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Author: Ziyi Wang
Publisher: OAE Publishing Inc.
ISBN:
Category : Computers
Languages : en
Pages : 27
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Book Description
This paper focuses on the problem of regional cooperative search using multiple unmanned aerial vehicles (UAVs) for targets that have the ability to perceive and evade. When UAVs search for moving targets in a mission area, the targets can perceive the positions and flight direction of UAVs within certain limits and take corresponding evasive actions, which makes the search more challenging than traditional search problems. To address this problem, we first define a detailed motion model for such targets and design various search information maps and their update methods to describe the environmental information based on the prediction of moving targets and the search results of UAVs. We then establish a multi-UAV search path planning optimization model based on the model predictive control, which includes various newly designed objective functions of search benefits and costs. We propose a priority-encoded improved genetic algorithm with a fine-adjustment mechanism to solve this model. The simulation results show that the proposed method can effectively improve the cooperative search efficiency, and more targets can be found at a much faster rate compared to traditional search methods.
Author: Ziyi Wang
Publisher: OAE Publishing Inc.
ISBN:
Category : Computers
Languages : en
Pages : 27
Get Book Here
Book Description
This paper focuses on the problem of regional cooperative search using multiple unmanned aerial vehicles (UAVs) for targets that have the ability to perceive and evade. When UAVs search for moving targets in a mission area, the targets can perceive the positions and flight direction of UAVs within certain limits and take corresponding evasive actions, which makes the search more challenging than traditional search problems. To address this problem, we first define a detailed motion model for such targets and design various search information maps and their update methods to describe the environmental information based on the prediction of moving targets and the search results of UAVs. We then establish a multi-UAV search path planning optimization model based on the model predictive control, which includes various newly designed objective functions of search benefits and costs. We propose a priority-encoded improved genetic algorithm with a fine-adjustment mechanism to solve this model. The simulation results show that the proposed method can effectively improve the cooperative search efficiency, and more targets can be found at a much faster rate compared to traditional search methods.
Author: Azima Motaghi
Publisher:
ISBN: 9781303920318
Category : Drone aircraft
Languages : en
Pages : 85
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Book Description
The ability to track targets using Unmanned Aerial Vehicles (UAVs) has a wide range of civilian and military applications. For example, for military personnel, it is critical to track and locate a variety of objects, including the movement of enemy vehicles. For civilian applications, we can easily find UAVs performing tasks related to land survey, weather forecasting, search and rescue missions, and monitoring farm crops. This study presents a novel method for determining the locations of moving ground-based targets using UAVs and human operators. In previous research, Sharma et al. [1] developed a vision-based target tracking algorithm. They used a Kalman-filter to estimate the target's position and velocity. An information-filter was used to control the sensor. Targets were geo-localized using the pixel locations of the targets in an image. The measurements of the UAV position, altitude, and camera pose angle along with the information embedded in the image provide the required input to an estimator to geo-locate ground targets. Using the highly sophisticated skills of humans for sensing environments, we are interested in integrating the abilities of human operators as a part of sensor network. The main contribution of this thesis is a cyber-physical system developed for reducing the localization errors of targets observed by either UAVs or humans working cooperatively. In particular, in the process of developing the system, we developed (1) an Extended Kalman Filter (EKF) based algorithm to estimate the positions of multiple targets, (2) a human sensor model using neural networks, and (3) a weighted filter to fuse local target estimations from multiple UAVs. Human sensor inputs were utilized to improve the geo localization accuracy of target position estimates. This technique requires operators to be equipped with an Android device; providing operators an easy access to Google map and Global Positioning System (GPS); such that they can specify a target's position on the map. Each sensor, UAVs or human operators, exchanges data through a Wi-Fi sensor network. A central station is used to collect the information observed by independent sensors for data fusion and combines them to generate more accurate estimates that would not be available from any single UAV or a human operator. The capability of the system was demonstrated using simulation results and Android hardware.
