Cooperative Supply Chains in Peace and at War

In the competition between supply chains, governance structure and coordination mechanisms can be as important as cost-efficiency. Flexible and non-committing contracts among upstream suppliers in cooperative alliances may lead to lower chain surplus through internal competition and renders the coordinator's position vulnerable for hostile take-overs. Cooperative supply chains are found in e.g. food industry, banking services, lawfirms and brokerage. The downstream processing or brand is owned collectively by the suppliers or service-providers. The supplier are linked to the chain by strong delivery (channel) rights and volume-based revenue-sharing schemes. The governance is flexible, promotes entry and market expansion. However, the decentralized decision making comes at a cost in terms of chain performance and resilience. A dynamic two-chain model with a captive and competitive market addresses the particular situation where the competing chain aggressor has a cooperative governance structure. The overt aggression at merger may have more to do with shortcomings in the managerial incentive structure than with the pursuit of market power. The results from the dynamic game is illustrated with empirical findings among dairy cooperatives in Denmark.Agribusiness,

Mechanical Design, Modelling and Control of a Novel Aerial Manipulator

In this paper a novel aerial manipulation system is proposed. The mechanical structure of the system, the number of thrusters and their geometry will be derived from technical optimization problems. The aforementioned problems are defined by taking into consideration the desired actuation forces and torques applied to the end-effector of the system. The framework of the proposed system is designed in a CAD Package in order to evaluate the system parameter values. Following this, the kinematic and dynamic models are developed and an adaptive backstepping controller is designed aiming to control the exact position and orientation of the end-effector in the Cartesian space. Finally, the performance of the system is demonstrated through a simulation study, where a manipulation task scenario is investigated.Comment: Comments: 8 Pages, 2015 IEEE International Conference on Robotics and Automation (ICRA '15), Seattle, WA, US

Localization from semantic observations via the matrix permanent

Most approaches to robot localization rely on low-level geometric features such as points, lines, and planes. In this paper, we use object recognition to obtain semantic information from the robot’s sensors and consider the task of localizing the robot within a prior map of landmarks, which are annotated with semantic labels. As object recognition algorithms miss detections and produce false alarms, correct data association between the detections and the landmarks on the map is central to the semantic localization problem. Instead of the traditional vector-based representation, we propose a sensor model, which encodes the semantic observations via random finite sets and enables a unified treatment of missed detections, false alarms, and data association. Our second contribution is to reduce the problem of computing the likelihood of a set-valued observation to the problem of computing a matrix permanent. It is this crucial transformation that allows us to solve the semantic localization problem with a polynomial-time approximation to the set-based Bayes filter. Finally, we address the active semantic localization problem, in which the observer’s trajectory is planned in order to improve the accuracy and efficiency of the localization process. The performance of our approach is demonstrated in simulation and in real environments using deformable-part-model-based object detectors. Robust global localization from semantic observations is demonstrated for a mobile robot, for the Project Tango phone, and on the KITTI visual odometry dataset. Comparisons are made with the traditional lidar-based geometric Monte Carlo localization