Landmark Guided Probabilistic Roadmap Queries

A landmark based heuristic is investigated for reducing query phase run-time of the probabilistic roadmap (\PRM) motion planning method. The heuristic is generated by storing minimum spanning trees from a small number of vertices within the \PRM graph and using these trees to approximate the cost of a shortest path between any two vertices of the graph. The intermediate step of preprocessing the graph increases the time and memory requirements of the classical motion planning technique in exchange for speeding up individual queries making the method advantageous in multi-query applications. This paper investigates these trade-offs on \PRM graphs constructed in randomized environments as well as a practical manipulator simulation.We conclude that the method is preferable to Dijkstra's algorithm or the ${\rm A}^*$ algorithm with conventional heuristics in multi-query applications.Comment: 7 Page

Gaussian belief propagation for real-time decentralised inference

For embodied agents to interact intelligently with their surroundings, they require perception systems that construct persistent 3D representations of their environments. These representations must be rich; capturing 3D geometry, semantics, physical properties, affordances and much more. Constructing the environment representation from sensory observations is done via Bayesian probabilistic inference and in practical systems, inference must take place within the power, compactness and simplicity constraints of real products. Efficient inference within these constraints however remains computationally challenging and current systems often require heavy computational resources while delivering a fraction of the desired capabilities. Decentralised algorithms based on local message passing with in-place processing and storage offer a promising solution to current inference bottlenecks. They are well suited to take advantage of recent rapid developments in distributed asynchronous processing hardware to achieve efficient, scalable and low-power performance. In this thesis, we argue for Gaussian belief propagation (GBP) as a strong algorithmic framework for distributed, generic and incremental probabilistic estimation. GBP operates by passing messages between the nodes on a factor graph and can converge with arbitrary asynchronous message schedules. We envisage the factor graph being the fundamental master environment representation, and GBP the flexible inference tool to compute local in-place probabilistic estimates. In large real-time systems, GBP will act as the `glue' between specialised modules, with attention based processing bringing about local convergence in the graph in a just-in-time manner. This thesis contains several technical and theoretical contributions in the application of GBP to practical real-time inference problems in vision and robotics. Additionally, we implement GBP on novel graph processor hardware and demonstrate breakthrough speeds for bundle adjustment problems. Lastly, we present a prototype system for incrementally creating hierarchical abstract scene graphs by combining neural networks and probabilistic inference via GBP.Open Acces

A robot swarm assisting a human fire-fighter

Emergencies in industrial warehouses are a major concern for fire-fighters. The large dimensions, together with the development of dense smoke that drastically reduces visibility, represent major challenges. The GUARDIANS robot swarm is designed to assist fire-fighters in searching a large warehouse. In this paper we discuss the technology developed for a swarm of robots assisting fire-fighters. We explain the swarming algorithms that provide the functionality by which the robots react to and follow humans while no communication is required. Next we discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also the means to locate the robots and humans. Thus, the robot swarm is able to provide guidance information to the humans. Together with the fire-fighters we explored how the robot swarm should feed information back to the human fire-fighter. We have designed and experimented with interfaces for presenting swarm-based information to human beings

Augmented Tree-based Routing Protocol for Scalable Ad Hoc Networks

In ad hoc networks scalability is a critical requirement if these technologies have to reach their full potential. Most of the proposed routing protocols do not operate efficiently with networks of more than a few hundred nodes. In this paper, we propose an augmented tree-based address space structure and a hierarchical multi-path routing protocol, referred to as Augmented Tree-based Routing (ATR), which utilizes such a structure in order to solve the scalability problem and to gain good resilience against node failure/mobility and link congestion/instability. Simulation results and performance comparisons with existing protocols substantiate the effectiveness of the ATR.Comment: Routing, mobile ad hoc network, MANET, dynamic addressing, multi-path, distributed hash table, DH

Greedy routing and virtual coordinates for future networks

At the core of the Internet, routers are continuously struggling with ever-growing routing and forwarding tables. Although hardware advances do accommodate such a growth, we anticipate new requirements e.g. in data-oriented networking where each content piece has to be referenced instead of hosts, such that current approaches relying on global information will not be viable anymore, no matter the hardware progress. In this thesis, we investigate greedy routing methods that can achieve similar routing performance as today but use much less resources and which rely on local information only. To this end, we add specially crafted name spaces to the network in which virtual coordinates represent the addressable entities. Our scheme enables participating routers to make forwarding decisions using only neighbourhood information, as the overarching pseudo-geometric name space structure already organizes and incorporates "vicinity" at a global level. A first challenge to the application of greedy routing on virtual coordinates to future networks is that of "routing dead-ends" that are local minima due to the difficulty of consistent coordinates attribution. In this context, we propose a routing recovery scheme based on a multi-resolution embedding of the network in low-dimensional Euclidean spaces. The recovery is performed by routing greedily on a blurrier view of the network. The different network detail-levels are obtained though the embedding of clustering-levels of the graph. When compared with higher-dimensional embeddings of a given network, our method shows a significant diminution of routing failures for similar header and control-state sizes. A second challenge to the application of virtual coordinates and greedy routing to future networks is the support of "customer-provider" as well as "peering" relationships between participants, resulting in a differentiated services environment. Although an application of greedy routing within such a setting would combine two very common fields of today's networking literature, such a scenario has, surprisingly, not been studied so far. In this context we propose two approaches to address this scenario. In a first approach we implement a path-vector protocol similar to that of BGP on top of a greedy embedding of the network. This allows each node to build a spatial map associated with each of its neighbours indicating the accessible regions. Routing is then performed through the use of a decision-tree classifier taking the destination coordinates as input. When applied on a real-world dataset (the CAIDA 2004 AS graph) we demonstrate an up to 40% compression ratio of the routing control information at the network's core as well as a computationally efficient decision process comparable to methods such as binary trees and tries. In a second approach, we take inspiration from consensus-finding in social sciences and transform the three-dimensional distance data structure (where the third dimension encodes the service differentiation) into a two-dimensional matrix on which classical embedding tools can be used. This transformation is achieved by agreeing on a set of constraints on the inter-node distances guaranteeing an administratively-correct greedy routing. The computed distances are also enhanced to encode multipath support. We demonstrate a good greedy routing performance as well as an above 90% satisfaction of multipath constraints when relying on the non-embedded obtained distances on synthetic datasets. As various embeddings of the consensus distances do not fully exploit their multipath potential, the use of compression techniques such as transform coding to approximate the obtained distance allows for better routing performances

