Technical session talks from ICRA 2012
TechTalks from event: Technical session talks from ICRA 2012
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Distributed Coverage with Mobile Robots on a Graph: Locational OptimizationThis paper presents a decentralized algorithm for coverage with mobile robots on a graph. Coverage is an important capability of multi-robot systems engaged in a number of different applications, including placement for environmental modeling, deployment for maximal quality surveillance, and even coordinated construction.We use distributed vertex substitution for locational optimization, and the controllers minimize the corresponding cost function. We prove that the proposed controller with two-hop communication guarantees convergence to the locally optimal configuration. We evaluate the algorithms in simulations and compare them to the coverage algorithm in a continuous domain.
An Approach to Multi-Agent Area Protection Using Bayes RiskWe introduce a novel approach to controlling the motion of a team of agents so that they jointly minimize a cost function utilizing Bayes risk. We use a particle-based approach and approximations that allow us to express the optimization problem as a mixed-integer linear program. We illustrate this approach with an area protection problem in which a team of mobile agents must intercept mobile targets before the targets enter a specified area. Bayes risk is a useful measure of performance for applications where agents must perform a classification task. By minimizing Bayes risk, agents are able to explicitly account for the cost of incorrect classification. In our application, a team of mobile agents must classify potential mobile targets as threat or safe based on the likelihood the targets will enter the specified area. The agents must also maneuver to intercept targets that are classified as threat.
On Coordination in Practical Multi-Robot PatrolMulti-robot patrol is a fundamental application of multi-robot systems. While much theoretical work exists providing an understanding of the optimal patrol strategy for teams of coordinated, homogeneous robots, little work exists on building and evaluating the performance of such systems in the real world. In this paper, we evaluate the performance of multi-robot patrol in a practical outdoor robotic system, and evaluate the effect of different coordination schemes on the performance of the robotic team, which is influenced by their communication capabilities and degree of heterogeneity. We specifically focus on frequency-based multi-robot patrol along a cyclic route specified by a set of GPS-waypoints. The multi-robot patrol algorithms evaluated vary in the level of coordination of the robots: no coordination, loose coordination, and strong coordination. In addition, we evaluate versions of these algorithms that distribute state information---either individual state, or state of the entire team (global state). Our experiments show that while strong coordination was theoretically proven to be optimal, in practice uncoordinated patrol performed better in terms of average waypoint visitation frequency. Furthermore, loosely coordinated patrol that shares only individual state outperformed all other coordination schemes in terms of worst-case frequency, and it performed significantly better than a loosely coordinated algorithm based on sharing global-view state. We respond t
Adaptive Sampling Using Mobile Sensor NetworksThis paper presents an adaptive sparse sampling approach and the corresponding real-time scalar field reconstruction method using mobile sensor networks. Traditionally, the sampling methods collect measurements without considering possible distributions of target signals. A feedback driven algorithm is discussed in this paper, where new measurements are determined based on the analysis of existing observations. The information amount of each potential measurement is evaluated under a sparse domain based on compressive sensing framework given all existing information shared among networked mobile sensors, and the most informative one is selected. The efficiency of this information-driven method falls into the information maximization for each individual measurement. The simulation results show the efficacy and efficiency of this approach, where a scalar field is recovered.
Coverage Control of Mobile Sensors for Adaptive Search of Unknown Number of TargetsWe present a multiscale adaptive search algorithm for efficiently searching an unknown number of stationary targets using a team of multiple mobile sensors. We first derive a Spectral Multiscale Coverage (SMC) control law for a Dubins vehicle model. Given a search prior, the SMC control gives rise to uniform coverage dynamics for the mobile sensors such that the amount of time spent observing a region is proportional to finding a target in it. In order to make the search robust to sensor uncertainties and Automatic Target Detection algorithm errors (i.e. false alarm, missed detections), we combine the SMC control with decision and estimation theoretic techniques. As new targets are discovered we use the Sequential Ratio Probability Test and Recursive Least Squares estimation to quantify the current uncertainty in target detection and location, respectively. This uncertainty is used to update the search prior so as to balance exploitation (reduce uncertainty in state of already discovered potential targets) and exploration (discover new targets). We demonstrate this adaptive search methodology in a high fidelity simulation environment and show an improved performance over lawnmower type search.