G?rungserscheinungen

Ohne Zusammenfassung     

Milch und K?se

Ohne Zusammenfassung     

Using role-based control to produce locomotion in chain-type self-reconfigurable robots

This paper presents a role-based approach to the problem of controlling locomotion of chain-type self-reconfigurable robots. In role-based control, all modules are controlled by identical controllers. Each controller consists of several playable roles and a role-selection mechanism. A role represents the motion of a module and how it synchronizes with connected modules. A controller selects which role to play depending on the local configuration of the module and the roles being played by connected modules. We use role-based control to implement a sidewinder and a caterpillar gait in the CONRO self-reconfigurable robot. The robot is made from up to nine modules connected in a chain. We show that the locomotion speed of the caterpillar gait is constant even with loss of 75% of the communication signals. Furthermore, we show that the speed of the caterpillar gait decreases gracefully with a decreased number of modules. We also implement a quadruped gait and show that without changing the controller the robot can be extended with an extra pair of legs and produce a hexapod gait. Based on these experiments, we conclude that role-based control is robust to signal loss, scales with an increased number of modules, and is a simple approach to the control of locomotion of chain-type self-reconfigurable robots.     

From the Politics of Urgency to the Governance of Preparedness: A Research Agenda on Urban Vulnerability

To date, little social science understanding has been developed about what it would mean to strategically build resilience in the context of such rich interdependencies between social, technical and natural worlds. We argue that shifts in strategies to deal with urban crises marks a turn from the politics of urgency, characteristic of crisis management, towards a governance of preparedness, characterised by strategies to build urban resilience. Social science needs to develop research agendas that critically engage with different understandings of resilience and the challenges of building resilience across different scales of urban governance.     

Parallel architectures for AI semantic network processing

Artificial intelligence (AI) applications are growing in several fields, and in many such applications knowledge bases must be manipulated. This activity is usually performed by an external agent such as a central processor, but often this cannot supply the speed required. Knowledge-oriented architectures provide an efficient execution of knowledge manipulations. This paper provides an introduction to a particular subset of knowledge-oriented architectures, the semantic network approach, which is one of the most commonly used methods of representing and manipulating knowledge in the AI field. A brief overview of the semantic network components is presented in order to provide a background to the topic. The purpose of this paper is to review the proposed, implemented and/or simulated architectures for semantic network processing, and to discuss the capabilities and limitations of such architectures.