Novel clustering-based pruning algorithms

Pattern Analysis and Applications - One of the crucial problems of designing a classifier ensemble is the proper choice of the base classifier line-up. Basically, such an ensemble is formed on the...     

考虑时序约束的多智能体协同任务分配

研究多智能体系统的多目标多任务分配问题, 考虑任务之间的时序关系, 建立分布式任务分配模型. 扩展了一致性包算法(CBBA), 按优先级将目标任务归入不同层级, 各智能体在构建任务包和任务路径时, 只将分配过高阶段任务的目标添加至相应的任务包和任务路径中, 从而保证目标任务时序约束的同时, 保持了CBBA算法的特性. 与多任务分配问题经典算法的比对实验表明, 所提出的改进算法求解结果稳定可靠, 运行时间优于经典算法.

     

自适应任务分配的粒子群优化算法

针对基本粒子群优化(PSO)算法早熟收敛、易陷入局部极值的缺陷,提出自适应任务分配的粒子群优化算法。该算法根据粒子的多样性动态分配粒子任务,把种群粒子分为开发和探索两种类型,分别采用全局模型和动态邻域局部模型执行开发和探索任务以平衡算法的全局和局部搜索能力,维持种群多样性。动态邻域模型扩大了解的搜索空间,能有效抑制早熟停滞现象,采用高斯扰动对处于停滞状态的精英粒子进行学习,协助精英粒子跳出局部最优,进入解空间的其他区域继续进行搜索。针对6个标准复合测试函数进行实验,结果表明所提算法具有更强的全局搜索能力,求解精度更高。     

Decentralized task allocation for heterogeneous multi-UAV system with task coupling constraints

The Journal of Supercomputing - Cooperative multiple task assignment problem is an essential issue in the collaboration of multiple unmanned aerial vehicles (UAVs). Consensus-based bundle algorithm...     

多机器人任务分配的研究与进展

从多机器人任务分配的类型、任务分配方法、任务的死锁与解除以及各种任务分配算法的对比等4个方面,对多机器人任务分配的最新研究进展进行了概述.分析了多机器人任务分配的发展趋势,指出动态环境和未知环境下大规模异构机器人任务分配问题的研究是必然趋势,在众多研究方法中,群体智能方法是解决该类问题的未来研究方向.     

RoboCup Rescue as multiagent task allocation among teams: experiments with task interdependencies

This paper addresses distributed task allocation among teams of agents in a RoboCup Rescue scenario. We are primarily concerned with testing different mechanisms that formalize issues underlying implicit coordination among teams of agents. These mechanisms are developed, implemented, and evaluated using two algorithms: Swarm-GAP and LA-DCOP. The latter bases task allocation on a comparison between an agent’s capability to perform a task and the capability demanded by this task. Swarm-GAP is a probabilistic approach in which an agent selects a task using a model inspired by task allocation among social insects. Both algorithms were also compared to another one that allocates tasks in a greedy way. Departing from previous works that tackle task allocation in the rescue scenario only among fire brigades, here we consider the various actors in the RoboCup Rescue, a step forward in the direction of realizing the concept of extreme teams. Tasks are allocated to teams of agents without explicit negotiation and using only local information. Our results show that the performance of Swarm-GAP and LA-DCOP are similar and that they outperform a greedy strategy. Also, it is possible to see that using more sophisticated mechanisms for task selection does pay off in terms of score.     

Multi-robot task allocation using CNP combines with neural network

Contract Net Protocol is a suitable method for multi-robot task allocation problems. However, it is difficult to find a function to evaluate robots’ bids when each robot gives more than one bid price to reflect its different abilities. We propose a method to fuse these prices and to decide which robot is the successful bidder using a BP neural network. The experiment result shows that the method is effective.     

