Hastily formed knowledge networks and distributed situation awareness for collaborative robotics

In the context of collaborative robotics,distributed situation awareness is essential for supporting collective intelligence in teams of robots and human agents where it can be used for both individual and collective decision support.This is particularly important in applications pertaining to emergency rescue and crisis management.During operational missions,data and knowledge are gathered incrementally and in different ways by heterogeneous robots and humans.We describe this as the creation of Hastily Formed Knowledge Networks(HFKNs).The focus of this paper is the specification and prototyping of a general distributed system architecture that supports the creation of HFKNs by teams of robots and humans.The information collected ranges from low-level sensor data to high-level semantic knowledge,the latter represented in part as RDF Graphs.The framework includes a synchronization protocol and associated algorithms that allow for the automatic distribution and sharing of data and knowledge between agents.This is done through the distributed synchronization of RDF Graphs shared between agents.High-level semantic queries specified in SPARQL can be used by robots and humans alike to acquire both knowledge and data content from team members.The system is empirically validated and complexity results of the proposed algorithms are provided.Additionally,a field robotics case study is described,where a 3D mapping mission has been executed using several UAVs in a collaborative emergency rescue scenario while using the full HFKN Framework.

Router and gateway node placement in wireless mesh networks for emergency rescue scenarios

The focus of this paper is on base functionalities required for UAV-based rapid deployment of an ad hoc communication infrastructure in the initial phases of rescue operations.The main idea is to use heterogeneous teams of UAVs to deploy communication kits that include routers,and are used in the generation of ad hoc Wireless Mesh Networks(WMN).Several fundamental problems are considered and algorithms are proposed to solve these problems.The Router Node Placement problem(RNP)and a generalization of it that takes into account additional constraints arising in actual field usage is considered first.The RNP problem tries to determine how to optimally place routers in a WMN.A new algorithm,the RRT-WMN algorithm,is proposed to solve this problem.It is based in part on a novel use of the Rapidly Exploring Random Trees(RRT)algorithm used in motion planning.A comparative empirical evaluation between the RRT-WMN algorithm and existing techniques such as the Covariance Matrix Adaptation Evolution Strategy(CMA-ES)and Particle Swarm Optimization(PSO),shows that the RRT-WMN algorithm has far better performance both in amount of time taken and regional coverage as the generalized RNP problem scales to realistic scenarios.The Gateway Node Placement Problem(GNP)tries to determine how to locate a minimal number of gateway nodes in a WMN backbone network while satisfying a number of Quality of Service(QoS)constraints.Two alternatives are proposed for solving the combined RNP-GNP problem.The first approach combines the RRT-WMN algorithm with a preexisting graph clustering algorithm.The second approach,WMNbyAreaDecomposition,proposes a novel divide-and-conquer algorithm that recursively partitions a target deployment area into a set of disjoint regions,thus creating a number of simpler RNP problems that are then solved concurrently.Both algorithms are evaluated on real-world GIS models of different size and complexity.WMNbyAreaDecomposition is shown to outperform existing algorithms using 73%to 92%fewer router nodes while at the same time satisfying all QoS requirements.