Multi-Robot Task Allocation: A Spatial Queuing Approach

Multi-Robot Task Allocation (MRTA) is an important area of research in autonomous multi-robot systems. The main problem in MRTA is to match a set of robots to a set of tasks so that the tasks can be completed by the robots while optimizing a certain metric such as the time required to complete all tasks, distance traveled by the robots and energy expended by the robots. We consider a scenario where the tasks can appear dynamically and the location of tasks are not known a priori by the robots. Additionally, for a task to be completed, it needs to be performed by multiple robots. This setting is called the MR-ST-TA (multi-robot, single-task, time- extended assginment) category of MRTA; solving the MRTA problem for this category is a known NP-hard problem. In this thesis, we address this problem by proposing a new algorithm that uses a spatial queue-based model to allocate tasks between robots while comparing its performance to several other known methods. We have implemented these algorithms on an accurately simulated model of Corobot robots within the Webots simulator for different numbers of robots and tasks. The results show that our method is adept in all proffered environments, especially scenarios that benefit from path planning, whereas other methods display inherent weakness at one end of the spectrum: a decentralized greedy approach exhibits inefficient behavior as the robot to task ratio dips below one, whereas the Hungarian method (an offline algorithm) fails to keep pace as the robot count increases

Using Automated Task Solution Synthesis to Generate Critical Junctures for Management of Planned and Reactive Cooperation between a Human-Controlled Blimp and an Autonomous Ground Robot

This thesis documents the use of an approach for automated task solution synthesis that algorithmically and automatically identifies periods during which a team of less-than-fully capable robots benefit from tightly-coupled, coordinated, cooperative behavior. I test two hypotheses: 1) That a team’s performance can be increased by cooperating during certain specific periods of a mission and 2) That these periods can be identified automatically and algorithmically. I also demonstrate how identification of cooperative periods can be performed both off-line prior to the application and reactively during mission execution. I validate these premises in a real-world experiment using a human-piloted Unmanned Aerial Vehicle (UAV) and an autonomous mobile robot. For this experiment I construct a UAV and use an off-the-shelf robot. To identify the cooperative periods I use the ASyMTRe task solution synthesis system, and I use the Player robot server for control tasks such as navigation and path planning. My results show that teams employing cooperative behaviors during algorithmically identified cooperative periods exhibit better performance than non-cooperative teams in a target localization task. I also present results showing an increased time cost for cooperative behaviors and compare the increased time cost of two cooperative approaches that generate cooperative periods prior to and during mission execution

Abstracting Multidimensional Concepts for Multilevel Decision Making in Multirobot Systems

Multirobot control architectures often require robotic tasks to be well defined before allocation. In complex missions, it is often difficult to decompose an objective into a set of well defined tasks; human operators generate a simplified representation based on experience and estimation. The result is a set of robot roles, which are not best suited to accomplishing those objectives. This thesis presents an alternative approach to generating multirobot control algorithms using task abstraction. By carefully analysing data recorded from similar systems a multidimensional and multilevel representation of the mission can be abstracted, which can be subsequently converted into a robotic controller. This work, which focuses on the control of a team of robots to play the complex game of football, is divided into three sections: In the first section we investigate the use of spatial structures in team games. Experimental results show that cooperative teams beat groups of individuals when competing for space and that controlling space is important in the game of robot football. In the second section, we generate a multilevel representation of robot football based on spatial structures measured in recorded matches. By differentiating between spatial configurations appearing in desirable and undesirable situations, we can abstract a strategy composed of the more desirable structures. In the third section, five partial strategies are generated, based on the abstracted structures, and a suitable controller is devised. A set of experiments shows the success of the method in reproducing those key structures in a multirobot system. Finally, we compile our methods into a formal architecture for task abstraction and control. The thesis concludes that generating multirobot control algorithms using task abstraction is appropriate for problems which are complex, weakly-defined, multilevel, dynamic, competitive, unpredictable, and which display emergent properties

Design of an UAV swarm

This master thesis tries to give an overview on the general aspects involved in the design of an UAV swarm. UAV swarms are continuoulsy gaining popularity amongst researchers and UAV manufacturers, since they allow greater success rates in task accomplishing with reduced times. Appart from this, multiple UAVs cooperating between them opens a new field of missions that can only be carried in this way. All the topics explained within this master thesis will explain all the agents involved in the design of an UAV swarm, from the communication protocols between them, navigation and trajectory analysis and task allocation

Human-robot teamwork: a knowledge-based solution

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de ComputadoresTeams of humans and robots pose new challenges to the teamwork field. This stems from the fact that robots and humans have significantly different perceptual, reasoning, communication and actuation capabilities. This dissertation contributes to solving this problem by proposing a knowledge-based multi-agent system to support design and execution of stereotyped (i.e. recurring) human-robot teamwork. The cooperative workflow formalism has been selected to specify team plans, and adapted to allow activities to share structured data, even during their execution. This novel functionality enables tightly coupled interactions among team members. Rather than focusing on automatic teamwork planning, this dissertation proposes a complementary and intuitive knowledge-based solution for fast deployment and adaptation of small scale human-robot teams. In addition, the system has been designed in order to improve task awareness of each mission participant, and of the human overall mission awareness. A set of empirical results obtained from simulated and real missions proved the concept and the reusability of such a system. Practical results showed that this approach used is an effective solution for small scale teams in stereotyped human-robot teamwork

Cooperative Navigation for Teams of Mobile Robots

Teams of mobile robots have numerous applications, such as space exploration, underground mining, warehousing, and building security. Multi-robot teams can provide a number of practical benefits in such applications, including simultaneous presence in multiple locations, improved system performance, and greater robustness and redundancy compared to individual robots. This thesis addresses three aspects of coordination and navigation for teams of mobile robots: localization, the estimation of the position of each robot in the environment; motion planning, the process of finding collision-free trajectories through the environment; and task allocation, the selection of appropriate goals to be assigned to each robot. Each of these topics are investigated in the context of many robots working in a common environment. A particle-filter based system for cooperative global localization is presented. The system combines the sensor data from three robots, including measurements of the distances between robots, to cooperatively estimate the global position of each robot in the environment. The method is developed for a single triad of robots, then extended to larger groups of robots. The algorithm is demonstrated in a simulation of robots equipped with only simple range sensors, and is shown to successfully achieve global localization of robots that are unable to localize using only their own local sensor data. Motion planning is investigated for large teams of robots operating in tunnel and corridor environments, where coordinated planning is often required to avoid collision or deadlock conditions. A complete and scalable motion planning algorithm is presented and evaluated in simulation with up to 150 robots. In contrast to popular decoupled approaches to motion planning (which cannot guarantee a solution), this algorithm uses a multi-phase approach to create and maintain obstacle-free paths through a graph representation of the environment. The resulting plan is a set of collision-free trajectories, guaranteeing that every robot will reach its goal. The problem of task allocation is considered in the same type of tunnel and corridor environments, where tasks are defined as locations in the environment that must be visited by one of the robots in the team. To find efficient solutions to the task allocation problem, an optimization approach is used to generate potential task assignments, and select the best solution. The multi-phase motion planner is applied within this system as an efficient method of evaluating potential task assignments for many robots in a large environment. The algorithm is evaluated in simulations with up to 20 robots in a map of large underground mine. A real-world implementation of 3 physical robots was used to demonstrate the implementation of the multi-phase motion planning and task allocation systems. A centralized motion planning and task allocation system was developed, incorporating localization and time-dependent trajectory tracking on the robot processors, enabling cooperative navigation in a shared hallway environment