Research on Circular Area Search algorithm of multi-robot service based on SOA cloud platform

Considering autonomous mobile robots with a variety of specific functions as a kind of service, when there are many types and quantities of services and the density of regional services is large, proposing an algorithm of Circular Area Search (CAS) because of the problem of multi-robot service scheduling in various areas. Firstly, Django is used as the web framework to build the Service-Oriented Architecture (SOA) multi-robot service cloud platform, which is the basic platform for multi-service combination. Then, the service type, the latitude and longitude and the scoring parameters of the service are selected as the service search metrics to design the CAS algorithm that based on the existing service information registered in MySQL and the Gaode Map for screening optimal service, and then providing the service applicant with the best service. Finally, the service applicant applies for the self-driving tour service as an example to perform performance simulation test on the proposed CAS algorithm. The results show that the CAS algorithm of the multi-robot service cloud platform proposed in this paper is practical compared to the global search. And compared with the Greedy Algorithm experiment, the service search time is reduced about 58% compared with the Greedy Algorithm, which verifies the efficiency of CAS algorithm.     

The new organisation of work: Building coalitions

This article introduces the theme of the special issue, linking current concerns in European social and industrial relations policy with the research traditions covered byAI & Society. Human centredness, skill and technology, and the central importance of education and learning are emphasised as we build new development coalitions.     

基于群体智能的多机器人任务分配

针对具有松散和紧密耦合型任务的大规模多机器人系统,研究了基于群体智能的任务分配方法。系统采用层次结构,高层用蚁群算法实现松散耦合型任务分配的寻优,提出逆转分配思想让蚂蚁代表任务,为每个任务选择任务的承担者。底层分别提出了基于蚁群、粒子群蚁群和量子蚁群实现机器人联盟的形成——产生紧耦合型任务解,并进行仿真。仿真结果表明,基本蚁群算法得到的解质量最差;粒子群蚁群算法得到的分配解最好,但是运算时间最长;量子蚁群算法得到的解稍次于粒子群蚁群算法,但分配时间比另两种算法减少了一半。因此,在大规模的多机器人任务分配中,量子蚁群算法具有更强的适用性。     

Analysis of retailers’ coalition stability for supply chain based on LCS and stable set

To attain the general form of stable coalition structure, this paper addressed the problem of retailers’ coalition stability in a two-stage supply chain consisting of one supplier and multiple retailers. A profit gain function was established via introducing market gain coefficient and coalition cost coefficient for different coalition structures. Based on the function, the profit of each retailer in all kinds of coalition structures was analysed, and the general feature of a stable coalition structure was attained by the largest consistent set method and the stable set method. Furthermore, some insights were obtained. For example, stable coalition structures are equidistributed or approximate equidistributed; with supplier’s cost increasing, the size of the retailers’ coalition increases. Finally, the above conclusions are verified by numerical simulation. The results of this paper provide a reference for retailers’ coalition in a supply chain, such as automobile or Information Technology supply chain.     

Computing collision-free motions for a team of robots using formation and non-holonomic constraints

This paper focuses on the collision-free motion of a team of robots moving in a 2D environment with formation and non-holonomic constraints. With the proposed approach one can simultaneously control the formation of the team and generate a safe path for each individual robot. The computed paths satisfy the non-holonomic constraints, avoid collisions, and minimize the task-completion time. The proposed approach, which combines techniques from mathematical programming and CAD, consists of two main steps: first, a global team path is computed and, second, individual motions are determined for each unit. The effectiveness of the proposed approach is demonstrated using several simulation experiments.     

XCSG在多机器人强化学习中的应用

XCS分类器在解决机器人强化学习方面已显示出较强的能力,但在多机器人领域仅局限于MDP环境,只能解决环境空间较小的学习问题。提出了XCSG来解决多机器人的强化学习问题。XCSG建立低维的逼近函数,梯度下降技术利用在线知识建立稳定的逼近函数,使Q-表格一直保持在稳定低维状态。逼近函数Q不仅所需的存储空间更小,而且允许机器人在线对已获得的知识进行归纳一般化。仿真实验表明,XCSG算法很好地解决了多机器人学习空间大、学习速度慢、学习效果不确定等问题。     

Testbed results of an opportunistic routing for multi-robot wireless networks

Opportunistic routing is a candidate for multihop wireless routing where the network topology and radio channels vary rapidly. However, there are not many opportunistic routing algorithms that can be implemented in a real multihop wireless network while exploiting the node mobility. It motivates us to implement an opportunistic routing, random basketball routing (BR), in a real multi-robot network to see if it can enhance the capacity of the multihop network as mobility increases. For implementation purposes, we added some features, such as destination RSSI measuring, a loop-free procedure and distributed relay probability updating, to the original BR. We carried out the experiments on a real multi-robot network and compared BR with AODV combined with CSMA/CA (routing + MAC protocol). We considered both static and dynamic scenarios. Our experiments are encouraging in that BR outperforms AODV + CSMA/CA, particularly in dynamic cases; the throughput of BR is 6.6 times higher than that of AODV + CSMA/CA. BR with dynamic networks shows 1.4 times higher throughput performance than BR with static networks. We investigate the performance of BR in the large-scale network using NS-2 simulation. We verify the effect of node density, speed, destination beacon signal and loop-free procedure. According to the large-scale simulation, the end-to-end throughput grows with the node speed.     

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.     

A Rough Set Approach to Estimating the Game Value and the Shapley Value from Data

A value of a game v is a function which to each coalition S assigns the value v(S) of this coalition, meaning the expected pay–off for players in that coalition. A classical approach of von Neumann and Morgenstern [6] had set some formal requirements on v which contemporary theories of value adhere to. A Shapley value of the game with a value v [14] is a functional Φ giving for each player p the value Φ_p(v) estimating the expected pay-off of the player p in the game. Game as well as conflict theory have been given recently much attention on the part of rough and fuzzy set communities [11,8,1,4,7,2]. In particular, problems of plausible strategies [1] in conflicts as well as problems related to Shapley's value [3,2] have been addressed.We confront here the problem of estimating a value as well as Shapley's value of a game from a partial data about the game. We apply to this end the rough set ideas of approximations, defining the lower and the upper value of the game and, respectively, the lower and upper Shapley value. We also define a notion of an exact coalition, on which both values coincide giving the true value of the game; we investigate the structure of the family of exact sets showing its closeness on complements, disjoint sums, and intersections of coalitions covering the set of players. This work sets open a new area of rough set applications in mining constructs from data. The construct mined in this case are values as well as Shapley values of games.     

骨干边缘分离网络联盟博弈入流量规划

针对骨干边缘分离网络下自私性对边缘自治域入流量规划的影响,提出了基于联盟博弈的入流量规划模型. 该模型以网络开销作为入流量规划性能的评价指标,建立了网络开销与网络运营商收益的关系. 边缘自治域通过与其他自治域形成联盟,优化其入流量规划性能,提高其网络运营商的收益. 同时,分析了联盟核心的特点,并提出基于SHAPLEY值的收益分配方法. 仿真结果表明,基于联盟博弈的入流量规划方法能提高边缘自治域的入流量规划性能,基于SHAPLEY值的收益分配方法实现了加入联盟核心的边缘自治域收益的公平分配. 边缘自治域能否组成联盟受到运营商收益与入流量规划性能关系的影响.