Fast task-sequence allocation for heterogeneous robot teams with a human in the Loop

Efficient task allocation with timing constraints to a team of possibly heterogeneous robots is a challenging problem with application, e. g., in search and rescue. In this paper a mixed-integer linear programming (MILP) approach is proposed for assigning heterogeneous robot teams to the simultaneous completion of sequences of tasks with specific requirements such as completion deadlines. For this purpose our approach efficiently combines the strength of state of the art mixed-integer linear programming (MILP) solvers with human expertise in mission scheduling. We experimentally show that simple and intuitive inputs by a human user have substantial impact on both computation time and quality of the solution. The presented approach can in principle be applied to quite general missions for robot teams with human supervision

General Concepts for Human Supervision of Autonomous Robot Teams

For many dangerous, dirty or dull tasks like in search and rescue missions, deployment of autonomous teams of robots can be beneficial due to several reasons. First, robots can replace humans in the workspace. Second, autonomous robots reduce the workload of a human compared to teleoperated robots, and therefore multiple robots can in principle be supervised by a single human. Third, teams of robots allow distributed operation in time and space. This thesis investigates concepts of how to efficiently enable a human to supervise and support an autonomous robot team, as common concepts for teleoperation of robots do not apply because of the high mental workload. The goal is to find a way in between the two extremes of full autonomy and pure teleoperation, by allowing to adapt the robots’ level of autonomy to the current situation and the needs of the human supervisor. The methods presented in this thesis make use of the complementary strengths of humans and robots, by letting the robots do what they are good at, while the human should support the robots in situations that correspond to the human strengths. To enable this type of collaboration between a human and a robot team, the human needs to have an adequate knowledge about the current state of the robots, the environment, and the mission. For this purpose, the concept of situation overview (SO) has been developed in this thesis, which is composed of the two components robot SO and mission SO. Robot SO includes information about the state and activities of each single robot in the team, while mission SO deals with the progress of the mission and the cooperation between the robots. For obtaining SO a new event-based communication concept is presented in this thesis, that allows the robots to aggregate information into discrete events using methods from complex event processing. The quality and quantity of the events that are actually sent to the supervisor can be adapted during runtime by defining positive and negative policies for (not) sending events that fulfill specific criteria. This reduces the required communication bandwidth compared to sending all available data. Based on SO, the supervisor is enabled to efficiently interact with the robot team. Interactions can be initiated either by the human or by the robots. The developed concept for robot-initiated interactions is based on queries, that allow the robots to transfer decisions to another process or the supervisor. Various modes for answering the queries, ranging from fully autonomous to pure human decisions, allow to adapt the robots’ level of autonomy during runtime. Human-initiated interactions are limited to high-level commands, whereas interactions on the action level (e. g., teleoperation) are avoided, to account for the specific strengths of humans and robots. These commands can in principle be applied to quite general classes of task allocation methods for autonomous robot teams, e. g., in terms of specific restrictions, which are introduced into the system as constraints. In that way, the desired allocations emerge implicitly because of the introduced constraints, and the task allocation method does not need to be aware of the human supervisor in the loop. This method is applicable to different task allocation approaches, e. g., instantaneous or time-extended task assignments, and centralized or distributed algorithms. The presented methods are evaluated by a number of different experiments with physical and simulated scenarios from urban search and rescue as well as robot soccer, and during robot competitions. The results show that with these methods a human supervisor can significantly improve the robot team performance

Dynamic Rule-Based Reasoning in Smart Environments

Slimme huizen en andere soorten slimme omgevingen kunnen worden gedefinieerd door verschillende belangrijke karakteristieken. De belangrijkste hiervan is ongetwijfeld de mogelijkheid om omgevingsbewust te zijn, om de fysieke omgeving te ervaren en om de context van de huidige situatie te begrijpen. Slimme omgevingen zouden in staat moeten zijn om met deze informatie te kunnen redeneren en waardevolle kennis te kunnen afleiden. Daarnaast zullen ze de mogelijkheid moeten hebben om intelligent te reageren in reactie op veranderende situaties, volgens bepaalde doelstellingen. Slimme omgevingen zijn vaak ubiquitous, wat betekent dat hun capaciteiten voor waarnemen en handelen berusten op apparaten die zijn ingebed in de fysieke wereld. De meeste van de huidige commerciele slimme omgevingsproducten presenteren slechts gedeeltelijke oplossingen, zoals automatische verlichting of energiebewustzijn. Verschillende factoren vertragen de commercialisering van volledig slimme huizen, waaronder de noodzaak om de oplossing op iedere nieuwe locatie opnieuw zeer nauwkeurig af te stellen, de inspanningen rondom de integratie en co'ordinatie van verschillende componenten, handelingen om een consistent model over verschillende subsystemen van verschillende bronnen samen te stellen, enzovoorts. Samenvattend, de grote hoeveelheid aan inspanningen die benodigd zijn om de oplossing van een locatie naar een andere te verplaatsen hindert de mogelijkheden voor het stroomlijnen van de uitrol. Wat zijn de overeenkomsten in het ontwerp en het ontwikkelproces van een slimme omgeving? Wat is een effectieve aanpak om een redeneringsmotor voor slimme omgevingen te ontwerpen die aan alle belangrijke vereisten voldoet? Hoe kan het effect van sensorfouten voor wat betreft de besluitvorming worden geminimaliseerd? Hoe kan een slim systeem het bestaan van verschillende energieleveranciers gebruiken om de energiekosten in de tijd te minimaliseren? In dit proefschrift bespreken we en geven we antwoord op een aantal belangrijke onderzoeksvraagstukken voor huidige pervasieve systemen, slimme omgevingen in het bijzonder