Fast Scheduling of Robot Teams Performing Tasks With Temporospatial Constraints

The application of robotics to traditionally manual manufacturing processes requires careful coordination between human and robotic agents in order to support safe and efficient coordinated work. Tasks must be allocated to agents and sequenced according to temporal and spatial constraints. Also, systems must be capable of responding on-the-fly to disturbances and people working in close physical proximity to robots. In this paper, we present a centralized algorithm, named 'Tercio,' that handles tightly intercoupled temporal and spatial constraints. Our key innovation is a fast, satisficing multi-agent task sequencer inspired by real-time processor scheduling techniques and adapted to leverage a hierarchical problem structure. We use this sequencer in conjunction with a mixed-integer linear program solver and empirically demonstrate the ability to generate near-optimal schedules for real-world problems an order of magnitude larger than those reported in prior art. Finally, we demonstrate the use of our algorithm in a multirobot hardware testbed

Deployment of Heterogeneous Swarm Robotic Agents Using a Task-Oriented Utility-Based Algorithm

In a swarm robotic system, the desired collective behavior emerges from local decisions made by robots, themselves, according to their environment. Swarm robotics is an emerging area that has attracted many researchers over the last few years. It has been proven that a single robot with multiple capabilities cannot complete an intended job within the same time frame as that of multiple robotic agents. A swarm of robots, each one with its own capabilities, are more flexible, robust, and cost-effective than an individual robot. As a result of a comprehensive investigation of the current state of swarm robotic research, this dissertation demonstrates how current swarm deployment systems lack the ability to coordinate heterogeneous robotic agents. Moreover, this dissertation's objective shall define the starting point of potential algorithms that lead to the development of a new software environment interface. This interface will assign a set of collaborative tasks to the swarm system without being concerned about the underlying hardware of the heterogeneous robotic agents. The ultimate goal of this research is to develop a task-oriented software application that facilitates the rapid deployment of multiple robotic agents. The task solutions are created at run-time, and executed by the agents in a centralized or decentralized fashion. Tasks are fractioned into smaller sub-tasks which are, then, assigned to the optimal number of robots using a novel Robot Utility Based Task Assignment (RUTA) algorithm. The system deploys these robots using it's application program interfaces (API's) and uploads programs that are integrated with a small routine code. The embedded routine allows robots to configure solutions when the decentralized approach is adopted. In addition, the proposed application also offers customization of robotic platforms by simply defining the available sensing and actuation devices. Another objective of the system is to improve code and component reusability to reduce efforts in deploying tasks to swarm robotic agents. Usage of the proposed framework prevents the need to redesign or rewrite programs should any changes take place in the robot's platform

Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms

This book is a reprint of the Special Issue “Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms”,which was published in Applied Sciences

A Resilient and Effective Task Scheduling Approach for Industrial Human-Robot Collaboration

Effective task scheduling in human-robot collaboration (HRC) scenarios is one of the great challenges of collaborative robotics. The shared workspace inside an industrial setting brings a lot of uncertainties that cannot be foreseen. A prior offline task scheduling strategy is ineffective in dealing with these uncertainties. In this paper, a novel online framework to achieve a resilient and reliable task schedule is presented. The framework can deal with deviations that occur during operation, different operator skills, error by the human or robot, and substitution of actors, while maintaining an efficient schedule by promoting parallel human-robot work. First, the collaborative job and the possible deviations are represented by AND/OR graphs. Subsequently, the proposed architecture chooses the most suitable path to improve the collaboration. If some failures occur, the AND/OR graph is adapted locally, allowing the collaboration to be completed. The framework is validated in an industrial assembly scenario with a Franka Emika Panda collaborative robot

Efficient Use of Human-robot Collaboration in Packaging through Systematic Task Assignment

The ageing workforce in Germany is a major challenge for many companies in the assembly and packaging of high-quality products. Particularly when individual processes require an increased amount of force or precision, the employees can be overstressed over a long period, depending on their physical constitution. One way of supporting employees in these processes is human-robot collaboration, because stressful process steps can be automated in a targeted manner. With conventional automation, this is currently not economically possible for many processes, as human capabilities are required. In order to achieve a balanced cooperation based on partnership, as well as to use additional potentials and to consider restrictions such as process times, it is necessary to ensure a good division of tasks between human and machine. The methodical procedure of allocation presented in this paper is based on the recreation of the process from basic process modules conducted by the process planner. Subsequently, these processes are divided according to the respective capabilities and the underlying process requirements. The company-specific target parameters, such as an improvement in ergonomics, are taken into account. The assignment procedure is described in a practical use case in the packaging of high-quality electronic consumer goods. Furthermore, the use case demonstrates the applicability of the approach. For these purposes, the parameters and requirements of the initial and result state of the workplace are described. The procedure and the decisions of the approach are shown with regard to the achievable goals