Author: Kevin M. Morris
Publisher:
ISBN:
Category : Drone aircraft
Languages : en
Pages : 250
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Book Description
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 5
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The purpose of this tutorial paper is to present an application example for the MultiUAV cooperative control simulation. MultiUAV has been used to simulate a Cooperative Moving Target Engagement (CMTE) scenario, with a team of UAVs acting as a sensor and communication network to cooperatively track and attack moving ground targets. This scenario illustrates the utility of MultiUAV for cooperative control applications requiring heterogeneous vehicles with varied sensor, communication, dynamic, and weapon capabilities. A human supervisor designates one or more moving ground targets for the vehicles to attack. The vehicle agents must then autonomously and cooperatively determine which vehicles will perform the required tasks, when the tasks will be performed, and what flight paths will be used. This requires assigning time-dependent cooperative and joint tasks, where multiple sub-elements of the primary task must be accomplished by different vehicles, for any of the tasks to have value. This tutorial focuses on the unique requirements of the CMTE scenario and how they are addressed in the MultiUAV simulation.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 15
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Book Description
A problem of assigning multiple unmanned aerial vehicles (UAVs) to simultaneously perform cooperative tasks on consecutive targets is posed as a new NP-hard combinatorial optimization problem. The investigated scenario consists of multiple ground moving targets prosecuted by a team of heterogeneous UAVs carrying designated sensors and/or weapons. To successfully prosecute each target it first needs to be simultaneously tracked by multiple UAVs, from significantly different line of sight angles to reduce the position estimate errors, and then attacked by a different UAV carrying a weapon. Even for small sized scenarios, the problem has prohibitive computational complexity for classical combinatorial optimization methods due to timing constraints on the simultaneous tasks and the coupling between task assignment and path planning for each UAV. A genetic algorithm (GA) is proposed for efficiently searching the space of feasible solutions. A matrix representation of the GA chromosomes simplifies the encoding process and the application of the genetic operators. To further simplify the encoding, the chromosome is composed of sets of multiple genes, each corresponding to the entire set of assignments on each target. Simulation results conform the viability of the proposed assignment algorithm for different sized scenarios. The sensitivity of the performance to variations in GA tuning parameters is also investigated.
Author: M. A. Shah
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
In this research Pythagorean Hodograph based path planning and camera based cooperative perception are investigated separately and then these two entirely separate areas (Path Planning and Perception) are integrated for the application in online pop-up obstacle locating & avoidance and moving target tracking & surveillance in dynamic environments. The path planning is integrated with the cooperative perception to deal with the challenges posed by the dynamic environment. The aim of this integration is to achieve maximum autonomy required to execute a mission autonomously by multiple fixed wings UAVs in a dynamic environment. During the mission execution, the cooperating UAVs start from some initial location in the operating environment and finish at some final location while trying to achieve the mission's objectives in a cooperative way. Naturally planning a feasible (safe and flyable) path for each participating UAV from initial position to a final location becomes a compulsory task of mission planning. For fixed wing UAVs flyable paths mean, paths which have tangential and curvature continuity and which obey the kinematic and dynamic constraint of the UAVs. In this research an algorithm based on Pythagorean hodograph curves is developed and used for planning feasible (safe and flyable) paths. The Pythagorean hodograph (PH) yields paths of exact length having tangential and curvature continuity. These continuous paths are made flyable for the UAVs by imposing the kinematic constraints of the UAVs. These constraints are imposed by the curvature and torsion manipulation of the planned paths. The safety of these paths is ensured by making it free of inter collisions between the vehicles and collisions with the known obstacles. These feasible paths are known as the initial paths or reference trajectories. In this research the operating environment is assumed to be dynamic in which changes are taking place at all times. Each UAV taking part in the mission is equipped with a vision sensor to perceive these changes continuously in a cooperative way. As the mission is assumed to be executed in day light, therefore light intensity video camera is used as a vision sensor. A perception algorithm for locating an object cooperatively in 3D is developed in this research. This algorithm is based on the optimization of errors in target position acquired by the on board camera. The algorithm is used by the cooperative perception system for optimal position estimation of the object in the scene. The target position information between the participating UAVs is exchanged through wireless communication for data fusion purposes. After developing efficient algorithms for path planning and cooperative perception, the two algorithms are integrated to be used in reactive obstacle avoidance and target tracking. During the mission, when the UAVs start their flight on the reference trajectories generated by the path planning algorithm, the perception algorithm comes into action. During the travel on these paths if the perception system of any of the UAVs detects an interrupting obstacle which was not known a priori in the map, then the exact location of this obstacle is determined with the help of the perception algorithm in a cooperative way. Using the location of the interrupting obstacle determined by the perception algorithm the path planning algorithm plans an evasive manoeuvre for the corresponding UAV to avoid it. After avoiding the obstacle the UAV comes back to its reference trajectory as soon as possible. In the operation of surveillance and tracking during the mission, the onboard perception algorithm locates an object of interest dynamically and the Pythagorean hodograph (PH) path planner uses this location to generate the paths for the cooperating UAVs to keep in close proximity of the target. In this case the close proximity of the target means to follow the moving target in such a way that it remains in the fields of views of the UAVs cameras at any time. By this integration of path planning and cooperative perception the continuous surveillance and tracking of the target was made possible even when the individual UAV experiences failure. During this research the mid flight obstacle locating & avoidance, and target surveillance & tracking have been successfully achieved by the integration of the path planning and cooperative perception. The purpose of this integration is to achieve an enhanced autonomy for the cooperating group of UAVs to increase the probability of their survival in mission being executed in dynamic environments.