Route Planning in Road Networks

We present various speedup techniques for route planning in road networks. After performing some preprocessing steps, we can compute accurate quickest-path lengths in a few microseconds on a 2.0 GHz machine, using real-world road networks with several million nodes. In addition to dealing with the static point-to-point problem, we also handle dynamic scenarios (like traffic jams) and many-to-many instances

Towards Efficient File Sharing and Packet Routing in Mobile Opportunistic Networks

With the increasing popularity of portable digital devices (e.g., smartphones, laptops, and tablets), mobile opportunistic networks (MONs) [40, 90] consisting of portable devices have attracted much attention recently. MONs are also known as pocket switched networks (PSNs) [52]. MONs can be regarded as a special form of mobile ad hoc networks (MANETs) [7] or delay tolerant networks (DTNs) [35, 56]. In such networks, mobile nodes (devices) move continuously and meet opportunistically. Two mobile nodes can communicate with each other only when they are within the communication range of each other in a peer-to-peer (P2P) manner (i.e., without the need of infrastructures). Therefore, such a network structure can potentially provide file sharing or packet routing services among portable devices without the support of network infrastructures. On the other hand, mobile opportunistic networks often experience frequent network partition, and no end-to-end contemporaneous path can be ensured in the network. These distinctive properties make traditional file sharing or packet routing algorithms in Internet or mobile networks a formidable challenge in MONs. In summary, it is essential and important to achieve efficient file sharing and packet routing algorithms in MONs, which are the key for providing practical and novel services and applications over such networks. In this Dissertation, we develop several methods to resolve the aforementioned challenges. Firstly, we propose two methods to enhance file sharing efficiency in MONs by creating replicas and by leveraging social network properties, respectively. In the first method, we investigate how to create file replicas to optimize file availability for file sharing in MONs. We introduce a new concept of resource for file replication, which considers both node storage and meeting frequency with other nodes. We theoretically study the influence of resource allocation on the average file access delay and derive a resource allocation rule to minimize the average file access delay. We also propose a distributed file replication protocol to realize the deduced optimal file replication rule. In the second method, we leverage social network properties to improve the file searching efficiency in MONs. This method groups common-interest nodes that frequently meet with each other into a community. It takes advantage of node mobility by designating stable nodes, which have the most frequent contact with community members, as community coordinators for intra-community file request forwarding, and highly-mobile nodes that visit other communities frequently as community ambassadors for inter-community file request forwarding. Based on such a community structure, an interest-oriented file searching scheme is proposed to first search local community and then search the community that is most likely to contain the requested file, leading to highly efficient file sharing in MONs. Secondly, we propose two methods to realize efficient packet routing among mobile nodes and among different landmarks in MONs, respectively. The first method utilizes distributed social map to route packets to mobile nodes efficiently with a low-cost in MONs. Each node builds its own social map consisting of nodes it has met and their frequently encountered nodes in a distributed manner. Based on both encountering frequency and social closeness of two linked nodes in the social map, we decide the weight of each link to reflect the packet delivery ability between the two nodes. The social map enables more accurate forwarder selection through a broader view and reduces the cost on information exchange. The second method realizes high-throughput packet routing among different landmarks in MONs. It selects popular places that nodes visit frequently as landmarks and divides the entire MON area into sub-areas represented by landmarks. Nodes transiting between two landmarks relay packets between the two landmarks. The frequency of node transits between two landmarks is measured to represent the forwarding capacity between them, based on which routing tables are built on each landmark to guide packet routing. Finally, packets are routed landmark by landmark to reach their destination landmarks. Extensive analysis and real-trace based experiments are conducted to support the designs in this Dissertation and demonstrate the effectiveness of the proposed methods in comparison with the state-of-art methods. In the future, we plan to further enhance the file sharing and packet routing efficiency by considering more realistic scenarios or including more useful information. We will also investigate the security and privacy issues in the proposed methods