Asynchronous stigmergic sorting of binary matrix patterns: applications of classical distributed computing ideas

Multi-agent stigmergy forms the basis of explanatory theories for various self-organized biological phenomena, and also serves as an implementation strategy for several important artificial applications. While a number of sophisticated techniques have been used in the modeling and analysis of stigmergic processes, none of them, yet, seem to be drawn from the toolbox of classical distributed computing. Our goals are to investigate and lay the groundwork for the use of classical distributed computing ideas in reasoning about stigmergic computation. Specifically, we investigate case studies that are drawn from the domain of binary matrix pattern sorting. Here, a `swarm\u27 of memory-less and non-communicating agents follow a set of local stigmergic rules to asynchronously sort the binary states of cells in a 2-D grid so as to satisfy some global pattern specification. This domain is attractive as a test-bed because it serves as an abstraction for instances of biological pattern sorting and also because of its stigmergic expressiveness. We demonstrate the application of the following four distributed computing concepts: (1) execution serializability, (2) local checking, (3) variant functions, and (4) indistinguishability (the last as an impossibility proof technique) in the modeling and analysis of our case studies. Based on our preliminary experience with this particular domain, it seems to us that classical distributed computing techniques could be applied further in reasoning about stigmergic systems, perhaps leading to the formulation of a generalized stigmergic computational paradigm based on the principles of distributed computing

Coordination of Multirobot Teams and Groups in Constrained Environments: Models, Abstractions, and Control Policies

Robots can augment and even replace humans in dangerous environments, such as search and rescue and reconnaissance missions, yet robots used in these situations are largely tele-operated. In most cases, the robots\u27 performance depends on the operator\u27s ability to control and coordinate the robots, resulting in increased response time and poor situational awareness, and hindering multirobot cooperation. Many factors impede extended autonomy in these situations, including the unique nature of individual tasks, the number of robots needed, the complexity of coordinating heterogeneous robot teams, and the need to operate safely. These factors can be partly addressed by having many inexpensive robots and by control policies that provide guarantees on convergence and safety. In this thesis, we address the problem of synthesizing control policies for navigating teams of robots in constrained environments while providing guarantees on convergence and safety. The approach is as follows. We first model the configuration space of the group (a space in which the robots cannot violate the constraints) as a set of polytopes. For a group with a common goal configuration, we reduce complexity by constructing a configuration space for an abstracted group state. We then construct a discrete representation of the configuration space, on which we search for a path to the goal. Based on this path, we synthesize feedback controllers, decentralized affine controllers for kinematic systems and nonlinear feedback controllers for dynamical systems, on the polytopes, sequentially composing controllers to drive the system to the goal. We demonstrate the use of this method in urban environments and on groups of dynamical systems such as quadrotors. We reduce the complexity of multirobot coordination by using an informed graph search to simultaneously build the configuration space and find a path in its discrete representation to the goal. Furthermore, by using an abstraction on groups of robots we dissociate complexity from the number of robots in the group. Although the controllers are designed for navigation in known environments, they are indeed more versatile, as we demonstrate in a concluding simulation of six robots in a partially unknown environment with evolving communication links, object manipulation, and stigmergic interactions

Advanced Knowledge Application in Practice

The integration and interdependency of the world economy leads towards the creation of a global market that offers more opportunities, but is also more complex and competitive than ever before. Therefore widespread research activity is necessary if one is to remain successful on the market. This book is the result of research and development activities from a number of researchers worldwide, covering concrete fields of research

Self–organised multi agent system for search and rescue operations

Autonomous multi-agent systems perform inadequately in time critical missions, while they tend to explore exhaustively each location of the field in one phase with out selecting the pertinent strategy. This research aims to solve this problem by introducing a hierarchy of exploration strategies. Agents explore an unknown search terrain with complex topology in multiple predefined stages by performing pertinent strategies depending on their previous observations. Exploration inside unknown, cluttered, and confined environments is one of the main challenges for search and rescue robots inside collapsed buildings. In this regard we introduce our novel exploration algorithm for multi–agent system, that is able to perform a fast, fair, and thorough search as well as solving the multi–agent traffic congestion. Our simulations have been performed on different test environments in which the complexity of the search field has been defined by fractal dimension of Brownian movements. The exploration stages are depicted as defined arenas of National Institute of Standard and Technology (NIST). NIST introduced three scenarios of progressive difficulty: yellow, orange, and red. The main concentration of this research is on the red arena with the least structure and most challenging parts to robot nimbleness