Author: Defense Documentation Center (U.S.)
Publisher:
ISBN:
Category : Congresses and conventions
Languages : en
Pages : 290

Get Book Here

Book Description

Author: T. D. Earl
Publisher:
ISBN:
Category : Ground-effect machines
Languages : en
Pages : 178

Get Book Here

Book Description

Author: David W. Taylor Naval Ship Research and Development Center
Publisher:
ISBN:
Category : Shipbuilding
Languages : en
Pages : 540

Get Book Here

Book Description

Author: Stanley W. Doroff
Publisher:
ISBN:
Category : Hydrofoil boats
Languages : en
Pages : 644

Get Book Here

Book Description

Author: Harvey R. Chaplin
Publisher:
ISBN:
Category : Ground-effect machines
Languages : en
Pages : 54

Get Book Here

Book Description

Author:
Publisher:
ISBN:
Category : Civil engineering
Languages : en
Pages : 36

Get Book Here

Book Description

Author:
Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 604

Get Book Here

Book Description

Author: Johannes Krzywinski Lunde
Publisher:
ISBN:
Category : Frictional resistance (Hydrodynamics)
Languages : en
Pages : 1182

Get Book Here

Book Description

Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 512

Get Book Here

Book Description

Author: Kimon P. Valavanis
Publisher: Springer Science & Business Media
ISBN: 9048187648
Category : Technology & Engineering
Languages : en
Pages : 519

Get Book Here

Book Description
In the last decade, signi?cant changes have occurred in the ?eld of vehicle motion planning, and for UAVs in particular. UAV motion planning is especially dif?cult due to several complexities not considered by earlier planning strategies: the - creased importance of differential constraints, atmospheric turbulence which makes it impossible to follow a pre-computed plan precisely, uncertainty in the vehicle state, and limited knowledge about the environment due to limited sensor capabilities. These differences have motivated the increased use of feedback and other control engineering techniques for motion planning. The lack of exact algorithms for these problems and dif?culty inherent in characterizing approximation algorithms makes it impractical to determine algorithm time complexity, completeness, and even soundness. This gap has not yet been addressed by statistical characterization of experimental performance of algorithms and benchmarking. Because of this overall lack of knowledge, it is dif?cult to design a guidance system, let alone choose the algorithm. Throughout this paper we keep in mind some of the general characteristics and requirements pertaining to UAVs. A UAV is typically modeled as having velocity and acceleration constraints (and potentially the higher-order differential constraints associated with the equations of motion), and the objective is to guide the vehicle towards a goal through an obstacle ?eld. A UAV guidance problem is typically characterized by a three-dimensional problem space, limited information about the environment, on-board sensors with limited range, speed and acceleration constraints, and uncertainty in vehicle state and sensor data.