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为实现多机器人系统的动态任务分配与协作,提出了一种面向多机器人动态任务分配的事件驱动免疫网络算法。将生物免疫网络的工作机理应用到多机器人动态任务分配算法中,借鉴Jerne的独特型免疫网络假说和Farmer提出的抗体激励动态方程,设计了多机器人任务分配与自主协作模型;基于事件驱动机制,设计了多机器人动态任务分配算法,并引入焦躁模型来解决任务死锁问题。仿真和实际多机器人系统实验结果表明,基于本文算法的多机器人系统在动态任务场景中具有较强的适应性和自主规划协调能力。 相似文献
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多机器人系统任务分配的研究进展 总被引:2,自引:0,他引:2
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。 相似文献
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建立系统的概率模型是描述和分析自组织多机器人系统的一条新思路。运用包括随机过程、矩阵论和线性代数等数学方法建立自组织多机器人系统的任务分配模型,克服了现存模型对任务类型数目无可扩展性的缺点。为了验证模型的一般性和有效性,以时间离散状态连续的马尔科夫链的极限分布作为任务分配的理论结果,优点是可以预测多机器人系统任务分配的长期稳定行为。任务分配的目的是保持执行任意一种任务的机器人数量占机器人总数的比例与该种任务所占总任务量的比例相等。仿真实验的结论也说明了任务分配模型可以达到理想的分配效果。 相似文献
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作为诸多移动机器人应用的基础,完全覆盖旨在为机器人规划出一条访问目标区域所有点且耗时最短的无碰撞路径。此类覆盖应用中,利用多台机器人协同覆盖可以有效缩短覆盖时间并提升系统的鲁棒性,同时也增加了算法设计复杂度和机器人协同管理难度。因此,文中研究了已知环境下的多机器人覆盖问题,该问题已被证明是一个NP难题。文中提出了一种启发式的基于多层次图划分的多机器人任务分配方法(Multi-robot Task Assignment Based on Multi-level Graph Partitioning, TAMP),该方法包含一种粗化任务分配算法和一种精细任务分配算法。粗化任务分配算法采用分层粗化的方法,通过图的极大匹配实现了节点融合以降低图的规模,并基于均匀种子的图增长方式获取了一个接近均衡的初始任务分配结果,提高算法效率;精细任务分配算法在粗化任务分配算法的基础上,提出了一种基于边界节点交换的Lazy&Lock策略,用于实现任务细分,提高求解精度。文中在不同规模的随机图和真实世界的治安巡逻场景下进行了仿真验证。仿真结果表明,相比经典的任务分配方法,TAMP方法将可求解的最大计算规... 相似文献
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Task allocation mechanisms are employed by multi-robot systems to efficiently distribute tasks between different robots. Currently, many task allocation methods rely on detailed expert knowledge to coordinate robots. However, it may not be feasible to dedicate an expert human user to a multi-robot system. Hence, a non-expert user may have to specify tasks to a team of robots in some situations. This paper presents a novel reduced human user input multi-robot task allocation technique that utilises Fuzzy Inference Systems (FISs). A two-stage primary and secondary task allocation process is employed to select a team of robots comprising manager and worker robots. A multi-robot mapping and exploration task is utilised as a model task to evaluate the task allocation process. Experiments show that primary task allocation is able to successfully identify and select manager robots. Similarly, secondary task allocation successfully identifies and selects worker robots. Both task allocation processes are also robust to parameter variation permitting intuitive selection of parameter values. 相似文献
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针对多机器人系统动态任务分配中存在的优化问题,在使用合同网初始任务分配的基础上提出了一种使用帕累托改进的任务二次分配算法。多机器人系统并行执行救火任务时,首先通过初始化任务分配将多机器人划分为若干子群;然后,每个子群承包某一救火任务,子群在执行任务的同时与就近子群进行帕累托改进确定需要迁移的机器人,实现两子群之间帕累托最优;最后,使用后序二叉树遍历对所有子群进行帕累托改进实现全局帕累托最优。理论分析和仿真结果表明,相较于强化学习算法和蚁群算法,所提算法的救火任务时间分别减少26.18%和37.04%;相较于传统合同网方法,所提算法在时间方面能够高效完成救火任务,在系统收益方面也具有明显优势。 相似文献
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《Advanced Robotics》2013,27(1-2):1-23
This paper presents a system for the coordination of aerial and ground robots for applications such as surveillance and intervention in emergency management. The overall system architecture is described. An important part for the coordination between robots is the task allocation strategy. A distributed market-based algorithm, called S + T, has been developed to solve the multi-robot task allocation problem in applications that require cooperation among the robots to accomplish all the tasks. Using this algorithm, robots can provide transport and communication relay services dynamically to other robots during the missions. Moreover, the paper presents a demonstration with a team of heterogeneous robots (aerial and ground) cooperating in a mission of fire detection and extinguishing. 相似文献
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Multi-Robot Task Allocation in Uncertain Environments 总被引:4,自引:0,他引:4
Multiple cooperating robots hold the promise of improved performance and increased fault tolerance for large-scale problems such as planetary survey and habitat construction. Multi-robot coordination, however, is a complex problem. We cast this problem in the framework of multi-robot dynamic task allocation under uncertainty. We then describe an empirical study that sought general guidelines for task allocation strategies in multi-robot systems. We identify four distinct task allocation strategies, and demonstrate them in two versions of the multi-robot emergency handling task. We describe an experimental setup to compare results obtained from a simulated grid world to those obtained from physical mobile robot experiments. Data resulting from eight hours of experiments with multiple mobile robots are compared to the trend identified in simulation. The data from the simulations show that there is no single strategy that produces best performance in all cases, and that the best task allocation strategy changes as a function of the noise in the system. This result is significant, and shows the need for further investigation of task allocation strategies and their application to planetary exploration. 相似文献
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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。 相似文献
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Intelligent Service Robotics - The problem of task allocation in a multi-robot system is the situation where we have a set of tasks and a number of robots; then each task is assigned to the... 相似文献