Deployment Environment for a Swarm of Heterogeneous Robots

The objective of this work is to develop a framework that can deploy and provide coordination between multiple heterogeneous agents when a swarm robotic system adopts a decentralized approach; each robot evaluates its relative rank among the other robots in terms of travel distance and cost to the goal. Accordingly, robots are allocated to the sub-tasks for which they have the highest rank (utility). This paper provides an analysis of existing swarm control environments and proposes a software environment that facilitates a rapid deployment of multiple robotic agents. The framework (UBSwarm) exploits our utility-based task allocation algorithm. UBSwarm configures these robots and assigns the group of robots a particular task from a set of available tasks. Two major tasks have been introduced that show the performance of a robotic group. This robotic group is composed of heterogeneous agents. In the results, a premature example that has prior knowledge about the experiment shows whether or not the robots are able to accomplish the task.https://doi.org/10.3390/robotics504002

The 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies

This publication comprises the papers presented at the 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland, on May 9-11, 1995. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed

Architecture for planning and execution of missions with fleets of unmanned vehicles

Esta tesis presenta contribuciones en el campo de la planificación automática y la programación de tareas, la rama de la inteligencia artificial que se ocupa de la realización de estrategias o secuencias de acciones típicamente para su ejecución por parte de vehículos no tripulados, robots autónomos y/o agentes inteligentes. Cuando se intenta alcanzar un objetivo determinado, la cooperación puede ser un aspecto clave. La complejidad de algunas tareas requiere la cooperación entre varios agentes. Mas aún, incluso si una tarea es lo suficientemente simple para ser llevada a cabo por un único agente, puede usarse la cooperación para reducir el coste total de la misma. Para realizar tareas complejas que requieren interacción física con el mundo real, los vehículos no tripulados pueden ser usados como agentes. En los últimos años se han creado y utilizado una gran diversidad de plataformas no tripuladas, principalmente vehículos que pueden ser dirigidos sin un humano a bordo, tanto en misiones civiles como militares. En esta tesis se aborda la aplicación de planificación simbólica de redes jerárquicas de tareas (HTN planning, por sus siglas en inglés) en la resolución de problemas de enrutamiento de vehículos (VRP, por sus siglas en inglés) [18], en dominios que implican múltiples vehículos no tripulados de capacidades heterogéneas que deben cooperar para alcanzar una serie de objetivos específicos. La planificación con redes jerárquicas de tareas describe dominios utilizando una descripción que descompone conjuntos de tareas en subconjuntos más pequeños de subtareas gradualmente, hasta obtener tareas del más bajo nivel que no pueden ser descompuestas y se consideran directamente ejecutables. Esta jerarquía es similar al modo en que los humanos razonan sobre los problemas, descomponiéndolos en subproblemas según el contexto, y por lo tanto suelen ser fáciles de comprender y diseñar. Los problemas de enrutamiento de vehículos son una generalización del problema del viajante (TSP, por sus siglas en inglés). La resolución del problema del viajante consiste en encontrar la ruta más corta posible que permite visitar una lista de ciudades, partiendo y acabando en la misma ciudad. Su generalización, el problema de enrutamiento de vehículos, consiste en encontrar el conjunto de rutas de longitud mínima que permite cubrir todas las ciudades con un determinado número de vehículos. Ambos problemas cuentan con una fuerte componente combinatoria para su resolución, especialmente en el caso del VRP, por lo que su presencia en dominios que van a ser tratados con un planificador HTN clásico supone un gran reto. Para la aplicación de un planificador HTN en la resolución de problemas de enrutamiento de vehículos desarrollamos dos métodos. En el primero de ellos presentamos un sistema de optimización de soluciones basado en puntuaciones, que nos permite una nueva forma de conexión entre un software especializado en la resolución del VRP con el planificador HTN. Llamamos a este modo de conexión el método desacoplado, puesto que resolvemos la componente combinatoria del problema de enrutamiento de vehículos mediante un solucionador específico que se comunica con el planificador HTN y le suministra la información necesaria para continuar con la descomposición de tareas. El segundo método consiste en mejorar el planificador HTN utilizado para que sea capaz de resolver el problema de enrutamiento de vehículos de la mejor forma posible sin tener que depender de módulos de software externos. Llamamos a este modo el método acoplado. Con este motivo hemos desarrollado un nuevo planificador HTN que utiliza un algoritmo de búsqueda distinto del que se utiliza normalmente en planificadores de este tipo. Esta tesis presenta nuevas contribuciones en el campo de la planificación con redes jerárquicas de tareas para la resolución de problemas de enrutamiento de vehículos. Se aplica una nueva forma de conexión entre dos planificadores independientes basada en un sistema de cálculo de puntuaciones que les permite colaborar en la optimización de soluciones, y se presenta