基于组合拍卖的多机器人任务死锁解决方法

针对多机器人搜集任务中可能出现的任务死锁现象,采用基于组合拍卖的方法来解决这一问题。提出了一种拍卖树方法,用来解决组合拍卖计算量过大的问题。仿真实验结果表明该方法不仅能够消除多机器人的任务死锁,而且能够在解决死锁问题的同时优化多机器人任务分配结果。     

基于一种蚁群算法的多机器人动态感知任务分配

多机器人系统在具有任务聚集特征的动态感知任务环境下执行搜集任务时,存在着由于任务分配不当而引起的冲突加剧问题.针对这一问题,提出了一种基于排斥信息素型蚁群算法的多机器人任务自主分配方法.进行了未知非结构化环境下的多机器人协作搜集仿真实验.仿真结果表明,采用本文所提方法可以实现多机器人搜集任务的自主分配,有效减少机器人的空间冲突,尤其在机器人数量较多的情况下,更能显示出该方法的优势.     

生物交哺行为启发的群体机器人搜集行为研究

为了提高群体机器人系统的整体性能,受生物系统中普遍存在的交哺现象的启发,在原来多机器人系统的基本行为的基础上,提出了一种引入交哺行为的多机器人协作机制。机器人依靠有限的感知能力和局部交互功能,以自组织方式执行目标搜集任务。机器人的内部状态变量反映其执行任务的情况以及对环境和其他机器人的评价。比较机器人的内部状态变量,可以判断是否需要交哺和交哺的方向性。主要目的是减少机器人之间的冲突,降低系统能量消耗的同时,提高机器人搜集目标的效率。最后通过计算机仿真实验以及与其他多机器人协作方法比较,分析该方法对提高系统性能的有效性。     

基于人工免疫系统的异构多机器人联盟形成

对动态环境下多机器人联盟形成问题进行了研究,提出了一种基于人工免疫系统的异构多机器人联盟形成方法.该方法在对比人工免疫系统与多机器人系统相似关系的基础上,利用人工免疫系统的隐喻机制为面向动态感知任务的异构多机器人联盟形成问题提供了一种新的思路和解决方法.进行了未知非结构化环境下的多机器人协作搜集仿真实验,仿真结果表明所提方法可以使多机器人系统自主地形成机器人联盟以完成动态感知任务,提高了多机器人系统执行任务的效率.     

基于强化学习的未知环境多机器人协作搜集

针对多机器人协作复杂搜集任务中学习空间大,学习速度慢的问题,提出了带共享区的双层强化学习算法。该强化学习算法不仅能够实现低层状态-动作对的学习,而且能够实现高层条件-行为对的学习。高层条件-行为对的学习避免了学习空间的组合爆炸,共享区的应用强化了机器人间协作学习的能力。仿真实验结果说明所提方法加快了学习速度,满足了未知环境下多机器人复杂搜集任务的要求。     

多机器人协作任务的仿真设计研究

多机器人协作问题已成为机器人研究领域的一个热点,而仿真研究是多机器人协作问题的重要研究手段.为了给多机器人协作任务提供评断依据,基于对MuRoS仿真平台的扩充提出了一套针对多机器人协作任务仿真的完整设计思路.首先分析了MuRoS多机器人任务模拟环境的结构框架和功能特点,描述了它执行仿真任务的工作流程.然后提出了几种常用虚拟控制器的接口设计方法.最后,通过几个典型的协作任务的仿真实例合理调用虚拟控制器,得到了满意的运行效果,验证了设计思路的有效性.     

