284 research outputs found

    Multi-Robot Task Allocation: A Spatial Queuing Approach

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    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 diļ¬€erent numbers of robots and tasks. The results show that our method is adept in all proļ¬€ered environments, especially scenarios that beneļ¬t from path planning, whereas other methods display inherent weakness at one end of the spectrum: a decentralized greedy approach exhibits ineļ¬ƒcient behavior as the robot to task ratio dips below one, whereas the Hungarian method (an oļ¬„ine algorithm) fails to keep pace as the robot count increases

    A Survey and Analysis of Multi-Robot Coordination

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    International audienceIn the field of mobile robotics, the study of multi-robot systems (MRSs) has grown significantly in size and importance in recent years. Having made great progress in the development of the basic problems concerning single-robot control, many researchers shifted their focus to the study of multi-robot coordination. This paper presents a systematic survey and analysis of the existing literature on coordination, especially in multiple mobile robot systems (MMRSs). A series of related problems have been reviewed, which include a communication mechanism, a planning strategy and a decision-making structure. A brief conclusion and further research perspectives are given at the end of the paper

    A Unified Framework for Solving Multiagent Task Assignment Problems

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    Multiagent task assignment problem descriptors do not fully represent the complex interactions in a multiagent domain, and algorithmic solutions vary widely depending on how the domain is represented. This issue is compounded as related research fields contain descriptors that similarly describe multiagent task assignment problems, including complex domain interactions, but generally do not provide the mechanisms needed to solve the multiagent aspect of task assignment. This research presents a unified approach to representing and solving the multiagent task assignment problem for complex problem domains. Ideas central to multiagent task allocation, project scheduling, constraint satisfaction, and coalition formation are combined to form the basis of the constrained multiagent task scheduling (CMTS) problem. Basic analysis reveals the exponential size of the solution space for a CMTS problem, approximated by O(2n(m+n)) based on the number of agents and tasks involved in a problem. The shape of the solution space is shown to contain numerous discontinuous regions due to the complexities involved in relational constraints defined between agents and tasks. The CMTS descriptor represents a wide range of classical and modern problems, such as job shop scheduling, the traveling salesman problem, vehicle routing, and cooperative multi-object tracking. Problems using the CMTS representation are solvable by a suite of algorithms, with varying degrees of suitability. Solution generating methods range from simple random scheduling to state-of-the-art biologically inspired approaches. Techniques from classical task assignment solvers are extended to handle multiagent task problems where agents can also multitask. Additional ideas are incorporated from constraint satisfaction, project scheduling, evolutionary algorithms, dynamic coalition formation, auctioning, and behavior-based robotics to highlight how different solution generation strategies apply to the complex problem space

    Dynamic Task-Allocation for Unmanned Aircraft Systems

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    This dissertation addresses improvements to a consensus based task allocation algorithms for improving the Quality of Service in multi-task and multi-agent environments. Research in the past has led to many centralized task allocation algorithms where a central computation unit is calculating the global optimum task allocation solution. The centralized algorithms are plagued by creating a single point of failure and the bandwidth needed for creating consistent and accurate situational awareness off all agents. This work will extend upon a widely researched decentralized task assignment algorithm based on the consensus principle. Although many extensions have led to improvements of the original algorithm, there is still much opportunity for improvement in providing sufficient and reliable task assignments in real-world dynamic conditions and changing environments. This research addresses practical changes made to the consensus based task allocation algorithms for improving the Quality of Service in multi-task and multi-agent environments

    Multi-type Fair Resource Allocation for Distributed Multi-Robot Systems

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    Fair resource allocation is essential to ensure that all resource requesters acquire adequate resources and accomplish tasks. We propose solutions to the fairness problem in multi-type resource allocation for multi-robot systems that have multiple resource requesters. We apply the dominant resource fairness (DRF) principle in our solutions to two different systems: single-tasking robots with multi-robot tasks (STR-MRT) and multi-tasking robots with single-robot tasks (MTR-SRT). In STR-MRT, each robot can perform only one task at a time, tasks are divisible, and accomplishing each task requires one or more robots. In MTR-SRT, each robot can perform multiple tasks at a time, tasks are not divisible, and accomplishing each task requires only one robot. We present centralized solutions to the fairness problem in STR-MRT. Meanwhile, we model the decentralized resource allocation in STR-MRT as a coordination game between the robots. Each robot subgroup is formed by robots that strategically select the same resource requester. For a requester associated with a specific subgroup, a consensus-based team formation algorithm further chooses the minimal set of robots to accomplish the task. We leverage the Deep Q-learning Network (DQN) to support requester selection. The results suggest that the DQN outperforms the commonly used Q-learning. Finally, we propose two decentralized solutions to promote fair resource allocation in MTR-SRT, as a centralized solution already exists. We first propose a task-forwarding solution in which the robots need to negotiate the placement of each task. In our second solution, each robot first selects resource requesters and then independently allocates resources to tasks that arrive from the selected requesters. The resource-requester selection phase of the latter solution models a coordination game that is solved by reinforcement learning. The experimental results suggest that both approaches outperform their baselines

