Control configurations in buyer-supplier relationships: environment- buyer organisation- goals and modes of control

Considering the growing externalisation of strategic activities, the problem of the control of buyer-supplier relationships is crucial. Therefore, researchers usually propose modes of control that are adapted to various environments. However, the organisations are often considered as “black boxes” whose goals are unspecified. This paper examines buyer-supplier control configurations that take into account the organisation of buying firms and their goals toward their suppliers. This research is based on six case studies conducted in the manufacturing industry (60 interviews). The outcome of the research is a matrix which represents four configurations of buyer-supplier control, based on the global purchasing environment of the buying firm (in terms of reciprocal dependence between the buyer and its suppliers). For each configuration, a type of purchasing organisation (structure and intra-organisational control of purchasing agents) and a principal goal for the buying firm are proposed: the lord-buyer wants to exert its power, the partner-buyer aims at assuring goal congruence with its suppliers, the vassal-buyer tries to reduce uncertainty and the market-buyer seeks to grasp opportunities on the market. For each configuration, the modes of control that the buyer exerts on its suppliers –in terms of means, objects of control, influence strategies of the buyer (more or less coercive) and suppliers reactions- are coherent with the main goal of the buyer.interorganisational control; buyer-supplier relationship; power; dependence; Goals of control

Communication Efficiency in Information Gathering through Dynamic Information Flow

This thesis addresses the problem of how to improve the performance of multi-robot information gathering tasks by actively controlling the rate of communication between robots. Examples of such tasks include cooperative tracking and cooperative environmental monitoring. Communication is essential in such systems for both decentralised data fusion and decision making, but wireless networks impose capacity constraints that are frequently overlooked. While existing research has focussed on improving available communication throughput, the aim in this thesis is to develop algorithms that make more efficient use of the available communication capacity. Since information may be shared at various levels of abstraction, another challenge is the decision of where information should be processed based on limits of the computational resources available. Therefore, the flow of information needs to be controlled based on the trade-off between communication limits, computation limits and information value. In this thesis, we approach the trade-off by introducing the dynamic information flow (DIF) problem. We suggest variants of DIF that either consider data fusion communication independently or both data fusion and decision making communication simultaneously. For the data fusion case, we propose efficient decentralised solutions that dynamically adjust the flow of information. For the decision making case, we present an algorithm for communication efficiency based on local LQ approximations of information gathering problems. The algorithm is then integrated with our solution for the data fusion case to produce a complete communication efficiency solution for information gathering. We analyse our suggested algorithms and present important performance guarantees. The algorithms are validated in a custom-designed decentralised simulation framework and through field-robotic experimental demonstrations

Enabling Parallel Wireless Communication in Mobile Robot Teams

Wireless inter-robot communication enables robot teams to cooperatively solve complex problems that cannot be addressed by a single robot. Applications for cooperative robot teams include search and rescue, exploration and surveillance. Communication is one of the most important components in future autonomous robot systems and is essential for core functions such as inter-robot coordination, neighbour discovery and cooperative control algorithms. In environments where communication infrastructure does not exist, decentralised multi-hop networks can be constructed using only the radios on-board each robot. These are known as wireless mesh networks (WMNs). However existing WMNs have limited capacity to support even small robot teams. There is a need for WMNs where links act like dedicated point-to-point connections such as in wired networks. Addressing this problem requires a fundamentally new approach to WMN construction and this thesis is the first comprehensive study in the multi-robot literature to address these challenges. In this thesis, we propose a new class of communication systems called zero mutual interference (ZMI) networks that are able to emulate the point-to-point properties of a wired network over a WMN implementation. We instantiate the ZMI network using a multi-radio multi-channel architecture that autonomously adapts its topology and channel allocations such that all network edges communicate at the full capacity of the radio hardware. We implement the ZMI network on a 100-radio testbed with up to 20-individual nodes and verify its theoretical properties. Mobile robot experiments also demonstrate these properties are practically achievable. The results are an encouraging indication that the ZMI network approach can facilitate the communication demands of large cooperative robot teams deployed in practical problems such as data pipe-lining, decentralised optimisation, decentralised data fusion and sensor networks

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Task allocation and consensus with groups of cooperating Unmanned Aerial Vehicles

The applications for Unmanned Aerial Vehicles are numerous and cover a range of areas from military applications, scientific projects to commercial activities, but many of these applications require substantial human involvement. This work focuses on the problems and limitations in cooperative Unmanned Aircraft Systems to provide increasing realism for cooperative algorithms. The Consensus Based Bundle Algorithm is extended to remove single agent limits on the task allocation and consensus algorithm. Without this limitation the Consensus Based Grouping Algorithm is proposed that allows the allocation and consensus of multiple agents onto a single task. Solving these problems further increases the usability of cooperative Unmanned Aerial Vehicles groups and reduces the need for human involvement. Additional requirements are taken into consideration including equipment requirements of tasks and creating a specific order for task completion. The Consensus Based Grouping Algorithm provides a conflict free feasible solution to the multi-agent task assignment problem that provides a reasonable assignment without the limitations of previous algorithms. Further to this the new algorithm reduces the amount of communication required for consensus and provides a robust and dynamic data structure for a realistic application. Finally this thesis provides a biologically inspired improvement to the Consensus Based Grouping Algorithm that improves the algorithms performance and solves some of the difficulties it encountered with larger cooperative requirements