基于改进遗传算法的多机器人任务分配方法

多点遍历任务是多机器人协作研究中常用的一个例子,普遍采用的单物品拍卖任务分配方法存在着无法得到全局最优解的缺点。为解决此问题,该文提出用组合拍卖的方法进行任务分配。由于组合拍卖的胜者决定问题（WDP）本身是一个NP—hard问题,怎样在短时间内获得令人满意的解是该文关心的焦点。该文通过对单亲遗传算法进行改进,提出环形染色体的概念,成功地解决了针对此任务的WDP求解问题。仿真试验表明,该算法实现简单,搜索效率高,在较短时间内能够得到满意解,满足多机器人动态任务分配的实时性要求。     

起伏地形环境中多移动机器人协作运输策略

对多机器人协作运输时环境地形起伏不平,运输过程中机器人和物体发生滑动的问题,提出了起伏地形环境中的协作运输策略.将观测者—推动者模式的多机器人协作推箱子任务转换为多机器人编队任务,通过基于几何规划的虚拟目标序列跟踪方法实现被推动物体脱离运动方向时运输编队的重新形成.协作运输策略中的观测者机器人采用平缓地形优先(navigate plain first to destination,NPFD)导航策略指导编队运动.     

基于积极团队情感基调的情感机器人协作任务分配拍卖算法

多机器人系统(Multi Robot System,MRS)通过引入机器人个体情感因素,可以有效提高个体的自主协作能力、决策能力以及多机器人系统的整体智能化水平。然而,以往研究主要集中于个体情感状态(情绪、个性等),缺乏从团队情感层面来探索积极团队情感基调(Positive Group Affective Tone,PGAT)对团队协作能力和团队有效性的影响。为了发挥PGAT在任务分配中的积极作用,降低因为团队成员情绪衰减而导致团队解散的风险,并增加团队协作能力和团队有效性,提出了基于PGAT的情感机器人协作任务分配拍卖算法。仿真追捕对比实验表明,相对于基于焦虑情感模型的改进合同网协议多机器人任务分配算法和基于自主意识的分布式情感机器人任务分配算法,基于PGAT的情感机器人协作任务分配拍卖算法的追捕成功率分别提高了269.3%和6.5%,任务分配成功率分别提高了138.7%和5.1%,平均追捕时间分别缩短了14.5%和26.3%,并且在150场追捕对比实验中,追捕时间小于对比算法的场次占比分别达到87.3%和90.7%。     

基于拍卖的多智能体任务分配算法

任务分配是多智能体系统协作关键问题,对此提出一种基于拍卖的多智能体任务分配算法.在限定时间内,利用拍卖算法综合考虑完成任务的效益及各智能体完成任务需付出的代价,得到接近最优的任务分配方案.动态的环境会造成方案滞后,通过动态调整,重要任务可吸引到更多智能体.为减轻动态调整过程的通信负担,引入令牌的概念,由令牌控制智能体发送信息的权利.通过在机器人救援仿真系统中的成功应用,验证了本算法的有效性.     

Dynamic task allocation for multi-robot search and retrieval tasks

Many application domains require search and retrieval, which is also known in the robotic domain as foraging. For example, in a search and rescue domain, a disaster area needs to be explored and transportation of survivors to a safe area needs to be arranged. Performing such a search and retrieval task by more than one robot increases performance if they are able to distribute their workload efficiently and evenly. In this work, we study the Multi-Robot Task Allocation (MRTA) problem in the search and retrieval domain, where a team of robots is required to cooperatively search for targets of interest in an environment and also retrieve them back to a home base. In comparison with typical foraging tasks, we look at a more general search and retrieval task in which the targets are distinguished with various types, and task allocation also requires taking into account temporal constraints on the team goal. As usual, robots have no prior knowledge about the location of targets in the environment but in addition they need to deliver targets to the home base in a specific order according to their types, which significantly increases the complexity of a foraging problem. We first use a graph-based model to analyse the search and retrieval problem and the dynamics of exploration and retrieval within a cooperative team. We then proceed to present an extended auction-based approach, as well as a prediction approach. The essential difference between these two approaches is that the task allocation and execution procedures in the auction approach are running in parallel, whereas a robot in the prediction approach only needs to choose a task to perform when it has no thing to do. The auction approach uses a winner determination mechanism to allocate tasks to each robot, whereas the robots in the prediction approach implicitly coordinate their activities by team reasoning that leads to consensuses about task allocation. We use the Blocks World for Teams (BW4T) simulator to evaluate the two approaches in our experimental study.     

