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.     

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.     

MRoCS: A new multi-robot communication system based on passive action recognition

Multi-robot search-and-rescue missions often face major challenges in adverse environments due to the limitations of traditional implicit and explicit communication. This paper proposes a novel multi-robot communication system (MRoCS), which uses a passive action recognition technique that overcomes the shortcomings of traditional models. The proposed MRoCS relies on individual motion, by mimicking the waggle dance of honey bees and thus forming and recognising different patterns accordingly. The system was successfully designed and implemented in simulation and with real robots. Experimental results show that, the pattern recognition process successfully reported high sensitivity with good precision in all cases for three different patterns thus corroborating our hypothesis.     

Local interactions over global broadcasts for improved task allocation in self-organized multi-robot systems

We present a study of self-organized multi-robot task-allocation, examining performance under local and centralized communication strategies. The results extend our current understanding of the effects of communication by providing evidence that local strategies can improve system performance over centralized strategies, in terms of total task throughput as well as reduced communication overheads. The framework employed is the attractive field model, a generic model of self-organized division of labour derived from observations of ant, human and robot social systems. The framework provides sufficient abstraction to accommodate both communication strategies. Each of the studies used 16 e-puck robots in a simplified manufacturing environment where sensing and communication was realized using camera-based overhead tracking and centralized communication. In terms of task throughput, communication overhead and energy efficiency, the experimental results show that systems with restricted access to information perform better than systems with free flow of information. This suggests a potential paradigm shift where, for self-organizing systems, diminishing access to information renders a system more efficient.     

An optimal algorithm for two robots path planning problem on the grid

This paper is a study on the problem of path planning for two robots on a grid. We consider the objective of minimizing the maximum path length which corresponds to minimizing the arrival time of the last robot at its goal position. We propose an optimal algorithm that solves the problem in linear time with respect to the size of the grid. We show that the algorithm is complete; meaning that it is sure to find an optimal solution or report if any does not exist.     

智能纱架多机械手换纱系统设计

智能纱架多机械手换纱系统集绕纱、搬运、中转、续纱机械手为一体,采用集中管理、分布控制的思想,设计了基于CAN总线的多机械手分布式控制系统,通过Lab VIEW软件实现了人机界面的交互完成对换纱系统的操作,并对系统中的核心部分续纱机械手进行合理的路径规划,使其行走路径尽可能短,进一步节省换纱时间,该系统实现了阿克明斯特地毯织机的换纱自动化,节约了人工和时间成本,提高了地毯织造的效率。