Incremental spectral clustering and its application to topological mapping

This paper presents a novel use of spectral clustering algorithms to support cases where the entries in the affinity matrix are costly to compute. The method is incremental – the spectral clustering algorithm is applied to the affinity matrix after each row/column is added – which makes it possible to inspect the clusters as new data points are added. The method is well suited to the problem of appearance-based, on-line topological mapping for mobile robots. In this problem domain, we show that we can reduce environment-dependent parameters of the clustering algorithm to just a single, intuitive parameter. Experimental results in large outdoor and indoor environments show that we can close loops correctly by computing only a fraction of the entries in the affinity matrix. The accompanying video clip shows how an example map is produced by the algorithm

Multi-robot Automated Search for Non-Adversarial Moving Evaders in an Unknown Environment

In this paper, the problem of searching for moving evaders in unknown environment using group of mobile robots is investigated. The aim is to find the moving evaders as fast as possible. Three different search techniques are proposed and evaluated through extensive experimentation. In the first two techniques, robots do not cooperate or coordinate their actions. Alternatively, they implement simple movement strategies to locate the evaders. On the contrary, in the third technique, robots employ explicit coordination among each other and they implement a relatively complex algorithm based on voronio graph to find the evaders. In the later technique, each robot needs to be equipped with communication and localization capabilities. The results showed that graph-based technique led to shortest search time. However, it also showed that a reasonable performance is possible with cheap robots implementing simple and non-coordination techniques. Keywords: Search, Multi-Robot, Voronio Graph, Moving Target, Coordination

Towards a Probabilistic Roadmap for Multi-robot Coordination

International audienceIn this paper, we discuss the problem of multi-robot coordination and propose an approach for coordinated multi-robot motion planning by using a probabilistic roadmap (PRM) based on adaptive cross sampling (ACS). The proposed approach, called ACS-PRM, is a sampling-based method and consists of three steps including C-space sampling, roadmap building and motion planning. In contrast to previous approaches, our approach is designed to plan separate kinematic paths for multiple robots to minimize the problem of congestion and collision in an effective way so as to improve the system efficiency. Our approach has been implemented and evaluated in simulation. The experimental results demonstrate the total planning time can be obviously reduced by our ACS-PRM approach compared with previous approaches

Incremental Topological Modeling using Sonar Gridmap in Home Environment

Abstract-This paper presents a method of topological modeling in home environments using only low-cost sonar sensors. The proposed method constructs a topological model using sonar gridmap by extracting subregions incrementally. A confidence for each occupied grid is evaluated to obtain reliable regions in a local gridmap, and a convexity measure is used to extract subregions automatically. Through these processes, the topological model is constructed without predefining the number of subregions in advance and the extracted subregions are guaranteed the convexity. Experimental results verify the performance of proposed method in real home environment

Exploiting Subgraph Structure in Multi-Robot Path Planning

Multi-robot path planning is difficult due to the combinatorial explosion of the search space with every new robot added. Complete search of the combined state-space soon becomes intractable. In this paper we present a novel form of abstraction that allows us to plan much more efficiently. The key to this abstraction is the partitioning of the map into subgraphs of known structure with entry and exit restrictions which we can represent compactly. Planning then becomes a search in the much smaller space of subgraph configurations. Once an abstract plan is found, it can be quickly resolved into a correct (but possibly sub-optimal) concrete plan without the need for further search. We prove that this technique is sound and complete and demonstrate its practical effectiveness on a real map. A contending solution, prioritised planning, is also evaluated and shown to have similar performance albeit at the cost of completeness. The two approaches are not necessarily conflicting; we demonstrate how they can be combined into a single algorithm which outperforms either approach alone

Hierarchical Map Building and Planning based on Graph Partitioning ∗

Abstract — Mobile robot localization and navigation requires a map- the robot’s internal representation of the environment. A common problem is that path planning becomes very inefficient for large maps. In this paper we address the problem of segmenting a base-level map in order to construct a higherlevel representation of the space which can be used for more efficient planning. We represent the base-level map as a graph for both geometric and appearance based space representations. Then we use a graph partitioning method to cluster nodes of the base-level map and in this way construct a high-level map, which is also a graph. We apply a hierarchical path planning method for stochastic tasks based on Markov Decision Processes (MDPs) and investigate the effect of choosing different numbers of clusters. Index Terms — mobile robots, hierarchical map building, topological map, path planning I

Simulated cognitive topologies: automatically generating highly contextual maps for complex journeys

As people traverse complex journeys, they engage in a number of information interactions across spatial scales and levels of abstraction. Journey complexity is characterised by factors including the number of actions required, and by variation in the contextual basis of reasoning such as a transition between different modes of transport. The high-level task of an A to B journey decomposes into a sequence of lower-level navigational sub-tasks, with the representation of geographic entities that support navigation during, between and across sub-tasks, varying relative to the nature of the task and the character of the geography. For example, transitioning from or to a particular mode of transport has a direct bearing on the natural level of representational abstraction that supports the task, as well as on the overall extent of the task’s region of influence on the traveller’s focus. Modern mobile technologies send data to a device that can in theory be context-specific in terms of explicitly reflecting a traveller’s heterogeneous information requirements, however the extent to which context is explicitly reflected in the selection and display of navigational information remains limited in practice, with a rigid, predetermined scale-based hierarchy of cartographic views remaining the underlying representational paradigm. The core subject of the research is the context-dependent selection and display of navigational information, and while there are many and varied considerations in developing techniques to address selection and display, the central challenge can simply be articulated as how to determine the probability, given the traveller’s current context, that a feature should be in the current map view. Clearly this central challenge extends to all features in the spatial extent, and so from a practical perspective, research questions centre around the initial selection of a subset of features, and around determining an overall probability distribution over the subset given the significance of features within the hierarchically ordered sequence of tasks. In this thesis research is presented around the use of graph structures as a practical basis for modeling urban geography to support heterogenous selections across viewing scales, and ultimately for displaying highly context-specific cartographic views. Through an iterative, empirical research methodology, a formalised approach based on routing networks is presented, which serves as the basis for modeling, selection and display. Findings are presented from a series of 7 situated navigation studies that included research with an existing navigation application as well as experimental research stimuli. Hypotheses were validated and refined over the course of the studies, with a focus on journey-specific regions that form around the navigable network. Empirical data includes sketch maps, textual descriptions, video and device interactions over the course of complex navigation exercises. Study findings support the proposed graph architecture, including subgraph classes that approximate cognitive structures central to natural comprehension and reasoning. Empirical findings lead to the central argument of a model based on causal mechanisms, in which relations are formalised between task, selection and abstraction. A causal framework for automatically determining map content for a given journey context is presented, with the approach involving a conceptual shift from treating geographic features as spatially indexed records, to treating them as variables with a finite number of possible states. Causal nets serve as the practical basis of reasoning, with geographic features being represented by variables in these causal structures. The central challenge of finding the probability that a variable in a causal net is in a particular state is addressed through a causal model in which journey context serves as the evidence that propagates over the net. In this way, complex heterogeneous selections for interactive multi-scale information spaces are expressed as probability distributions determined through message propagation. The thesis concludes with a discussion around the implications of the approach for the presentation of navigational information, and it is shown how the framework can support context-specific selection and disambiguation of map content, demonstrated through the central use case of navigating complex urban journeys