一种栅格模型下机器人路径规划的改进蚁群算法

提出了一种静态环境下机器人路径规划的改进蚁群算法。该算法使用栅格法对机器人的工作空间进行建模,通过模拟蚂蚁的觅食行为,采用折返的迭代方式对目标进行搜索。在搜索过程中,以移动方向一定范围内最大信息素和目标引导函数作为启发式因子。此外,根据蚁群算法处理本问题时信息素散播的特点,重构了信息素的更新策略和散播方式。仿真实验结果表明,这些改进措施使最优路径的寻找快速而高效,即使在障碍物非常复杂的环境下,也能迅速地规划出一条最优路径。     

Procurement decision making mechanism of divisible goods based on multi-attribute auction

Multi-attribute auction enables negotiation on several attributes in addition to the price such as quality, quantity, time of delivery and service levels. Most of the existing multi-attribute auction mechanisms are designed by considering a unique good or indivisible multiple goods. This paper focuses on designing a multi-attribute auction mechanism for addressing the decision making problem of multi-attribute and multi-source procurement of a kind of homogeneous continuous divisible goods (such as coal, oil, electricity and gas). The suppliers’ optimal bidding strategies are discussed, and a bidding method named the minimum bid increment method is proposed to simulate the overall process of suppliers’ multiple rounds of bidding. Theoretical analysis shows that our auction mechanism is an efficient mechanism, and satisfies the incentive compatibility conditions and the individual rationality conditions. Moreover, a multi-attribute auction example about the steam coal procurement is given to show how to implement our multi-attribute auction mechanism.     

工业机器人时间最优轨迹规划及轨迹控制的理论与实验研究

提出了一种用于工业机器人时间最优轨迹规划及轨迹控制的新方法, 它可以确保在关节位移、速度、加速度以及二阶加速度边界值的约束下, 机器人手部沿笛卡尔空间中规定路径运动的时间最短. 在这种方法中, 所规划的关节轨迹都采用二次多项式加余弦函数的形式, 不仅可以保证各关节运动的位移、速度、加速度连续而且还可以保证各关节运动的二阶加速度连续. 采用这种方法, 既可以提高机器人的工作效率又可以延长机器人的工作寿命. 以PUMA 5 6 0机器人为对象进行了计算机仿真和机器人实验, 结果表明这种方法是正确和有效的. 它为工业机器人在非线性运动学约束条件下的时间最优轨迹规划及控制问题提供了一种较好的解决方案.     

基于栅格模型机器人路径规划的改进蚁群算法

提出了一种静态环境下机器人路径规划的改进蚁群算法.该算法使用栅格法对机器人的工作空间进行建模,通过模拟蚂蚁的觅食行为,采用折返的迭代方式对目标进行搜索;在搜索过程中,以移动方向一定范围内最大信息素和目标引导函数作为启发式因子;此外,根据蚁群算法处理本问题时信息素散播的特点,重构了信息素的更新策略和散播方式.仿真试验结果表明,改进措施使最优路径的寻找快速而高效,即使在障碍物非常复杂的环境下,算法也能迅速地规划出一条最优路径.     

基于多Agent的分布式通信对抗目标分配系统

针对通信对抗目标分配的特点,提出一种基于多Agent的分布式目标分配系统。给出问题描述和模型建立的方法,设计分布式协同拍卖机制和分布协同拍卖算法,其中,分布协同拍卖算法包括竞拍序列禁忌规则、目标威胁度更新规则等。仿真结果表明,与遗传算法相比,该算法收敛速度较快、实时性较强。     

About control of electricity market

We consider auction problem for network of inter-connected energy markets. The conditions of optimality for this problem are derived. The numerical method based on these conditions for running auction is constructed. It is shown how running auction can be achieved by using automated control system.     

Mathematical Model of Foraging in a Group of Robots: Effect of Interference

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.     

An Autonomous Water Vapor Plume Tracking Robot Using Passive Resistive Polymer Sensors

A simple reactive robot is described which is capable of tracking a water vapor plume to its source. The robot acts completely within the plume and is endowed with no deliberate information about wind direction or speed, yet accurately tracks the plume upstream. The robot's behavior, results from the behavior of simple resistive polymer sensors and their strategic placement on the robot's body.