Disjoint task allocation algorithms for MIN machines with minimalconflicts

This paper addresses task allocation schemes for MIN-based multiprocessors. Two types of allocation policies, cubic and noncubic, are discussed here. Conflicts through the network and inability to partition the system effectively are the main bottlenecks in a MIN-based system. To solve both the problems, a renaming scheme for input and output ports of a MIN is proposed. We use the baseline MIN as an example in this work and call the renaming scheme as bit reversal (BR) matching pattern. Allocation with the new matching pattern minimizes conflicts and partitions the system completely into independent subsystems. The novelty of this matching pattern is that we can use any dynamic cubic allocation and/or scheduling scheme developed for the hypercubes also for the MIN machines. The BR matching pattern can be used with any kind of MIN. An allocation policy for noncubic tasks is also presented with this matching pattern. Various performance measures with different allocation algorithms are compared via simulation. The advantages of the algorithms with the proposed matching pattern are shown in terms of system efficiency, delay and task miss ratio     

Decentralized task allocation for surveillance systems with critical tasks

This paper considers the problem of assigning a set of tasks to a set of heterogeneous agents under the additional assumptions that some tasks must be necessarily allocated and therefore are critical for the assignment problem, and that each agent can execute a limited number of tasks. In order to solve this problem in a decentralized way (i.e., without any form of central supervision), we develop an extension of an algorithm proposed in the recent literature. After analyzing convergence and communication requirement of the algorithm, a set of numerical simulations is provided to confirm the effectiveness of the proposed approach.     

具有角色意识的社区服务型时空众包任务分配

为了适应社区众包配送中投递箱或代收点收纳量有限等情况,本文提出社区服务型时空众包任务分配问题,根据用户定义的时间将物品较为实时地配送到地。针对该问题,本文通过基于角色的协作模型ECARGO(environments-classes, agents, roles, groups, objects)形式化问题,针对高资格值工人配送高价值量订单集的目标,提出基于贪婪分配的PQGR(places-qualification-based greedy)算法、基于考虑代理和角色冲突的团队多角色分配方法的PQGM(places-qualification-based GMAC)算法以及进一步缩短运行时间的改进PQGM算法。数据处理和量化方面,提出基于核密度聚类的新型角色感知方法以实现任务的有效划分,提出基于学习遗忘曲线的代理地点资格值多阶段量化模型,实现代理地点资格值的在线学习和自适应更新。最后,本文在gMission数据集和合成数据集上进行实验,验证了算法的有效性和效率。     

Decentral task allocation for industrial AGV-systems with resource constraints

Automated Guided Vehicles (AGVs) form a large and important part of the logistics transportation systems in today's industry and are widely used, especially in Europe. Today's AGV-systems offered by global manufacturers almost all operate under some form of centralized control where a single central controller coordinates the entire fleet of AGVs. There is a trend towards decentralized control of these systems where AGVs make individual decisions that promote the flexibility, robustness and scalability of transport. However, its practical implementation seems to be in its infancy. In addition to the lack of practical implementation of decentralized control in industrial AGV-systems, we have observed a research gap in intelligent resource management of AGV-systems, which we have tried to address in previous work by proposing a more intelligent resource management approach. In this paper, we have addressed both the perceived lack of practical decentralized AGV control and the lack of intelligent resource management by proposing a decentralized task allocation algorithm based on sequential single-item auctions, taking into account resource constraints, and in which our intelligent resource management approach from previous work is introduced. We have benchmarked our new approach to a genetic algorithm-based task-allocation solver that uses “threshold-100”-charging as a resource management strategy. The result of the proposal is a decentralized task-allocation architecture under resource constraints that could be used in current AGV-systems to add more decentralized features to the fleet.     