un nuevo planificador HTN con un algoritmo de búsqueda distinto al comúnmente utilizado. Se muestra la aplicación de estos dos métodos en misiones civiles dentro del entorno de los Proyectos ARCAS y AEROARMS financiados por la Comisión Europea y se presentan extensos resultados de simulación para comprobar la validez de los dos métodos propuestos.This thesis presents contributions in the field of automated planning and scheduling, the branch of artificial intelligence that concerns the realization of strategies or action sequences typically for execution by unmanned vehicles, autonomous robots and/or intelligent agents. When trying to achieve certain goal, cooperation may be a key aspect. The complexity of some tasks requires the cooperation among several agents. Moreover, even if the task is simple enough to be carried out by a single agent, cooperation can be used to decrease the overall cost of the operation. To perform complex tasks that require physical interaction with the real world, unmanned vehicles can be used as agents. In the last years a great variety of unmanned platforms, mainly vehicles that can be driven without a human on board, have been developed and used both in civil and military missions. This thesis deals with the application of Hierarchical Task Network (HTN) planning in the resolution of vehicle routing problems (VRP) [18] in domains involving multiple heterogeneous unmanned vehicles that must cooperate to achieve specific goals. HTN planning describes problem domains using a description that decomposes set of tasks into subsets of smaller tasks and so on, obtaining low-level tasks that cannot be further decomposed and are supposed to be executable. The hierarchy resembles the way the humans reason about problems by decomposing them into sub-problems depending on the context and therefore tend to be easy to understand and design. Vehicle routing problems are a generalization of the travelling salesman problem (TSP). The TSP consists on finding the shortest path that connects all the cities from a list, starting and ending on the same city. The VRP consists on finding the set of minimal routes that cover all cities by using a specific number of vehicles. Both problems have a combinatorial nature, specially the VRP, that makes it very difficult to use a HTN planner in domains where these problems are present. Two approaches to use a HTN planner in domains involving the VRP have been tested. The first approach consists on a score-based optimization system that allows us to apply a new way of connecting a software specialized in the resolution of the VRP with the HTN planner. We call this the decoupled approach, as we tackle the combinatorial nature of the VRP by using a specialized solver that communicates with the HTN planner and provides all the required information to do the task decomposition. The second approach consists on improving and enhancing the HTN planner to be capable of solving the VRP without needing the use of an external software. We call this the coupled approach. For this reason, a new HTN planner that uses a different search algorithm from these commonly used in that type of planners has been developed and is presented in this work. This thesis presents new contributions in the field of hierarchical task network planning for the resolution of vehicle routing problem domains. A new way of connecting two independent planning systems based on a score calculation system that lets them cooperate in the optimization of the solutions is applied, and a new HTN planner that uses a different search algorithm from that usually used in other HTN planners is presented. These two methods are applied in civil missions in the framework of the ARCAS and AEROARMS Projects funded by the European Commission. Extensive simulation results are presented to test the validity of the two approaches

Task Partitioning for Distributed Assembly

This thesis addresses the problem of how to plan a strategy for a team of robots to cooperatively build a structure, henceforth referred to as the distributed assembly problem. The problem of distributed assembly requires a range of capabilities for successful completion of the task. These include accurate sensing and manipulation using a mobile robot, the ability to continuously adhere to precedence constraints on placements, and the ability to guarantee static stability at every stage of construction. The fundamental contribution of this work is to propose methods to address task allocation problems in the presence of constraints on task ordering. Algorithms are presented to partition 2- and 3-D assembly tasks into separate subtasks that satisfy local and global precedence constraints between the assembly components. The objective is to achieve a partitioning that minimizes completion time by minimizing the workload imbalance between the robots, and maximizes assembly parallelization. Towards this objective four approaches are presented. The first is an approach where each robot runs a simultaneous Dijkstra's Algorithm with its own root. The second approach incorporates online workload balancing and error correction by adding a communication scheme and a scanning robot equipped with a visual depth sensor. The third approach addresses the task partitioning using an algorithm inspired by Ant Colony Optimization. Finally, the problem of cooperative manipulation for tasks that require close coordination is addressed. All approaches are tested in both simulation and experiment.Ph.D., Mechanical Engineering -- Drexel University, 201