Discrete search with multiple sensors

A stationary object is hidden in location i , i =1,2,..., K , with probability p i . There are M sensors available and each location can be searched by at most one sensor at each instant of time. Each search of a location takes one unit of time and is conducted independently of previous searches, so that a search of location i finds the object, if it is in that location, with probability α i . After each search of a location a sensor may either continue to search the same location or switch without any delay to another location. We determine optimal search strategies that maximize the total probability of successful search in N units of time, discuss an implementation of an optimal search strategy, and specify conditions under which the solution can be obtained by a forward induction argument. Finally, we discuss the relationship to multi-armed bandits with multiple plays.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45842/1/186_2004_Article_360.pd

Submodular utility optimization in sensor networks for capacity constraints

With the fast development of wireless sensor network (WSN) technologies, WSNs have widely shifted from a specialized platform for a single application to an integrated infrastructure supporting multiple applications. It is hence a critical problem to allocate multiple applications to multiple sensors in order to maximize user utility subject to various resource constraints. The resulting constrained optimization problem is difficult since it is discrete, nonlinear, and not in closed-form. In this report, we develop an efficient optimization algorithm with rigorous approximation bounds for submodular monotonic optimization with multiple knapsack constraints. Based on a variance reduction formulation, we prove several important theoretical properties, including the monotonicity and submodularity of functions and the multiple knapsack structure of constraints. Then, by exploiting these properties, we propose a local search algorithm with fractional relaxation of constraints and prove the approximation bound that is better than any known results. Experimentally, we verify the theoretical properties on a large dataset from the Intel Berkeley Lab. Comparison against other constrained search algorithms show that our algorithm is superior in both solution time and quality, making it a practical choice for WSN design

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

Recent advances on recursive filtering and sliding mode design for networked nonlinear stochastic systems: A survey

Copyright © 2013 Jun Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Some recent advances on the recursive filtering and sliding mode design problems for nonlinear stochastic systems with network-induced phenomena are surveyed. The network-induced phenomena under consideration mainly include missing measurements, fading measurements, signal quantization, probabilistic sensor delays, sensor saturations, randomly occurring nonlinearities, and randomly occurring uncertainties. With respect to these network-induced phenomena, the developments on filtering and sliding mode design problems are systematically reviewed. In particular, concerning the network-induced phenomena, some recent results on the recursive filtering for time-varying nonlinear stochastic systems and sliding mode design for time-invariant nonlinear stochastic systems are given, respectively. Finally, conclusions are proposed and some potential future research works are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61329301, 61333012, 61374127 and 11301118, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant no. GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Closed-loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search

In search applications, autonomous unmanned vehicles must be able to efficiently reacquire and localize mobile targets that can remain out of view for long periods of time in large spaces. As such, all available information sources must be actively leveraged -- including imprecise but readily available semantic observations provided by humans. To achieve this, this work develops and validates a novel collaborative human-machine sensing solution for dynamic target search. Our approach uses continuous partially observable Markov decision process (CPOMDP) planning to generate vehicle trajectories that optimally exploit imperfect detection data from onboard sensors, as well as semantic natural language observations that can be specifically requested from human sensors. The key innovation is a scalable hierarchical Gaussian mixture model formulation for efficiently solving CPOMDPs with semantic observations in continuous dynamic state spaces. The approach is demonstrated and validated with a real human-robot team engaged in dynamic indoor target search and capture scenarios on a custom testbed.Comment: Final version accepted and submitted to 2018 FUSION Conference (Cambridge, UK, July 2018

Surveying human habit modeling and mining techniques in smart spaces

A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field

Strategy Synthesis for Autonomous Agents Using PRISM

We present probabilistic models for autonomous agent search and retrieve missions derived from Simulink models for an Unmanned Aerial Vehicle (UAV) and show how probabilistic model checking and the probabilistic model checker PRISM can be used for optimal controller generation. We introduce a sequence of scenarios relevant to UAVs and other autonomous agents such as underwater and ground vehicles. For each scenario we demonstrate how it can be modelled using the PRISM language, give model checking statistics and present the synthesised optimal controllers. We conclude with a discussion of the limitations when using probabilistic model checking and PRISM in this context and what steps can be taken to overcome them. In addition, we consider how the controllers can be returned to the UAV and adapted for use on larger search areas