Decentralized multi-robot cooperation with auctioned pomdps

ABSTRACT Planning under uncertainty faces a scalability problem when considering multi-robot teams, as the information space scales exponentially with the number of robots. To address this issue, this paper proposes to decentralize multi-agent Partially Observable Markov Decision Process (POMDPs) while maintaining cooperation between robots by using POMDP policy auctions. Auctions provide a flexible way of coordinating individual policies modeled by POMDPs and have low communication requirements. Additionally, communication models in the multi-agent POMDP literature severely mismatch with real inter-robot communication. We address this issue by applying a decentralized data fusion method in order to efficiently maintain a joint belief state among the robots. The paper focuses on a cooperative tracking application, in which several robots have to jointly track a moving target of interest. The proposed ideas are illustrated in real multi-robot experiments, showcasing the flexible and robust coordination that our techniques can provide

A review of task allocation methods for UAVs

Unmanned aerial vehicles, can offer solutions to a lot of problems, making it crucial to research more and improve the task allocation methods used. In this survey, the main approaches used for task allocation in applications involving UAVs are presented as well as the most common applications of UAVs that require the application of task allocation methods. They are followed by the categories of the task allocation algorithms used, with the main focus being on more recent works. Our analysis of these methods focuses primarily on their complexity, optimality, and scalability. Additionally, the communication schemes commonly utilized are presented, as well as the impact of uncertainty on task allocation of UAVs. Finally, these methods are compared based on the aforementioned criteria, suggesting the most promising approaches

Toward a framework for implementation of climate change treaty through self-enforcing mechanisms

Global warming caused by accumulation of emissions of greenhouse gases (GHGs) is a public bad, addressing which requires collective action by all the countries of the world. Under the United Nations Convention on Climate Change (UNFCCC), most countries have negotiated the Kyoto Protocol for GHG emissions control to stabilize climate change. Several issues about the Protocol remain unresolved -- first, most of the significant countries are required to take a decision on whether or not to sign such a protocol, which has large-scale implications for their energy and industrial sectors and economic well-being; second, climate change mitigation is a public good entailing that all the countries would stand to gain due to mitigation action taken by a sub-group of one or more countries; and third, there exists no supra-national authority to enforce such a protocol for the individual sovereign nations. Thus, commitment to cooperate on an international agreement on climate change control remains tenuous. Formally, such a cooperative model is likely to be unstable. The paper discusses the pros and cons of the already proposed international cooperative mechanisms toward climate change mitigation and highlights the problem of information revelation, particularly related to the abatement issues. In this context, it attempts to outline a structure of a self-enforcing burden sharing mechanism for climate change mitigation in an incomplete information framework. The mechanism is an adoption of the well-known Vickrey-Clarke-Groves mechanism, widely used in mechanism design theory.Climate change negotiations; cooperative games; stable coalitions; self-enforcing mechanism

Information-Theoretic Control of Multiple Sensor Platforms

This thesis is concerned with the development of a consistent, information-theoretic basis for understanding of coordination and cooperation decentralised multi-sensor multi-platform systems. Autonomous systems composed of multiple sensors and multiple platforms potentially have significant importance in applications such as defence, search and rescue mining or intelligent manufacturing. However, the effective use of multiple autonomous systems requires that an understanding be developed of the mechanisms of coordination and cooperation between component systems in pursuit of a common goal. A fundamental, quantitative, understanding of coordination and cooperation between decentralised autonomous systems is the main goal of this thesis. This thesis focuses on the problem of coordination and cooperation for teams of autonomous systems engaged in information gathering and data fusion tasks. While this is a subset of the general cooperative autonomous systems problem, it still encompasses a range of possible applications in picture compilation, navigation, searching and map building problems. The great advantage of restricting the domain of interest in this way is that an underlying mathematical model for coordination and cooperation can be based on the use of information-theoretic models of platform and sensor abilities. The information theoretic approach builds on the established principles and architecture previously developed for decentralised data fusion systems. In the decentralised control problem addressed in this thesis, each platform and sensor system is considered to be a distinct decision maker with an individual information-theoretic utility measure capturing both local objectives and the inter-dependencies among the decisions made by other members of the team. Together these information-theoretic utilities constitute the team objective. The key contributions of this thesis lie in the quantification and study of cooperative control between sensors and platforms using information as a common utility measure. In particular, * The problem of information gathering is formulated as an optimal control problem by identifying formal measures of information with utility or pay-off. * An information-theoretic utility model of coupling and coordination between decentralised decision makers is elucidated. This is used to describe how the information gathering strategies of a team of autonomous systems are coupled. * Static and dynamic information structures for team members are defined. It is shown that the use of static information structures can lead to efficient, although sub-optimal, decentralised control strategies for the team. * Significant examples in decentralised control of a team of sensors are developed. These include the multi-vehicle multi-target bearings-only tracking problem, and the area coverage or exploration problem for multiple vehicles. These examples demonstrate the range of non-trivial problems to which the theory in this thesis can be employed