Author: Andrew Ke-Ping Sun
Publisher:
ISBN: 9780494600184
Category : Aerospace engineering
Languages : en
Pages : 73
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Book Description
In this thesis, a solution to the multi Unmanned Aerial Vehicle (UAV) search and intercept problem for a moving target is presented. For the search phase, an adapted diffusion-based algorithm is used to manage the target uncertainty while individual UAVs are controlled with a hybrid receding horizon/potential method. The coordinated search is made possible by an uncertainty weighting process. The team intercept phase algorithm is a behavioural approach based on the analytical solution of Isaac's Single-Pursuer/Single-Evader (SPSE) homicidal chauffeur problem. In this formulation, the intercepting control is taken to be a linear combination of the individual SPSE controls that would exist for each of the evader/pursuer pairs. A particle swarm optimizer is applied to find approximate optimal weighting coefficients for discretized intervals of the game time. Simulations for the team search, team intercept and combined search and intercept problem are presented.
Author: Andrew Ke-Ping Sun
Publisher:
ISBN:
Category : Aerospace engineering
Languages : en
Pages : 73
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Book Description
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 74
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Book Description
Unmanned Aerial Vehicles (UAVs) have been recently used in many areas due to their ability to perform almost as efficiently as piloted aircrafts, at low cost, without endangering human life. In this research, we consider a fleet of UAVs performing a search mission in a bounded area where static targets are located. The search strategy is a cooperative one, rather than a centralized one due to low bandwidth and dynamics in the system. Cooperation has been noted in the literature to be very important for multi-vehicle control systems such as the one considered in this research. Our solution method is a Dynamic Programming (DP) algorithm for computing the trajectories of multiple UAVs from a mission starting point with the objective of cooperatively searching the set of fixed targets. The algorithm presented in this research calculates a gain function for maximizing the number of targets found in the area. Each vehicle maintains a (dynamic) cognitive map of probabilities of indicating where the targets are likely to exist and where other vehicles have already been routed. (Abstract shortened by UMI.).
Author: Matthew D. Flint
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
The work presented here is part of a research program being conducted in the area of decision and control for autonomous unmanned aerial vehicles (UAV2s). Speci.cally, the formulation is presented for the problem of generating near-optimal trajectories to follow in order for several UAV2s to cooperatively search for targets in a given area for which some a priori data about target distribution is available. In order to solve this problem, a discrete time decision model is created. The solution based on this model is presented, which utilizes a dynamic programming approach, implemented with a best-.rst search algorithm. This solution predicts the best path for individual vehicles to take under constraints on movement and computational power. A key reduction in computational complexity as compared to the ideal case is made by utilizing a limited look-ahead policy and by modeling other vehicles as stochastic elements. The formulation is .exible enough to respond to additional goals and restrictions, also. A set of simulation studies is provided that shows the utility of this approach. The proposed method is demonstrated against a standard search, and another method that currently exists in the literature.
