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针对多机器人远程监控系统信息错综复杂、协作不稳定的问题,建立基于多智能体(Multi-Agent)技术的系统体系结构,描述系统各组件之间的信息交互关系,优化人-机智能分配。分析系统物理结构的特点,提出一种基于Multi-Agent技术的共享控制系统分层体系结构,结合黑板结构和点对点结构,给出Multi-Agent的混合通信模型。针对遥操作系统的特点,设计一种混合型Agent体系结构,举例研究Agent的实现方法。通过多操作者控制机器人保持队形的实验,验证了该混合型Agent体系结构的实用性和有效性。 相似文献
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BDI模型是智能体设计的一种成熟结构,本文将BDI模型应用于多机器人智能体系统设计中.文章先从形式逻辑角度描述系统模型,然后讨论基于合同网的多机器人智能体的协作机制,最后给出基于BDI模型的多机器智能体的实现模型. 相似文献
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基于协作协进化的多智能体机器人协作研究 总被引:2,自引:0,他引:2
协作问题一直是自主多智能体机器人系统研究的关键问题之一。基于多智能体机器人系统的CCP协作协议所生成的各智能体机器人的任务序列依赖于目标的初始顺序,因此难以得到最优解。文章提出了利用协作协进化来实现多智能体机器人之间协作的一种机制。该方法采用基于协作种群的技术来生成多智能体机器人任务执行序列,在给定的任务分解产生的所有可能解中寻找最优解,并通过交换局部知识和并行决策等手段来优化系统的性能。利用该机制,对3个智能体协作搬运8个物体进行计算机模拟,结果表明,该机制在优化任务执行序列方面作用明显,从而能有效提高多智能体机器人系统的性能。 相似文献
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作为自动化和智能化时代的代表,机器人技术的发展成为智能控制领域研究的焦点,各种基于机器人的智能控制技术应运而生,机器人被越来越多地应用于实现与环境之间的复杂多接触交互任务.本文以机器人复杂多接触交互任务为核心问题展开讨论,结合基于强化学习的机器人智能体训练相关研究,对基于强化学习方法实现机器人多接触交互任务展开综述.概述了强化学习在机器人多接触任务研究中的代表性研究,当前研究中存在的问题以及改进多接触交互任务实验效果的优化方法,结合当前研究成果和各优化方法特点对未来机器人多接触交互任务的智能控制方法进行了展望. 相似文献
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基于人工神经网络的多机器人协作学习研究 总被引:5,自引:0,他引:5
机器人足球比赛是一个有趣并且复杂的新兴的人工智能研究领域,它是一个典型的多智能体系统。文中主要研究机器人足球比赛中的协作行为的学习问题,采用人工神经网络算法实现了两个足球机器人的传球学习,实验结果表明了该方法的有效性。最后讨论了对BP算法的诸多改进方法。 相似文献
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Design Agents with Sharing Learning Mechanism 总被引:1,自引:0,他引:1
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In multi-robot applications, such as foraging or collection tasks, interference, which results from competition for space between spatially extended robots, can significantly affect the performance of the group. We present a mathematical model of foraging in a homogeneous multi-robot system, with the goal of understanding quantitatively the effects of interference. We examine two foraging scenarios: a simplified collection task where the robots only collect objects, and a foraging task, where they find objects and deliver them to some pre-specified home location. In the first case we find that the overall group performance improves as the system size grows; however, interference causes this improvement to be sublinear, and as a result, each robot's individual performance decreases as the group size increases. We also examine the full foraging task where robots collect objects and deliver them home. We find an optimal group size that maximizes group performance. For larger group sizes, the group performance declines. However, again due to the effects of interference, the individual robot's performance is a monotonically decreasing function of the group size. We validate both models by comparing their predictions to results of sensor-based simulations in a multi-robot system and find good agreement between theory and simulations data. 相似文献
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A rearrangement problem involving multiple mobile robots is addressed in this paper. In the problem, it is important to identify task decomposition, task allocation, and path planning applicable to distinct environments while rearrangement tasks are executed. We here define ‘task apportionment’ as an operation that sets up task decomposition and conducts task allocation based on that setup. We propose a method for task apportionment and path planning applicable to distinct environments. The method establishes the necessary intermediate configurations of objects as one way of task decomposition and determines task allocation and path planning as a semi-optimized solution by using simulated annealing. The proposed method is compared with a continuous transportation method and a territorial method through simulations and experiments. In the simulations, the proposed method is, on the average, 17 and 20% faster than the continuous transportation method and the territorial method, respectively. In the experiments, the proposed method is, on the average, 22 and 16% faster than the continuous transportation and the territorial method, respectively. These results show that the proposed method can realize an efficient rearrangement task by mobile robots in various working environments under feasible computation time, especially in environments with a mixture of wide and narrow areas and an uneven distribution of objects. 相似文献
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Given a collection of parameterized multi-robot controllers associated with individual behaviors designed for particular
tasks, this paper considers the problem of how to sequence and instantiate the behaviors for the purpose of completing a
more complex, overarching mission. In addition, uncertainties about the environment or even the mission specifications
may require the robots to learn, in a cooperative manner, how best to sequence the behaviors. In this paper, we approach this
problem by using reinforcement learning to approximate the solution to the computationally intractable sequencing problem,
combined with an online gradient descent approach to selecting the individual behavior parameters, while the transitions
among behaviors are triggered automatically when the behaviors have reached a desired performance level relative to a task
performance cost. To illustrate the effectiveness of the proposed method, it is implemented on a team of differential-drive
robots for solving two different missions, namely, convoy protection and object manipulation. 相似文献
