This paper provides insight into how to develop a universal battery discharge model based on key mission parameters, which allows for predicting the battery usage over the course of the scheduled missions and can, in turn, be used to determine which missions to delegate to other robots in the fleet, or if more robots are needed in the fleet to accomplish the production plan. The resulting model is, therefore, necessary for mission scheduling in a flexible production system, including autonomous mobile robot transportation networks.

The construction of a smart autonomous mobile robot system that can operate in an anonymous environment can be difficult. Building an autonomous mobile robot requires collecting data from sensors and cameras in order to recognize the surrounding environment, get knowledge of existing landmarks, and figure out its position and orientation. Many research studies have been carried out in recent years providing algorithms for achieving autonomous motion of mobile robot systems and object detection. The most common platform utilized for robot motion design is ROS (Robot Operating System) and for object detection YOLO (You Only Look Once) is a common choice. Additionally, the autonomous system needs motion planning in a systematic way to avoid obstacles by using LIDAR or other sensing methods generating the feedback signals required for control of motion around occupied spaces. One of the most effective and versatile feedback systems for mobile robot control can be constructed using visual feedback, which relies on a high-resolution camera that processes the image in real-time and the result is fed back to the control system for decision making. Visual feedback can be employed alone without sensors for feedback control of autonomous mobile robots, and it can be used for multiple tasks like path planning, obstacle avoidance, object detection, object recognition in addition to motion control. In this research, results of design and construction of an autonomous mobile rescue robot for finding victims through video data are presented. It is aimed to find victims via applying video as a data collection and feedback generation tool in the autonomous mobile robot control system. The visual feedback and YOLO v3 are used not only for finding victims but also for motion control. Our autonomous mobile robot is designed to move smoothly to the victim's direction and avoids obstacles by using ROS Global Planner. The robot is tested in multiple scenarios in different locations, various obstacle and victim numbers, and satisfactory results with good performances are obtained.

Purpose: The purpose of this paper is to describe the implementation of a Fleet Management System (FMS) that plans and controls the execution of logistics tasks by a set of mobile robots in a real-world hospital environment. The FMS is developed upon an architecture that hosts a routing engine, a task scheduler, an Endorse Broker, a controller and a backend Application Programming Interface (API). The routing engine handles the geo-referenced data and the calculation of routes; the task scheduler implements algorithms to solve the task allocation problem and the trolley loading problem using Integer Linear Programming (ILP) model and a Genetic Algorithm (GA) depending on the problem size. The Endorse Broker provides a messaging system to exchange information with the robotic fleet, while the controller implements the control rules to ensure the execution of the work plan. Finally, the Backend API exposes some FMS to external systems.Design/methodology/approach: The first part of the paper, focuses on the dynamic path planning problem of a set of mobile robots in indoor spaces such as hospitals, laboratories and shopping centres. A review of algorithms developed in the literature, to address dynamic path planning, is carried out; and an analysis of the applications of such algorithms in mobile robots that operate in real in-door spaces is performed. The second part of the paper focuses on the description of the FMS, which consists of five integrated tools to support the multi-robot dynamic path planning and the fleet management.Findings: The literature review, carried out in the context of path planning problem of multiple mobile robots in in-door spaces, has posed great challenges due to the environment characteristics in which robots move. The developed FMS for mobile robots in healthcare environments has resulted on a tool that enables to: (i) interpret of geo-referenced data; (ii) calculate and recalculate dynamic path plans and task execution plans, through the implementation of advanced algorithms that take into account dynamic events; (iii) track the tasks execution; (iv) fleet traffic control; and (v) to communicate with one another external systems.Practical implications: The proposed FMS has been developed under the scope of ENDORSE project that seeks to develop safe, efficient, and integrated indoor robotic fleets for logistic applications in healthcare and commercial spaces. Moreover, a computational analysis is performed using a virtual hospital floor-plant.Originality/value: This work proposes a novel FMS, which consists of integrated tools to support the mobile multi-robot dynamic path planning in a real-world hospital environment. These tools include: a routing engine that handles the geo-referenced data and the calculation of routes. A task scheduler that includes a mathematical model to solve the path planning problem, when a low number of robots is considered. In order to solve large size problems, a genetic algorithm is also implemented to compute the dynamic path planning with less computational effort. An Endorse broker to exchanges information between the robotic fleet and the FMS in a secure way. A backend API that provides interface to manage the master data of the FMS, to calculate an optimal assignment of a set of tasks to a group of robots to be executed on a specific date and time, and to add a new task to be executed in the current shift. Finally, a controller to ensures that the robots execute the tasks that have been assigned by the task scheduler.

