Timothy H. Chung

On a stochastic sensor selection algorithm with applications in sensor scheduling and sensor coverage

In this note we consider the following problem. Suppose a set of sensors is jointly trying to estimate a process. One sensor takes a measurement at every time step and the measurements are then exchanged among all the sensors. What is the sensor schedule that results in the minimum error covariance? We describe a stochastic sensor selection strategy that is easy to implement and is computationally tractable. The problem described above comes up in many domains out of which we discuss two. In the sensor selection problem, there are multiple sensors that cannot operate simultaneously (e.g., sonars in the same frequency band). Thus measurements need to be scheduled. In the sensor coverage problem, a geographical area needs to be covered by mobile sensors each with limited range. Thus from every position, the sensors obtain a different view-point of the area and the sensors need to optimize their trajectories. The algorithm is applied to these problems and illustrated through simple examples.

Search and pursuit-evasion in mobile robotics

This paper surveys recent results in pursuit-evasion and autonomous search relevant to applications in mobile robotics. We provide a taxonomy of search problems that highlights the differences resulting from varying assumptions on the searchers, targets, and the environment. We then list a number of fundamental results in the areas of pursuit-evasion and probabilistic search, and we discuss field implementations on mobile robotic systems. In addition, we highlight current open problems in the area and explore avenues for future work.

On a decentralized active sensing strategy using mobile sensor platforms in a network

In this paper, we consider the problem of active sensing using mobile nodes as a sensor network to estimate the state of a dynamic target. We propose a gradient-search-based decentralized algorithm that demonstrates the benefits of distributed sensing. We then examine the task of tracking multiple targets, and address it via a simple extension to our algorithm. Simulation results show that our simple decentralized approach performs quite well and leads to interesting cooperative behavior.

A decentralized motion coordination strategy for dynamic target tracking

This paper presents a decentralized motion planning algorithm for the distributed sensing of a noisy dynamical process by multiple cooperating mobile sensor agents. This problem is motivated by localization and tracking tasks of dynamic targets. Our gradient-descent method is based on a cost function that measures the overall quality of sensing. We also investigate the role of imperfect communication between sensor agents in this framework, and examine the trade-offs in performance between sensing and communication. Simulations illustrate the basic characteristics of the algorithms

A Decision-Making Framework for Control Strategies in Probabilistic Search

This paper presents the search problem formulated as a decision problem, where the searcher decides whether the target is present in the search region, and if so, where it is located. Such decision-based search tasks are relevant to many research areas, including mobile robot missions, visual search and attention, and event detection in sensor networks. The effect of control strategies in search problems on decision-making quantities, namely time-to-decision, is investigated in this work. We present a Bayesian framework in which the objective is to improve the decision, rather than the sensing, using different control policies. Furthermore, derivations of closed-form expressions governing the evolution of the belief function are also presented. As this framework enables the study and comparison of the role of control for decision-making applications, the derived theoretical results provide greater insight into the sequential processing of decisions. Numerical studies are presented to verify and demonstrate these results

Analysis of Search Decision Making Using Probabilistic Search Strategies

In this paper, we propose a formulation of the spatial search problem, where a mobile searching agent seeks to locate a stationary target in a given search region or declare that the target is absent. The objective is to minimize the expected time until this search decision of targets presence (and location) or absence is made. Bayesian update expressions for the integration of observations, including false-positive and false-negative detections, are derived to facilitate both theoretical and numerical analyses of various computationally efficient (semi-)adaptive search strategies. Closed-form expressions for the search decision evolution and analytic bounds on the expected time to decision are provided under assumptions on search environment and/or sensor characteristics. Simulation studies validate the probabilistic search formulation and comparatively demonstrate the effectiveness of the proposed search strategies.

Communication and sensing trade-offs in decentralized mobile sensor networks: a cross-layer design approach

In this paper we characterize the impact of imperfect communication on the performance of a decentralized mobile sensor network. We first examine and demonstrate the trade-offs between communication and sensing objectives, by determining the optimal sensor configurations when introducing imperfect communication. We further illustrate the performance degradation caused by non-ideal communication links in a decentralized mobile sensor network. To address this, we propose a decentralized motion-planning algorithm that considers communication effects. The algorithm is a cross-layer design based on the proper interface of physical and application layers. Simulation results will show the performance improvement attained by utilizing this algorithm.

Scheduling for distributed sensor networks with single sensor measurement per time step

We examine the problem of distributed estimation when only one sensor can take a measurement per time step. We solve for the optimal recursive estimation algorithm when the sensor switching schedule is given. We then consider the effect of noise in communication channels. We also investigate the problem of determining an optimal sensor switching strategy. We see that this problem involves searching a tree in general and propose two strategies for pruning the tree to minimize the computation. The first is a sliding window strategy motivated by the Viterbi algorithm, and the second one uses thresholding. The performance of the algorithms is illustrated using numerical examples.

Multi-agent probabilistic search in a sequential decision-theoretic framework

Consider the task of searching a region for the presence or absence of a target using a team of multiple searchers. This paper formulates this search problem as a sequential probabilistic decision, which enables analysis and design of efficient and robust search control strategies. Imperfect detections of the targets possible locations are made by each search agent and shared with teammates. This information is used to update the evolving decision variable which represents the belief that the target is present in the region. The sequential decision-theoretic formulation presented in this paper provides an analytic framework to evaluate team search systems, as it includes a performance metric (time until decision), a measure of uncertainty (decision confidence thresholds) and imperfect information gathering (detection error). Strategies for cooperative search are evaluated in this context, and comparisons between homogeneous and hybrid search strategies are investigated in numerical studies.

A probabilistic framework for object search with 6-DOF pose estimation

This article presents a systematic approach to the problem of autonomous 3D object search in indoor environments, using a two-wheeled non-holonomic robot equipped with an actuated stereo-camera head and processing done on a single laptop. A probabilistic grid-based map encodes the likelihood of object existence in each cell and is updated after each sensing action. The updating schema incorporates characteristic parameters modeled after the robot’s sensing modalities and allows for sequential updating via Bayesian recursion methods. Two types of sensing modalities are used to update the map: a coarse search method (global search) based on a color histogram approach, and a more refined search method (local search) based on Scale-Invariant Feature Transform (SIFT) feature matching. If the local search correctly locates the desired object, its 6-DOF pose is estimated using stereo applied to each SIFT feature (i.e. 3D SIFT feature), which is then fed as measurements into an Extended Kalman Filter (EKF) for sustained tracking. If the local search fails to locate the desired object in a particular cell, the cell is updated in the probability map and the next peak probability cell is identified and planned to using a separate grid-based costmap populated via obstacle detection from stereo, with planning done using an A* planner. Experimental results obtained from the use of this method on a mobile robot are presented to illustrate and validate the approach, confirming that the search strategy can be carried out with modest computation on a single laptop.