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The current trends in the robotics field have led to the development of large-scale multiple robot systems, and they are deployed for complex missions. The robots in the system can communicate and interact with each other for resource sharing and task processing. Many of such systems fail despite the availability of necessary resources. The major reason for this is their poor coordination mechanism. Task planning, which involves task decomposition and task allocation, is paramount in the design of coordination and cooperation strategies of multiple robot systems. Task allocation mechanism allocates the task in a mission to the robots by maximizing the overall expected performance, and thereby reducing the total allocation cost for the team. In this paper, we formulate a heuristic search-based task allocation algorithm for the task processing in heterogeneous multiple robot system, by maximizing the efficiency in terms of both communication and processing cost. We assume a set of decomposed tasks of a mission, which needs to be allocated to the robots. The near-optimal allocation schemes are found using the proposed peer structure algorithm for the given problem, where the number of the tasks is more than the robots present in the system. The cost function is the summation of static overhead cost of robots, assignment cost, and the communication cost between the dependent tasks, if they are assigned to different robots. Experiments are performed to verify the effectiveness of the algorithm by comparing it with the existing methods in terms of computational time and quality of solution. The experimental results show that the proposed algorithm performs the best under different problem scales. This proves that the algorithm can be scaled for larger system and it can work for dynamic multiple robot system. 相似文献
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Task allocation is an important aspect of multiagent coordination. However, there are many challenges in developing appropriate strategies for multiagent teams so that they operate efficiently. Real‐world scenarios such as flooding disasters usually require the use of heterogeneous robots and the execution of tasks with different structures and complexities. In this paper, we propose a decentralized task allocation mechanism considering different types of tasks for heterogeneous agent teams where agents play different roles and carry out tasks according to their own capabilities. We have run several experiments to evaluate the proposed mechanism. The results show that the proposed mechanism appears to scale well and provides near‐optimal allocations. 相似文献
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《Robotics and Autonomous Systems》2007,55(7):572-588
This paper describes an adaptive task assignment method for a team of fully distributed mobile robots with initially identical functionalities in unknown task environments. A hierarchical assignment architecture is established for each individual robot. In the higher hierarchy, we employ a simple self-reinforcement learning model inspired by the behavior of social insects to differentiate the initially identical robots into “specialists” of different task types, resulting in stable and flexible division of labor; on the other hand, in dealing with the cooperation problem of the robots engaged in the same type of task, Ant System algorithm is adopted to organize low-level task assignment. To avoid using a centralized component, a “local blackboard” communication mechanism is utilized for knowledge sharing. The proposed method allows the robot team members to adapt themselves to the unknown dynamic environments, respond flexibly to the environmental perturbations and robustly to the modifications in the team arising from mechanical failure. The effectiveness of the presented method is validated in two different task domains: a cooperative concurrent foraging task and a cooperative collection task. 相似文献
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Vadakkepat P. Ooi Chia Miin Xiao Peng Tong Heng Lee 《Fuzzy Systems, IEEE Transactions on》2004,12(4):559-565
An extensive fuzzy behavior-based architecture is proposed for the control of mobile robots in a multiagent environment. The behavior-based architecture decomposes the complex multirobotic system into smaller modules of roles, behaviors and actions. Fuzzy logic is used to implement individual behaviors, to coordinate the various behaviors, to select roles for each robot and, for robot perception, decision-making, and speed control. The architecture is implemented on a team of three soccer robots performing different roles interchangeably. The robot behaviors and roles are designed to be complementary to each other, so that a coherent team of robots exhibiting good collective behavior is obtained. 相似文献
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The emergence of service robots in our environment raises the need to find systems that help the robots in the task of managing the information from human environments. A semantic model of the environment provides the robot with a representation closer to the human perception, and it improves its human-robot communication system. In addition, a semantic model will improve the capabilities of the robot to carry out high level navigation tasks. This paper presents a semantic relational model that includes conceptual and physical representation of objects and places, utilities of the objects, and semantic relation among objects and places. This model allows the robot to manage the environment and to make queries about the environment in order to do plans for navigation tasks. In addition, this model has several advantages such as conceptual simplicity and flexibility of adaptation to different environments. To test the performance of the proposed semantic model, the output for the semantic inference system is associate to the geometric and topological information of objects and places in order to do the navigation tasks. 相似文献
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《Advanced Robotics》2013,27(11-12):1365-1383
A rearrangement task of multiple objects is discussed here. In this paper, robots that carry more than one object simultaneously are referred to as multi-task functional, whereas a standard single-task robot carries one object at a time. With multi-task functional robots, the total length of transfer paths is shortened and the processing time is reduced. We propose a planning algorithm that consists of simulated annealing and a scheduling method using prioritization rules for a group of multi-task functional robots. We also propose a planning methodology on synchronization timing between the robots. Experiments involving two robots were conducted in real and simulated environments to show the effectiveness of the proposed algorithms. 相似文献