140 research outputs found
Decentralized dynamic task allocation for UAVs with limited communication range
We present the Limited-range Online Routing Problem (LORP), which involves a
team of Unmanned Aerial Vehicles (UAVs) with limited communication range that
must autonomously coordinate to service task requests. We first show a general
approach to cast this dynamic problem as a sequence of decentralized task
allocation problems. Then we present two solutions both based on modeling the
allocation task as a Markov Random Field to subsequently assess decisions by
means of the decentralized Max-Sum algorithm. Our first solution assumes
independence between requests, whereas our second solution also considers the
UAVs' workloads. A thorough empirical evaluation shows that our workload-based
solution consistently outperforms current state-of-the-art methods in a wide
range of scenarios, lowering the average service time up to 16%. In the
best-case scenario there is no gap between our decentralized solution and
centralized techniques. In the worst-case scenario we manage to reduce by 25%
the gap between current decentralized and centralized techniques. Thus, our
solution becomes the method of choice for our problem
A Distributed Pipeline for Scalable, Deconflicted Formation Flying
Reliance on external localization infrastructure and centralized coordination
are main limiting factors for formation flying of vehicles in large numbers and
in unprepared environments. While solutions using onboard localization address
the dependency on external infrastructure, the associated coordination
strategies typically lack collision avoidance and scalability. To address these
shortcomings, we present a unified pipeline with onboard localization and a
distributed, collision-free motion planning strategy that scales to a large
number of vehicles. Since distributed collision avoidance strategies are known
to result in gridlock, we also present a decentralized task assignment solution
to deconflict vehicles. We experimentally validate our pipeline in simulation
and hardware. The results show that our approach for solving the optimization
problem associated with motion planning gives solutions within seconds in cases
where general purpose solvers fail due to high complexity. In addition, our
lightweight assignment strategy leads to successful and quicker formation
convergence in 96-100% of all trials, whereas indefinite gridlocks occur
without it for 33-50% of trials. By enabling large-scale, deconflicted
coordination, this pipeline should help pave the way for anytime, anywhere
deployment of aerial swarms.Comment: 8 main pages, 1 additional page, accepted to RA-L and IROS'2
A Finite-Time Cutting Plane Algorithm for Distributed Mixed Integer Linear Programming
Many problems of interest for cyber-physical network systems can be
formulated as Mixed Integer Linear Programs in which the constraints are
distributed among the agents. In this paper we propose a distributed algorithm
to solve this class of optimization problems in a peer-to-peer network with no
coordinator and with limited computation and communication capabilities. In the
proposed algorithm, at each communication round, agents solve locally a small
LP, generate suitable cutting planes, namely intersection cuts and cost-based
cuts, and communicate a fixed number of active constraints, i.e., a candidate
optimal basis. We prove that, if the cost is integer, the algorithm converges
to the lexicographically minimal optimal solution in a finite number of
communication rounds. Finally, through numerical computations, we analyze the
algorithm convergence as a function of the network size.Comment: 6 pages, 3 figure
Topology-aware optimal task allocation framework for mission critical environment: Centralized and decentralized approaches
A Mission Critical Environment (MCE) consists of error-prone, highly variable, and highly rate limited communication channels. Paradoxically, this environment substantially increases the need to perform Optimal Task Allocation (OTA), while at the same time making it much harder to perform OTA efficiently. To perform OTA in MCE, in this thesis, I have proposed two novel automated algorithms. The first algorithm is called Centralized Optimal Task Allocation Algorithm (COTAA), where I consider OTA for publish/subscribe-based MCE since it has unique characteristics such as high level publish/subscribe node and task differentiation and high scalability. I also propose an architectural framework and communication protocols emphasizing the unique challenges of MCE. I adopt well known Hungarian Algorithm and Rectangular Assignment Algorithm to solve the OTA problem in polynomial time. The second algorithm is called Decentralized Optimal Task Allocation Algorithm (DOTAA) which exploits the concept of application-layer Distributed Hash Table (DHT) to perform OTA in MCE. Through simulations, I evaluate the performance of both COTAA and DOTAA for multiple mission critical scenarios. The results indicate that both COTAA and DOTAA achieve the goal of OTA in highly dynamic MCEs, with low processing time and communication overhead
A Distributed Version of the Hungarian Method for Multi-Robot Assignment
In this paper, we propose a distributed version of the Hungarian Method to
solve the well known assignment problem. In the context of multi-robot
applications, all robots cooperatively compute a common assignment that
optimizes a given global criterion (e.g. the total distance traveled) within a
finite set of local computations and communications over a peer-to-peer
network. As a motivating application, we consider a class of multi-robot
routing problems with "spatio-temporal" constraints, i.e. spatial targets that
require servicing at particular time instants. As a means of demonstrating the
theory developed in this paper, the robots cooperatively find online,
suboptimal routes by applying an iterative version of the proposed algorithm,
in a distributed and dynamic setting. As a concrete experimental test-bed, we
provide an interactive "multi-robot orchestral" framework in which a team of
robots cooperatively plays a piece of music on a so-called orchestral floor
An Integrated Approach for Achieving Multi-Robot Task Formations
©2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.DOI: 10.1109/TMECH.2009.2014056In this paper, a problem, called the initial formation problem, within the multirobot task allocation domain is addressed. This problem consists in deciding which robot should go to each of the positions of the formation in order to minimize an objective. Two different distributed algorithms that solve this problem are explained. The second algorithm presents a novel approach that uses cost means to model the cost distribution and improves the performance of the task allocation algorithm. Also, we present an approach that integrates distributed task allocation algorithms with a behavior-based architecture to control formations of robot teams. Finally, simulations and real experiments are used to analyze the formation behavior and provide performance metrics associated with implementation in realistic scenarios
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