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.     

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。     

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.     

基于顺序扩展一致性包算法的多无人机分布式任务分配

以异构多无人机协同执行复杂的耦合多任务为背景,提出一种求解分布式任务分配问题非死锁的顺序扩展一致性包算法.首先,建立考虑任务载荷资源、任务时序、威胁区等约束条件的时序多任务分配模型;其次,对一致性包算法的任务包构建过程和冲突消解规则进行扩展,并设计一种基于有向图深度优先搜索的方法进行任务方案的死锁检测和修正,以实现无冲突和无死锁的任务分配;然后,将关联任务之间的时序约束转化为软时间窗约束,利用顺序分层的策略进行求解;最后,为了提高任务分配结果的可靠性,采用Dubins曲线路径将航路规划耦合到任务分配中.仿真实验表明,所提出的算法能够快速有效地求解异构多无人机分布式耦合多任务分配问题,具备良好的最优性和时效性.     

Hadoop任务分配策略的改进

Hadoop广泛应用于大数据的并行处理,其现有的任务分配策略多面向同构环境,或者没有充分利用集群的全局信息,或者在异构环境下无法兼顾执行效率与算法复杂度。针对这些问题,提出异构环境下的任务分配算法λ-Flow算法,将原先一次完成的任务分配过程划分成多轮,每轮基于当前集群状态,以及上轮任务的执行情况,动态进行任务分配,直至全部任务分配结束,以期达到最优执行效率。通过与其他算法对比实验表明,λ-Flow算法能够更好地适应集群的动态变化,有效减少作业执行时间。     

分布式多机器人通信仿真系统

针对目前多机器人通信仿真系统较少的问题,进行了多机器人通信仿真系统的设计研究．提出的多移动机器人通信仿真系统设计方案,侧重于反映通信网络的拓扑变化情况,以及多个机器人之间是如何进行通信的．仿真系统预留了机器人控制算法的接口,便于结合机器人避碰、任务分配、连通覆盖等进行综合研究．多机器人覆盖研究是目前多移动机器人和无线传感器网络中的一个研究热点,针对这个问题,采用了虚拟力分配策略,使得多机器人在保持连通性的同时尽可能大地覆盖某一区域,最后以六边形覆盖为约束条件进行了区域覆盖,并实现了该仿真系统的原型．实验表明,该仿真系统能准确地模拟多机器人在保持相互通信的情况下,达到最大化的区域覆盖．证实了基于虚拟力覆盖策略的有效性．     

基于主从结构的多水下机器人协同路径规划

关于多水下机器人协同路径规划问题,是多水下机器人协同控制的重要研究内容之一,是一种典型的含多个约束条件的组合优化问题.针对多机器人协同路径规划因约束条件多导致算法复杂度高、耗时、求解困难等问题,提出了一种主从结构的并行多水下机器人协同路径规划算法.进化过程的每一代,子层结构应用粒子群并行算法,生成各架机器人当前的最优路径,同时,主层结构应用微分进化算法实时给出当前考虑机器人与障碍物、机器人与机器人之间避碰情况下,总系统运行时间最短的路径组合方案.上述结构将多约束分解到不同层面,有效地降低了单层结构因过多的约束条件计算时间过长以及不易实现等困难.仿真结果表明,上述算法不仅能在静态环境下生成可行的、优化的组合路径,而且在当障碍物随时间随机移动的动态环境下,也表现出可行的、良好的效果,为求解多水下机器人协同路径规划问题提供了一个高效的解决方案.     