Multiagent task allocation in social networks

This paper proposes a new variant of the task allocation problem, where the agents are connected in a social network and tasks arrive at the agents distributed over the network. We show that the complexity of this problem remains NP-complete. Moreover, it is not approximable within some factor. In contrast to this, we develop an efficient greedy algorithm for this problem. Our algorithm is completely distributed, and it assumes that agents have only local knowledge about tasks and resources. We conduct a broad set of experiments to evaluate the performance and scalability of the proposed algorithm in terms of solution quality and computation time. Three different types of networks, namely small-world, random and scale-free networks, are used to represent various social relationships among agents in realistic applications. The results demonstrate that our algorithm works well and also that it scales well to large-scale applications. In addition we consider the same problem in a setting where the agents holding the resources are self-interested. For this, we show how the optimal algorithm can be used to incentivize these agents to be truthful. However, the efficient greedy algorithm cannot be used in a truthful mechanism, therefore an alternative, cluster-based algorithm is proposed and evaluated.     

基于效用函数的多机器人系统任务分配

目前所采用的多机器人系统任务分配方法大多都忽略了任务分配的解质量问题。从定量的角度出发,提出了一种基于效用函数的多机器人系统任务分配策略,在机器人能力向量和子任务要求的能力向量基础上,建立了效用函数的数学模型,根据效用函数大小进行任务分配。仿真实验在足球机器人仿真比赛平台上进行,结果表明该任务分配算法对异构多机器人系统合作具有很好的通用性,且算法快速简单,能够实现任务到机器人的最优映射。     

基于信息素的多机器人协作任务分配

针对多机器人系统未知环境下自主任务分配问题,提出了将虚拟吸引信息素和虚拟排斥信息素相结合的多机器人任务分配方法。在动态未知环境下,进行了多机器人协作搜集实验,实验结果表明所提方法既可以避免多个机器人集中在一个空间内造成冲突加剧的现象,又可以实现多机器人自主地进行任务分配目的。     

Social-welfare based task allocation for multi-robot systems with resource constraints

This paper aims to propose a distributed task allocation algorithm for a team of robots that have constraints on energy resources and operate in an unknown dynamic environment. The objective of the allocation is to maximize task completion ratio while minimizing resource usage. The approach we propose is inspired by the social welfare in economics that helps extend the combined operational lifetime of the team by balancing resource consumptions among robots. This social welfare based task allocation method positions a robot team appropriately in preparedness for dynamic future events and enables to achieve the objectives of the system flexibly depending on the application context. Our simulation-based experiments show that the proposed algorithm outperforms a typical market-based approach in various scenarios.     

复杂约束条件下异构多智能体联盟任务分配

分布式任务决策是提高多智能体系统自主性的关键.以异构多智能体协同执行复杂任务为背景,首先建立了一种考虑任务载荷资源约束、任务耦合关系约束及执行窗口约束等条件的异构多智能体分布式联盟任务分配模型;其次,对一致性包算法(CBBA)进行了扩展,提出了基于改进冲突消解原则的一致性联盟算法(CBCA),以实现异构多智能体协同无冲突任务分配,并进一步证明了在一定条件下CBCA算法收敛于改进顺序贪婪算法(ISGA).最后通过数值仿真,验证了CBCA算法求解复杂约束条件下异构多智能体联盟任务分配问题的可行性和快速性.     

Scalable hedonic coalition formation for task allocation with heterogeneous robots

Intelligent Service Robotics - Tasks in the real world are complex and often require multiple robots to collaborate to be serviced. In many cases, a task might require different sensory inputs and...     

Optimal task allocation in hypercube multiprocessor ensembles

Criteria are established to determine the optimal policy for allocating a set of uniform tasks onto a multiprocessor hypercube ensemble. It is shown that the optimal policy depends on the ratio of computation to intertask communication required by the distributed program, and that based on this ratio, tasks should be placed either all on one processor or uniformly distributed over the largest possible hypercube.     

多机器人系统任务分配的研究进展

多机器人系统任务分配是机器人研究领域一个关键的研究课题。从多机器人任务分配分类及问题描述、多机器人任务分配的研究动态等方面对多机器人任务分配进行了综述,并根据近期文献探讨了多机器人系统任务分配需要解决的若干重要问题。