40,559 research outputs found
Coordinated Targeting of Mobile Sensor Networks for Ensemble Forecast Improvement
No full textThis paper presents an efficient targeting algorithm to coordinate a team of mobile sensor platforms in order to extract information from the natural environment for the purpose of improved forecasting. This coordinated targeting is complicated by the large dimensionality of the natural dynamic systems (and thus of the decision space), as well as by the constraints in the vehicle motions. While the backward formulation developed by the present authors provides a baseline framework to efficiently address the dimensionality challenge in an unconstrained setting, the key contributions of this paper are twofold: (a) to delineate how to effectively incorporate the sensor platform constrained mobility in the targeting process and (b) to demonstrate the importance of the interteam information sharing to achieve good targeting performance. Numerical examples of simplified weather forecasting verify that the presented method renders good targeting solutions while retaining computational tractability, which is crucial for the design of sensor networks that tightly interact with, and rapidly adapt to, large-scale dynamic environments.This work is funded by NSF CNS-0540331 as part of the DDDAS program with Dr. Frederica Darema as the overall program manager. The authors thank Dr. James A. Hansen for invaluable discussions on ensemble-based targeting and weather models
Efficient Targeting of Sensor Networks for Large-Scale Systems
No full textThis paper proposes an efficient approach to an observation targeting problem that is complicated by a combinatorial number of targeting choices and the large dimension of the system state, when the goal is to minimize the uncertainty in some quantities of interest. The primary improvements in the efficiency are obtained by computing the impact of each possible measurement choice on the uncertainty reduction backwards. This backward method provides an equivalent solution to a traditional forward approach under some standard assumptions, while removing the requirement of calculating a combinatorial number of covariance updates. A key contribution of this paper is to prove that the backward approach operates never slower than the forward approach, and that it works significantly faster than the forward one for ensemble-based representations. The primary benefits are shown on a simplified weather problem using the Lorenz-95 model.This work is funded by NSF CNS-0540331 as part of the DDDAS program with Dr. Frederica Darema as the overall program manager. The authors thank Dr. James A. Hansen for invaluable discussions on ensemble-based
targeting and weather models
A multi-UAV targeting algorithm for ensemble forecast improvement
No full textThis work is funded by NSF CNS-0540331 as part of the DDDAS program with Dr. Frederica
Darema as the overall program manager
Continuous Motion Planning for Information Forecast
No full textThis work is funded by NSF CNS-0540331 as part of the DDDAS program with Dr. Frederica Darema as the overall program manager. The authors thank Luca Bertuccelli for
insightful discussions
Chance Constrained RRT for Probabilistic Robustness to Environmental Uncertainty
No full texturl is to conference schedule where talk is listed.For motion planning problems involving many or unbounded forms of uncertainty, it may not be possible to identify a path guaranteed to be feasible, requiring consideration of the trade-off between planner conservatism and the risk of infeasibility. This paper presents a novel real-time planning algorithm, chance constrained rapidly-exploring random trees (CC-RRT), which uses chance constraints
to guarantee probabilistic feasibility for linear systems subject to process noise and/or uncertain, possibly dynamic obstacles. By using RRT, the algorithm enjoys the computational benefits of sampling-based algorithms, such as trajectory-wise constraint checking and incorporation of heuristics, while explicitly incorporating uncertainty within the formulation. Under the assumption of Gaussian noise, probabilistic feasibility at each time step can be established through simple simulation of the state conditional mean and the evaluation of linear constraints. Alternatively,
a small amount of additional computation can be used to explicitly compute a less conservative probability bound at each time step. Simulation results show that this algorithm can be used for efficient identification and execution of probabilistically safe paths in real time.United States. Dept. of the Air Force (AFOSR grant FA9550-08-1-0086
Mobile Agent Trajectory Prediction using Bayesian Nonparametric Reachability Trees
No full textThis paper presents an efficient trajectory prediction algorithm that has been developed to improve the performance of future collision avoidance and detection systems. The main idea is to embed the inferred intention information of surrounding agents into their estimated reachability sets to obtain a probabilistic description of their future paths. More specifically, the proposed approach combines the recently developed RRT-Reach algorithm and mixtures of Gaussian Processes. RRT-Reach was introduced by the authors as an extension of the closed-loop rapidly-exploring random tree (CL-RRT) algorithm to compute reachable sets of moving objects in real-time. A mixture of Gaussian processes (GP) is a flexible nonparametric Bayesian model used to represent a distribution over trajectories and have been previously demonstrated by the authors in a UAV interception and tracking of ground vehicles planning scheme. The mixture is trained using typical maneuvers learned from statistical data, and RRT-Reach utilizes samples from the GP to grow probabilistically weighted feasible paths of the surrounding vehicles. The resulting approach, denoted as RR-GP, has RRTReach's benefits of computing trajectories that are dynamically feasible by construction, therefore efficiently approximating the reachability set of surrounding vehicles following typical patterns. RRT-GP also features the GP mixture's benefits of providing a probabilistic weighting on the feasible trajectories produced by RRTReach, allowing our system to systematically weight trajectories by their likelihood. A demonstrative example on a car-like vehicle illustrates the advantages of the RR-GP approach by comparing it to two other GP-based algorithms. © 2011 by Professor Jonathan P. How, Massachusetts Institute of Technology. Published by the American Institute of Aeronautics and Astronautics, Inc