基于多目标粒子群优化算法的装备维修任务分配

装备维修任务分配问题是典型的多约束/多目标/非线性规划问题,利用传统方法无法求解,因此提出了一种约束多目标粒子群算法,并运用该算法对装备维修任务分配问题进行了优化求解。仿真结果表明,约束多目标粒子群算法针对该问题,在不同参数和约束条件下都有很强的收敛寻优能力,能快速产生多个非支配解,是一种高效的算法,对实现装备维修任务分配的客观量化优化决策有重要作用。     

基于资源拍卖的农业多机器人任务分配

针对农田环境中多机器人协同作业的问题,提出一种基于资源的任务分配算法,用于在具有机器人资源的再填充站的长期任务中高效地执行多个任务.针对多机器人任务分配问题,对多机器人任务进行建模,并分析任务相关模型及任务能量指标.在进行拍卖算法任务分配时,在考虑机器人数目约束、工作时间约束、距离约束的基础上,加入任务执行能力的约束,考虑机器人在长期任务执行期间资源量消耗问题,使各个农机有序地为农田地块服务,降低整个系统的执行代价,提高任务完成量.利用MATLAB平台进行仿真实验,生成多机器人多任务点的分配优化结果,并设置多组不同数量的机器人,对比该算法同其他三种算法的效果.仿真结果表明,该算法可以有效地提高作业效率,在相同条件下使资源消耗量及任务完成量达到最优,证明了其优越性,同时计算结果与实际作业完成量更接近,提高了结果的精准性.     

基于高阶多项式的爬游机器人足端轨迹规划

针对深海爬游机器人足端轨迹规划问题,采用高阶多项式拟合的方法对其进行研究;首先,介绍了爬游机器人整体结构并对其进行运动学建模,结合爬游机器人运动学模型提出了一种直线与曲线相结合的机器人足端轨迹;其次,利用四阶多项式和六阶多项式分别对机器人足端轨迹进行拟合,比较两种拟合结果可知,六阶多项式拟合方法对机器人足端速度、加速度的规划效果更佳;利用六阶多项式轨迹拟合方法对多段轨迹连接点处的速度问题进行了分析,解决了机器人在运动过程中腿部抖动问题,使机械腿具有良好的控制柔顺性;最后,根据D-H法则建立机器人单腿仿真模型,通过仿真验证了算法的可行性,进一步在水池中利用机器人实物样机验证了算法的有效性.     

An algorithm for cooperative task allocation in scalable,constrained multiple robot systems

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.     

多自治水下机器人多任务分配的自组织算法

针对自治水下机器人（AUV）研究中的多机器人多任务分配问题,提出一种基于自组织映射（SOM）神经网络的多AUV多目标分配策略.将目标点的位置坐标作为SOM神经网络的输入向量进行自组织竞争计算,输出为对应的AUV机器人,从而控制一组AUV在不同的地点完成不同的任务,使机器人按照优化的路径规则到达指定的目标位置.为了表明所提出算法的有效性,给出了二维、三维作业环境中的仿真实验结果.     

Multi-AUV SOM task allocation algorithm considering initial orientation and ocean current environment

There is an ocean current in the actual underwater working environment. An improved self-organizing neural network task allocation model of multiple autonomous underwater vehicles (AUVs) is proposed for a three-dimensional underwater workspace in the ocean current. Each AUV in the model will be competed, and the shortest path under an ocean current and different azimuths will be selected for task assignment and path planning while guaranteeing the least total consumption. First, the initial position and orientation of each AUV are determined. The velocity and azimuths of the constant ocean current are determined. Then the AUV task assignment problem in the constant ocean current environment is considered. The AUV that has the shortest path is selected for task assignment and path planning. Finally, to prove the effectiveness of the proposed method, simulation results are given.

     

A Neural Network Approach to Dynamic Task Assignment of Multirobots

In this paper, a neural network approach to task assignment, based on a self-organizing map (SOM), is proposed for a multirobot system in dynamic environments subject to uncertainties. It is capable of dynamically controlling a group of mobile robots to achieve multiple tasks at different locations, so that the desired number of robots will arrive at every target location from arbitrary initial locations. In the proposed approach, the robot motion planning is integrated with the task assignment, thus the robots start to move once the overall task is given. The robot navigation can be dynamically adjusted to guarantee that each target location has the desired number of robots, even under uncertainties such as when some robots break down. The proposed approach is capable of dealing with changing environments. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies.     