Computer-based sensors and actuators such as global positioning systems, machine vision, and laser-based sensors have progressively been incorporated into mobile robots with the aim of configuring autonomous systems capable of shifting operator activities in agricultural tasks. However, the incorporation of many electronic systems into a robot impairs its reliability and increases its cost. Hardware minimization, as well as software minimization and ease of integration, is essential to obtain feasible robotic systems. A step forward in the application of automatic equipment in agriculture is the use of fleets of robots, in which a number of specialized robots collaborate to accomplish one or several agricultural tasks. This paper strives to develop a system architecture for both individual robots and robots working in fleets to improve reliability, decrease complexity and costs, and permit the integration of software from different developers. Several solutions are studied, from a fully distributed to a whole integrated architecture in which a central computer runs all processes. This work also studies diverse topologies for controlling fleets of robots and advances other prospective topologies. The architecture presented in this paper is being successfully applied in the RHEA fleet, which comprises three ground mobile units based on a commercial tractor chassis.

Robotics is a scientific discipline which includes the operation and development of robots and other fields of computer science, as well as electrical and mechanical engineering and high-performance modular control systems. This paper reports the first results of a research project that uses a previously developed MES - Modular Electronic System for autonomous mobile robots and for developing robots to use the MES in other scientific applications. This approach offers the ability to build a scalable and flexible control system with only a few modules. A novel feature of our approach is its modular integration of electronics with the ability to multitask, which can be adapted for particular applications. We demonstrate the robustness of this approach in controlling an indoor mobile robot for the EUROBOT 2009 and 2010.

The acquisition of navigation competencies by a mobile robot is one of the key problems in the development of an autonomous mobile robot. This subject has been approached in the scope of the development of ARCoS-autonomous mobile robot control system-whose purpose is to transform a mobile platform in a real autonomous mobile robot able to deal with several tasks in different environments and also able to evolve through the dynamic acquisition of new navigation competencies. In our approach the robot competencies will emerge from the reactive agent's actions involved in the assigned mission. Each reactive agent (RA) is responsible for control the mobile robot in the achievement of an elementary competence. The proposed RA architecture, integrating the connectionist and symbolic paradigms, aims to be rich enough to accommodate agents whose competence is easily described and programmed as well as those which are hard, boring or even impossible to be specified being learned from the experience. The RA architecture also implements the associated cooperation strategies with the other agents participating in the execution of the same task and therefore, allowing the mobile robot to exhibit more complex competence.

In this paper, we describe a basic sensing system for self-diagnosing an autonomous mobile robot. In recent years, many researches on intelligent robots and systems have been done. But, when such robots and systems work in the real environment, it is important for those robots and systems to have the ability to recognize their own conditions for detecting faults. On the point of view, we should consider pay more attention to diagnose in such intelligent systems. Therefore we try to construct an internal sensing system as a self-diagnosis system on a real robot. Especially, in this paper, we discuss about motor system of an autonomous omnidirectional mobile robot, which was developed in RIKEN. The self-diagnosis system consists of multiple sensors, which are voltage, current, encoder, and magnetic sensors. We show some diagnosing experimental results using the real system. From the results, we could collect basic data for fault detection of the system.

Flexible automation in the form of mobile robots holds the potential for decreasing operating costs while improving delivery performance in mid-size hospital delivery systems. This paper discusses the use of simulation modeling to analyze the costs, benefits, and performance tradeoffs related to the installation and use of a fleet of mobile robots within mid-size hospital facilities. The results of this study enable a better understanding of the delivery and transportation requirements of mid-sized hospitals and how a fleet of mobile robots can meet those requirements. We show that for clinical laboratory deliveries a fleet of 6 mobile robots can achieve significant performance gains over the current system of 3 human couriers while still remaining cost effective. The 6-robot alternative reduces the annual cost by approximately 56% and improves turn-around time performance by 33%.