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

Meeting in a Polygon by Anonymous Oblivious Robots

The Meeting problem for k>=2 searchers in a polygon P (possibly with holes) consists in making the searchers move within P, according to a distributed algorithm, in such a way that at least two of them eventually come to see each other, regardless of their initial positions. The polygon is initially unknown to the searchers, and its edges obstruct both movement and vision. Depending on the shape of P, we minimize the number of searchers k for which the Meeting problem is solvable. Specifically, if P has a rotational symmetry of order sigma (where sigma=1 corresponds to no rotational symmetry), we prove that k=sigma+1 searchers are sufficient, and the bound is tight. Furthermore, we give an improved algorithm that optimally solves the Meeting problem with k=2 searchers in all polygons whose barycenter is not in a hole (which includes the polygons with no holes). Our algorithms can be implemented in a variety of standard models of mobile robots operating in Look-Compute-Move cycles. For instance, if the searchers have memory but are anonymous, asynchronous, and have no agreement on a coordinate system or a notion of clockwise direction, then our algorithms work even if the initial memory contents of the searchers are arbitrary and possibly misleading. Moreover, oblivious searchers can execute our algorithms as well, encoding information by carefully positioning themselves within the polygon. This code is computable with basic arithmetic operations (provided that the coordinates of the polygon\u27s vertices are algebraic real numbers in some global coordinate system), and each searcher can geometrically construct its own destination point at each cycle using only a compass. We stress that such memoryless searchers may be located anywhere in the polygon when the execution begins, and hence the information they initially encode is arbitrary. Our algorithms use a self-stabilizing map construction subroutine which is of independent interest

A practical pursuit-evasion algorithm: Detection and tracking

Abstract-This paper presents a practical algorithm for evader detection and tracking using one or more pursuers. The solution employs two advanced data structures. The first one is the Rapidly-Exploring Random Tree (RRT). It is constructed randomly but evenly distributed to generate a roadmap that captures the connectivity of the free space. The second data structure is the k-dimensional tree (Kd-Tree). Upon completion of the RRTs construction, their vertices are inserted in a Kd-Tree. At the tracking phase, the Kd-Tree will be queried repeatedly for retrieving the set of potential locations to be used by each pursuer in order to monitor or track an evader. Thus, the usage of the Kd-Tree will reduce the querying cost, during tracking, to a logarithmic time. A lazy collision detection strategy is used to resolve collisions with obstacles at runtime. As a result unnecessary checks are eliminated and hence improving the system performance. Simulation results show the validity of the proposed algorithm

Environment Characterization for Non-Recontaminating Frontier-Based Robotic Exploration

This paper addresses the problem of obtaining a concise description of a physical environment for robotic exploration. We aim to determine the number of robots required to clear an environment using non-recontaminating exploration. We introduce the medial axis as a configuration space and derive a mathematical representation of a continuous environment that captures its underlying topology and geometry. We show that this representation provides a concise description of arbitrary environments, and that reasoning about points in this representation is equivalent to reasoning about robots in physical space. We leverage this to derive a lower bound on the number of required pursuers. We provide a transformation from this continuous representation into a symbolic representation. Finally, we present a generalized pursuit-evasion algorithm. Given an environment we can compute how many pursuers we need, and generate an optimal pursuit strategy that will guarantee the evaders are detected with the minimum number of pursuers.Singapore-MIT Alliance for Research and Technology Center (Future Urban Mobility Project)United States. Air Force Office of Scientific Research (Award FA9550-08-1-0159)National Science Foundation (U.S.) (Award CNS-0715397)National Science Foundation (U.S.) (Award CCF-0726514)National Science Foundation (U.S.) (Grant 0735953

Extracting surveillance graphs from robot maps

Abstract — GRAPH-CLEAR is a recently introduced theo-retical framework to model surveillance tasks accomplished by multiple robots patrolling complex indoor environments. In this paper we provide a first step to close the loop between its graph-based theoretical formulation and practical scenarios. We show how it is possible to algorithmically extract suitable so-called surveillance graphs from occupancy grid maps. We also identify local graph modification operators, called contractions, that alter the graph being extracted so that the original surveillance problem can be solved using less robots. The algorithm we present is based on the Generalized Voronoi Diagram, a structure that can be simply computed using watershed like algorithms. Our algorithm is evaluated by processing maps produced by mobile robots exploring indoor environments. It turns out that the proposed algorithm is fast, robust to noise, and opportunistically modifies the graph so that less expensive strategies can be computed. I