Optimal design of a stair-climbing mobile robot with flip mechanism

Stairs overcoming is a primary challenge for mobile robots moving in human environments, and the contradiction between the portability and the adaptability of stair climbing robot is not well resolved. In this paper, we present an optimal design of a flip-type mobile robot in order to improve the adaptability as well as stability while climbing stairs. The kinematic constraints on the flip mechanism are derived to prevent undesired interferences among stairs, wheels and main body during climbing stairs. The objective function is proposed according to the traction demand of the robot during stair-climbing motion for the first time and the value of the objective function is calculated though kinetic analysis. The Taguchi method is using as the optimization tool because of its simplicity and cost-effectiveness both in formulating an objective function and in satisfying multiple constraints simultaneously. The performance of the robot under the optimal parameters is verified through simulations and experiments.     

Coordinating heterogeneous teams of robots using temporal symbolic planning

The efficient coordination of a team of heterogeneous robots is an important requirement for exploration, rescue, and disaster recovery missions. In this paper, we present a novel approach to target assignment for heterogeneous teams of robots. It goes beyond existing target assignment algorithms in that it explicitly takes symbolic actions into account. Such actions include the deployment and retrieval of other robots or manipulation tasks. Our method integrates a temporal planning approach with a traditional cost-based planner. The proposed approach was implemented and evaluated in two distinct settings. First, we coordinated teams of marsupial robots. Such robots are able to deploy and pickup smaller robots. Second, we simulated a disaster scenario where the task is to clear blockades and reach certain critical locations in the environment. A similar setting was also investigated using a team of real robots. The results show that our approach outperforms ad-hoc extensions of state-of-the-art cost-based coordination methods and that the approach is able to efficiently coordinate teams of heterogeneous robots and to consider symbolic actions.     

基于交换树的多机器人任务协调与负荷平衡方法

针对多机器人任务分工与协调过程中,未能有效解决的带任务偏序关系的负荷平衡问题,提出一种基于交换树的多机器人任务协调与负荷平衡方法。首先,通过有向赋权图（约束图）对带偏序关系约束的多机器人任务分工问题进行描述;其次,根据有向赋权图提出了初始任务分工策略,通过改进Dijkstra算法解决多机器人之间任务协调问题;最后,提出负荷平衡策略,通过交换树竞拍的方法解决机器人之间任务负荷不平衡问题。仿真结果表明,与一般Dijkstra方法相比,执行完任务负荷平衡策略之后,工作效率明显提高了12%,机器人之间的任务负荷差也减少了30%,验证了该方法的有效性。     

Adaptive task assignment for multiple mobile robots via swarm intelligence approach

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.     

COBOS: Cooperative backoff adaptive scheme for multirobot task allocation

In this paper, the cooperative backoff adaptive scheme (COBOS) is proposed for task allocation amongst a team of heterogeneous robots. The COBOS operates in regions with limited communication ranges, and is robust against robot malfunctions and uncertain task specifications, with each task potentially requiring multiple robots. The portability of tasks across teams (or when team demography changes) is improved by specifying tasks using basis tasks in a matrix framework. The adaptive feature of COBOS further increases the flexibility of robot teams, allowing robots to adjust their actions based on past experience. In addition, we study the properties of COBOS: operation domain; communication requirements; computational complexity; and solution quality; and compare the scheme with other task-allocation mechanisms. Realistic simulations are carried out to verify the effectiveness of the proposed scheme.     

Using communication to reduce locality in distributed multiagent learning

Abstract. This paper attempts to bridge the fields of machine learning, robotics, and distributed AI. It discusses the use of communication in reducing the undesirable effects of locality in fully distributed multi-agent systems with multiple agents robots learning in parallel while interacting with each other. Two key problems, hidden state and credit assignment, are addressed by applying local undirected broadcast communication in a dual role: as sensing and as reinforcement. The methodology is demonstrated on two multi-robot learning experiments. The first describes learning a tightly-coupled coordination task with two robots, the second a loosely-coupled task with four robots learning social rules. Communication is used to (1) share sensory data to overcome hidden state and (2) share reinforcement to overcome the credit assignment problem between the agents and bridge the gap between local individual and global group pay-off.