Region of attraction comparison for gradient projection anti-windup compensated systems
No full textThe gradient projection anti-windup (GPAW) scheme was recently proposed for saturated multi-input-multi-output (MIMO) nonlinear systems driven by MIMO nonlinear controllers, a topic recognized as an open problem in a recent survey paper. Thus far, stability results for GPAW compensated systems are restricted to the simple case of a saturated first order linear time invariant (LTI) plant driven by a first order LTI controller. Here, we present a region of attraction (ROA) comparison result for general GPAW compensated regulatory systems. The ROA comparison result is demonstrated on a simple planar nonlinear system, which also highlights the limitations of existing state-of-the-art anti-windup results
Continuous Trajectory Planning of Mobile Sensors for Informative Forecasting
No full textThis paper addresses planning of continuous paths for mobile sensors to reduce uncertainty in some quantities of interest in the future.
The mutual information between the continuous measurement path and the future verification variables defines the information reward.
Two expressions for computing this mutual information are presented: the filter form extended from the state-of-the-art and the smoother
form inspired by the conditional independence structure. The key properties of the approach using the filter and smoother strategies are
presented and compared. The smoother form is shown to be preferable because it provides better computational efficiency, facilitates easy
integration with existing path synthesis tools, and most importantly, enables correct quantification of the rate of information accumulation.
A spatial interpolation technique is used to relate the motion of the sensor to the evolution of the measurement matrix, which leads to
the formulation of the optimal path planning problem. A gradient-ascent steering law based on the concept of information potential field
is also presented as a computationally efficient suboptimal strategy. A simplified weather forecasting example is used to compare several
planning methodologies and to illustrate the potential performance benefits of using the proposed planning approach.National Science Foundation (CNS-0540331
Probabilistic Feasibility for Nonlinear Systems with Non-Gaussian Uncertainty using RRT
No full textFor motion planning problems involving many or unbounded forms of uncertainty, it may
not be possible to identify a path guaranteed to be feasible, requiring consideration of the
trade-o between planner conservatism and the risk of infeasibility. Recent work developed
the chance constrained rapidly-exploring random tree (CC-RRT) algorithm, a real-time
planning algorithm which can e ciently compute risk at each timestep in order to guarantee
probabilistic feasibility. However, the results in that paper require the dual assumptions of
a linear system and Gaussian uncertainty, two assumptions which are often not applicable
to many real-life path planning scenarios. This paper presents several extensions to the
CC-RRT framework which allow these assumptions to be relaxed. For nonlinear systems
subject to Gaussian process noise, state distributions can be approximated as Gaussian by
considering a linearization of the dynamics at each timestep; simulation results demonstrate
the e ective of this approach for both open-loop and closed-loop dynamics. For systems
subject to non-Gaussian uncertainty, we propose a particle-based representation of the
uncertainty, and thus the state distributions; as the number of particles increases, the
particles approach the true uncertainty. A key aspect of this approach relative to previous
work is the consideration of probabilistic bounds on constraint satisfaction, both at every
timestep and over the duration of entire paths.United States. Air Force (USAF, grant FA9550-08-1-0086)United States. Air Force Office of Scientific Research (AFOSR, Grant FA9550-08-1-0086
An online algorithm for constrained POMDPs
No full textThis work seeks to address the problem of planning in the presence of uncertainty and constraints. Such problems arise in many situations, including the basis of this work, which involves planning for a team of first responders (both humans and robots) operating in an urban environment. The problem is framed as a Partially-Observable Markov Decision Process (POMDP) with constraints, and it is shown that even in a relatively simple planning problem, modeling constraints as large penalties does not lead to good solutions. The main contribution of the work is a new online algorithm that explicitly ensures constraint feasibility while remaining computationally tractable. Its performance is demonstrated on an example problem and it is demonstrated that our online algorithm generates policies comparable to an offline constrained POMDP algorithm.United States. Office of Naval Research (grant N00014-07-1-0749
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