Multi-robot team formation control in the GUARDIANS project

Purpose The GUARDIANS multi-robot team is to be deployed in a large warehouse in smoke. The team is to assist firefighters search the warehouse in the event or danger of a fire. The large dimensions of the environment together with development of smoke which drastically reduces visibility, represent major challenges for search and rescue operations. The GUARDIANS robots guide and accompany the firefighters on site whilst indicating possible obstacles and the locations of danger and maintaining communications links. Design/methodology/approach In order to fulfill the aforementioned tasks the robots need to exhibit certain behaviours. Among the basic behaviours are capabilities to stay together as a group, that is, generate a formation and navigate while keeping this formation. The control model used to generate these behaviours is based on the so-called social potential field framework, which we adapt to the specific tasks required for the GUARDIANS scenario. All tasks can be achieved without central control, and some of the behaviours can be performed without explicit communication between the robots. Findings The GUARDIANS environment requires flexible formations of the robot team: the formation has to adapt itself to the circumstances. Thus the application has forced us to redefine the concept of a formation. Using the graph-theoretic terminology, we can say that a formation may be stretched out as a path or be compact as a star or wheel. We have implemented the developed behaviours in simulation environments as well as on real ERA-MOBI robots commonly referred to as Erratics. We discuss advantages and shortcomings of our model, based on the simulations as well as on the implementation with a team of Erratics.</p

Position-Based Multi-Agent Dynamics for Real-Time Crowd Simulation (MiG paper)

Exploiting the efficiency and stability of Position-Based Dynamics (PBD), we introduce a novel crowd simulation method that runs at interactive rates for hundreds of thousands of agents. Our method enables the detailed modeling of per-agent behavior in a Lagrangian formulation. We model short-range and long-range collision avoidance to simulate both sparse and dense crowds. On the particles representing agents, we formulate a set of positional constraints that can be readily integrated into a standard PBD solver. We augment the tentative particle motions with planning velocities to determine the preferred velocities of agents, and project the positions onto the constraint manifold to eliminate colliding configurations. The local short-range interaction is represented with collision and frictional contact between agents, as in the discrete simulation of granular materials. We incorporate a cohesion model for modeling collective behaviors and propose a new constraint for dealing with potential future collisions. Our new method is suitable for use in interactive games.Comment: 9 page

Identity and Commitment: Sen\u27s Conception of the Individual

This paper develops a conception of personal identity for Amartya Sen’s capability framework that emphasizes his self-scrutinizing aspect of the self and related concept of commitment, and compares this conception to the collective intentionality-based one advanced in Davis (2003c). The paper also distinguishes personal identity and social identity, and contrasts Sen’s framework with recent standard economics’ explanation of social identity in terms of conformity. Sen’s concept of commitment is examined in two formulations, and the later version is related to Bernard Williams’ thinking about identity-conferring commitments. The paper’s concludes by arguing that explaining personal identity as a special capability and possible object of social-economic policy provides one way of resolving the debate over whether the capability framework ought to have a short-list of essential capabilities

Reconciling structure and agency in strategy -as-practice research: Towards a strong- structuration theory approach

An overwhelming focus of research on the micro agency of strategic actors has led to the literature being characterized as demonstrating a micro-myopia, resulting in a micro-isolationism. This means we know little about how the micro interrelates with the macro in strategy work. We address this problem in our conceptual article which adopts a structurationist stance to explicate how strategy-as-practice (SaP) research could be enhanced and extended by paying equal attention to both agency and structure. Specifically, we advance strong structuration theory (SST), a promising development from Giddens’ seminal work on structuration theory, to show how strategic activity can be understood as an ongoing process of structuration unfolding over time. We argue for the use of both types of methodological bracketing (context and conduct analysis), advocating systematic attention to the interplay between macro-societal and micro-local levels of analysis. Our discussion concludes with guidance for researchers inviting them to undertake empirical fieldwork that overcomes SaP’s current micro-myopia, creating a more balanced corpus of work

Towards A Theory-Of-Mind-Inspired Generic Decision-Making Framework

Simulation is widely used to make model-based predictions, but few approaches have attempted this technique in dynamic physical environments of medium to high complexity or in general contexts. After an introduction to the cognitive science concepts from which this work is inspired and the current development in the use of simulation as a decision-making technique, we propose a generic framework based on theory of mind, which allows an agent to reason and perform actions using multiple simulations of automatically created or externally inputted models of the perceived environment. A description of a partial implementation is given, which aims to solve a popular game within the IJCAI2013 AIBirds contest. Results of our approach are presented, in comparison with the competition benchmark. Finally, future developments regarding the framework are discussed.Comment: 7 pages, 5 figures, IJCAI 2013 Symposium on AI in Angry Bird