1,721,138 research outputs found
A potential game approach for distributed cooperative sensing for maximum mutual information
No full textAlignment and ortho-rectification of lunar surface image using the NASA Ames Stereo Pipeline
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Hybrid Control Trajectory Optimization for Air-breathing Hypersonic Vehicle
No full textTrajectory optimization problem for air-breathing hypersonic vehicle is addressed in this paper. The engine of hypersonic vehicle is assumed as a dual-mode scramjet engine which can be operated as a ramjet and scramjet for wide range of flight Mach number. Boost-skipping trajectory was proposed for range maximization of hypersonic vehicle, and based on this trajectory, flight modes of dual-mode scramjet are divided into three modes, which are ram mode, scram mode, non-powered mode. Hypersonic vehicle was modelled with consideration of changes of physical quantities over mode transition. To deal with discrete mode changes as well as continuous control, hybrid optimal control method is applied to this problem. Simulation results demonstrate that the optimized trajectory with hybrid control has better performance compared to cyclic mode transition trajectory. Also, a vehicle which imitates the characteristics of dual-mode scramjet vehicle is implemented to optimize the trajectory. The results suggest that the hybrid optimal control can be applied to the trajectory optimization of a dual-mode scramjet vehicle considering the mode transition in infinite time horizon. Copyright (C) 2020 The Authors
Informative windowed forecasting of continuous-time linear systems for mutual information-based sensor planning
No full textThis paper presents an expression of mutual information that defines the information gain in planning of sensing resources, when the goal is to reduce the forecast uncertainty of some quantities of interest and the system dynamics is described as a continuous-time linear system. The method extends the smoother approach in Choi and How (2010b) to handle a more general notion of the verification entity continuous sequence of variables over some finite time window in the future. The expression of mutual information for this windowed forecasting case is derived and quantified, taking advantage of an underlying conditional independence structure and utilizing a two-filter formula for fixed-interval smoothing with correlated noises. Two numerical examples on (a) a two-state linear system with time-varying one-way coupling dynamics, and (b) idealized weather forecasting with moving verification paths demonstrate the validity of the proposed quantification methodology.
Topology optimization of hierarchical sensor fusion network considering time delay
No full textTime delay in sensor fusion is one of the main obstacles to situational awareness and can be mitigated through sensor network planning. This paper deals with topology optimization problem considering time delay in hierarchical sensor fusion network. For this purpose, the network utility maximization (NUM) problem is formulated by quantifying the gain from the information fusion of the local sensor nodes considering the time delay by borrowing the mutual information form, and the centralized optimization is performed using the genetic algorithm
Vehicle Routing Problem with Pickup, Relay, and Delivery: Delivery Task Assignment in Hybrid-Transit Logistics Network
No full textA Traveling Salesman Problem-Based Approach to Observation Scheduling for Satellite Constellation
No full textThis paper addresses observation task scheduling of a heterogeneous satellite constellation in low-Earth-orbits. The goal of scheduling is to find a sequence and times for observing the ground objects to maximize the sum of values of the observed ground objects while satisfying all the constraints associated with complex mission specifications. The method develops an instance of the asymmetric traveling salesman problem (ATSP) for this scheduling, and solves the resulting ATSP using a well-established Lin-Kernighan-Helsgaun algorithm. Numerical experiments demonstrate the characteristics, efficiency, and scalability of the proposed scheduling approach, in particular, compared to the first-in first-out strategy-based greedy algorithm.
Partial agreement task assignment algorithm for secure plan consensus in multi-agent system
No full textThis paper presents a secure market based task assignment algorithm for decentralized multi-agent system. In a decentralized algorithm, individual agents calculate their solution with imperfect information of environments. To achieve agreement in the system, the plan consensus solves local task assignment problems and reach a conflict-free solution through the plan exchange. The Consensus Based Bundle Algorithm(CBBA), which is well-known consensus based plan auction method, guarantees feasible and conflict-free solution. However, in the plan consensus protocol, each agent determines tasks only through bidding for each task like maximum bid consensus. It is vulnerable to malicious data to attack a consensus process or solution result. Therefore, providing security to the decentralized agent system is a major issue. The main contribution of this paper is an algorithm, termed partial-agreement consensus-based bundle algorithm, that proposed for two types of attacks, exaggerated score attack and changing score attack. Partial agreement of the agent with some of the mission plans resists attacks that are intended to degrade the overall plan. The security solution is a more robust task allocation algorithm with a partial agreement on the plans and task bidding data
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