1,594 research outputs found
Market-Based Approach to Mobile Surveillance Systems
The active surveillance of public and private sites is increasingly becoming a very important and critical issue. It is, therefore, imperative to develop mobile surveillance systems to protect these sites. Modern surveillance systems encompass spatially distributed mobile and static sensors in order to provide effective monitoring of persistent and transient objects and events in a given area of interest (AOI). The realization of the potential of mobile surveillance requires the solution of different challenging problems such as task allocation, mobile sensor deployment, multisensor management, cooperative object detection and tracking, decentralized data fusion, and interoperability and accessibility of system nodes. This paper proposes a market-based approach that can be used to handle different problems of mobile surveillance systems. Task allocation and cooperative target tracking are studied using the proposed approach as two challenging problems of mobile surveillance systems. These challenges are addressed individually and collectively
Efficient delay-tolerant particle filtering
This paper proposes a novel framework for delay-tolerant particle filtering
that is computationally efficient and has limited memory requirements. Within
this framework the informativeness of a delayed (out-of-sequence) measurement
(OOSM) is estimated using a lightweight procedure and uninformative
measurements are immediately discarded. The framework requires the
identification of a threshold that separates informative from uninformative;
this threshold selection task is formulated as a constrained optimization
problem, where the goal is to minimize tracking error whilst controlling the
computational requirements. We develop an algorithm that provides an
approximate solution for the optimization problem. Simulation experiments
provide an example where the proposed framework processes less than 40% of all
OOSMs with only a small reduction in tracking accuracy
GRASP News Volume 9, Number 1
A report of the General Robotics and Active Sensory Perception (GRASP) Laboratory
Multiple-objective sensor management and optimisation
One of the key challenges associated with exploiting modern Autonomous Vehicle technology for military surveillance tasks is the development of Sensor Management strategies which maximise the performance of the on-board Data-Fusion systems. The focus of this thesis is the development of Sensor Management algorithms which aim to optimise target tracking processes. Three principal theoretical and analytical contributions are presented which are related to the manner in which such problems are formulated and subsequently solved.Firstly, the trade-offs between optimising target tracking and other system-level objectives relating to expected operating lifetime are explored in an autonomous ground sensor scenario. This is achieved by modelling the observer trajectory control design as a probabilistic, information-theoretic, multiple-objective optimisation problem. This novel approach explores the relationships between the changes in sensor-target geometry that are induced by tracking performance measures and those relating to power consumption. This culminates in a novel observer trajectory control algorithm based onthe minimax approach.The second contribution is an analysis of the propagation of error through a limited-lookahead sensor control feedback loop. In the last decade, it has been shown that the use of such non-myopic (multiple-step) planning strategies can lead to superior performance in many Sensor Management scenarios. However, relatively little is known about the performance of strategies which use different horizon lengths. It is shown that, in the general case, planning performance is a function of the length of the horizon over which the optimisation is performed. While increasing the horizon maximises the chances of achieving global optimality, by revealing information about the substructureof the decision space, it also increases the impact of any prediction error, approximations, or unforeseen risk present within the scenario. These competing mechanisms aredemonstrated using an example tracking problem. This provides the motivation for a novel sensor control methodology that employs an adaptive length optimisation horizon. A route to selecting the optimal horizon size is proposed, based on a new non-myopic risk equilibrium which identifies the point where the two competing mechanisms are balanced.The third area of contribution concerns the development of a number of novel optimisation algorithms aimed at solving the resulting sequential decision making problems. These problems are typically solved using stochastic search methods such as Genetic Algorithms or Simulated Annealing. The techniques presented in this thesis are extensions of the recently proposed Repeated Weighted Boosting Search algorithm. In its originalform, it is only applicable to continuous, single-objective, ptimisation problems. The extensions facilitate application to mixed search spaces and Pareto multiple-objective problems. The resulting algorithms have performance comparable with Genetic Algorithm variants, and offer a number of advantages such as ease of implementation and limited tuning requirements
Estimating and exploiting the degree of independent information in distributed data fusion
Double counting is a major problem in distributed data fusion systems. To maintain flexibility and scalability, distributed data fusion algorithms should just use local information. However globally optimal solutions only exist in highly restricted circumstances. Suboptimal algorithms can be applied in a far wider range of cases, but can be very conservative.
In this paper we present preliminary work to develop
distributed data fusion algorithms that can estimate and
exploit the correlations between the estimates stored in
different nodes in a distributed data fusion network.
We show that partial information can be modelled as
kind of “overweighted” Covariance Intersection algorithm. We motivate the need for an adaptive scheme
by analysing the correlation behaviour of a simple distributed data fusion network and show that it is complicated and counterintuitive. Two simple approaches
to estimate the correlation structure are presented and
their results analysed. We show that significant advantages can be obtained
\u3cem\u3eGRASP News\u3c/em\u3e: Volume 9, Number 1
The past year at the GRASP Lab has been an exciting and productive period. As always, innovation and technical advancement arising from past research has lead to unexpected questions and fertile areas for new research. New robots, new mobile platforms, new sensors and cameras, and new personnel have all contributed to the breathtaking pace of the change. Perhaps the most significant change is the trend towards multi-disciplinary projects, most notable the multi-agent project (see inside for details on this, and all the other new and on-going projects). This issue of GRASP News covers the developments for the year 1992 and the first quarter of 1993
Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environment
We study a search and tracking (S&T) problem where a team of dynamic search
agents must collaborate to track an adversarial, evasive agent. The
heterogeneous search team may only have access to a limited number of past
adversary trajectories within a large search space. This problem is challenging
for both model-based searching and reinforcement learning (RL) methods since
the adversary exhibits reactionary and deceptive evasive behaviors in a large
space leading to sparse detections for the search agents. To address this
challenge, we propose a novel Multi-Agent RL (MARL) framework that leverages
the estimated adversary location from our learnable filtering model. We show
that our MARL architecture can outperform all baselines and achieves a 46%
increase in detection rate.Comment: Accepted by IEEE International Symposium on Multi-Robot & Multi-Agent
Systems (MRS) 202
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