    Multi-Robot Coalition Formation

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    Mechanism Selection for Multi-Robot Task Allocation

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    There is increasing interest in fielding multi-robot teams for applications such as search and rescue, warehouse automation, and delivery of consumer goods. Task allocation is an important problem to solve in such multi-robot settings. Given a mission that can be decomposed into discrete tasks, the Multi-Robot Task Allocation (MRTA) problem looks for an assignment of tasks to robots that ultimately results in efficient execution of the mission. There is a range of approaches to this optimisation problem, from centralised solvers to fully distributed methods that involve no explicit coordination between team members. Somewhere in the middle of this range lie market-based approaches, where tasks can be treated as goods, robots as "buyers" who can compute and express their own preferences for tasks in a virtual marketplace, and some clearing mechanism exists to match tasks to robots according to these preferences. The most common type of market-based mechanism for multi-robot task allocation is an auction, in which tasks are announced to the team, robots compute and place bids that encode some measure of cost or utility of performing the tasks, and tasks are awarded to robots over a number of rounds, according to the particular rules of the mechanism. Many different auction mechanisms exist, and they vary in the trade-offs that they make between computation time and space on the one hand, and performance of the execution of the mission on the other. In addition, the performance that results from a mechanism's allocation can be greatly affected by properties of task environments---the spatial and temporal arrangements of tasks, as well as other properties like precedence constraints, whether tasks require the simultaneous cooperation of multiple robots, and so on---in which it is employed. A simple mechanism that is inexpensive to compute and scales well may perform well in some environments, but not in others. The work presented in this thesis focuses on this relationship between auction-based task allocation mechanisms and properties of task environments, with the goal of developing a method of selecting, from a portfolio, a mechanism that is appropriate for a given task environment. The first part of this work is an empirical performance evaluation of a range of mechanisms employed in a series of environments of increasing complexity. The second part of this work uses results from this evaluation to develop and train a data-driven method of mechanism selection using properties of environments that can be measured at the start of a mission. The results show that, under certain conditions, this method of mechanism selection can lead to significant performance improvements compared to using a single mechanism alone

    Instantaneous multi-sensor task allocation in static and dynamic environments

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    A sensor network often consists of a large number of sensing devices of different types. Upon deployment in the field, these sensing devices form an ad hoc network using wireless links or cables to communicate with each other. Sensor networks are increasingly used to support emergency responders in the field usually requiring many sensing tasks to be supported at the same time. By a sensing task we mean any job that requires some amount of sensing resources to be accomplished such as localizing persons in need of help or detecting an event. Tasks might share the usage of a sensor, but more often compete to exclusively control it because of the limited number of sensors and overlapping needs with other tasks. Sensors are in fact scarce and in high demand. In such cases, it might not be possible to satisfy the requirements of all tasks using available sensors. Therefore, the fundamental question to answer is: ā€œWhich sensor should be allocated to which task?", which summarizes the Multi-Sensor Task Allocation (MSTA) problem. We focus on a particular MSTA instance where the environment does not provide enough information to plan for future allocations constraining us to perform instantaneous allocation. We look at this problem in both static setting, where all task requests from emergency responders arrive at once, and dynamic setting, where tasks arrive and depart over time. We provide novel solutions based on centralized and distributed approaches. We evaluate their performance using mainly simulations on randomly generated problem instances; moreover, for the dynamic setting, we consider also feasibility of deploying part of the distributed allocation system on user mobile devices. Our solutions scale well with different number of task requests and manage to improve the utility of the network, prioritizing the most important tasks.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Planning Algorithms for Multi-Robot Active Perception

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    A fundamental task of robotic systems is to use on-board sensors and perception algorithms to understand high-level semantic properties of an environment. These semantic properties may include a map of the environment, the presence of objects, or the parameters of a dynamic field. Observations are highly viewpoint dependent and, thus, the performance of perception algorithms can be improved by planning the motion of the robots to obtain high-value observations. This motivates the problem of active perception, where the goal is to plan the motion of robots to improve perception performance. This fundamental problem is central to many robotics applications, including environmental monitoring, planetary exploration, and precision agriculture. The core contribution of this thesis is a suite of planning algorithms for multi-robot active perception. These algorithms are designed to improve system-level performance on many fronts: online and anytime planning, addressing uncertainty, optimising over a long time horizon, decentralised coordination, robustness to unreliable communication, predicting plans of other agents, and exploiting characteristics of perception models. We first propose the decentralised Monte Carlo tree search algorithm as a generally-applicable, decentralised algorithm for multi-robot planning. We then present a self-organising map algorithm designed to find paths that maximally observe points of interest. Finally, we consider the problem of mission monitoring, where a team of robots monitor the progress of a robotic mission. A spatiotemporal optimal stopping algorithm is proposed and a generalisation for decentralised monitoring. Experimental results are presented for a range of scenarios, such as marine operations and object recognition. Our analytical and empirical results demonstrate theoretically-interesting and practically-relevant properties that support the use of the approaches in practice
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