File Name: guidance navigation and control .zip
Jump to navigation. The functions of Guidance, Navigation, and Control are vital to all forms of air and space flight. The Space History collections in this area attempt to reflect that significance and illustrate the breadth of the topic.
Download PDF. A short summary of this paper. PrefaceOver the last few decades, both aeronautics and astronautics have been strong motors for the advancement of control systems theory and application, as well as the fields of sensors, data fusion and navigation.
Many of those achievements that earned aerospace the reputation as a synonym for high tech and progress have been enabled by innovations in guidance, navigation and control. Today, aerospace is still one of the driving applications in those fields, which stems from the special characteristics and needs of that segment. In an airplane seating hundreds of people, you cannot use the latest control algorithm to find out if it works by trial and error.
In a deep space probe sailing for new shores, you need to do things right on the first and only attempt; unlike a car or a microwave oven you cannot test the integrated system in real operation before its actual mission. You cannot simply put a bulky machined box with standard components which works in "living room" environmental conditions in the slender body of an agile missile.
Things are also quite different on the algorithm side. Aerospace systems can have highly nonlinear and strongly coupled dynamics. The ranges of altitude, Mach number, center of gravity and weight are enormous and the dynamics can significantly change with those parameters. Huge uncertainties can still remain in spite of costly modeling efforts. The range of time scales contributing to the system dynamics is large, speeds are higher, the environment is harsher and changing more rapidly, the distances travelled are much larger and the operation times for some systems are much longer than in other fields.
To summarize, the challenges in the aerospace disciplines are unique and more demanding than in other domains. If these challenges were not enough, appropriate solutions must also be reliable, highly accurate, highly available, safe and must guarantee a well-defined performance level, even under a large variety of circumstances like system failure. An airplane cannot turn right and stop at the next cloud if things go wrong. All these challenges must be accomplished under mass, volume, power and cost constraints.
This may sound like praise for aerospace, its scientists and engineers, but however you wish to see it, it is a viable explanation as to why, in contrast to other fields, there have always been dedicated conferences on "flight control", "space navigation" and "missile guidance", as specialized sessions at general conferences are not enough. The American AIAA Guidance, Navigation and Control conference serves as a brilliant example where the community of "rocket scientists" gathers to present on and to discuss these specific topics.
IntroductionAutonomous flight in densely populated environment like urban terrain generally requires excellent maneuverability. Hence, helicopter-based UAV platforms are preferred. Especially for dynamic high speed flight, changes like moving obstacles or mission updates can require such complex control platforms to be equipped with an onboard motion planning system.
As the survey by Goerzen  points out, a sole precision tracking of a precomputed trajectory is not a feasible overall solution. Dynamic constraints, atmospheric conditions, uncertainty in the vehicle state estimates, and limited knowledge about the environment may leave no chance to follow a precomputed plan precisely.
Approaches for path smoothing 1 exist  that allow to generate continuously differentiable, timely annotated paths. Moreover, given a feasible flight control system, a number of path following solutions exist [3,4] even for commercial, black box autopilots .
Thus, this work proposes a decoupled approach to motion planning. Previous work by Andert and Adolf  indicates that a thorough problem decomposition is one key to enable realtime sensor fusion, obstacle modeling, and 3D motion planning during flight. This work develops this idea further by decomposing the trajectory generation problem in decoupled subsequent layers.
Although path and trajectory planning may have the same meaning, there is a fundamental difference between them. In this context a path is defined as the interpolation of position coordinates. Trajectories refer to timely annotated paths, e. Even a hover-capable vehicle cannot perform arbitrary yaw turns during fast forward flight, such that an adequate yaw attitude command must be determined. A common approach is to use an instantaneous tangent along a path. A continuously differentiable path geometry should be preferred to enable smooth transitions.
The trajectory will be vehicle specific. The configuration space 2 may be altered if the vehicle properties or the characteristics of the environment change. Generally, path search uses only simplified dynamic constraints and concentrates on collision-free path segments, often with various geometries e. Then a time dimension is added to the path yielding a trajectory by defining the velocity profile over the path geometry.
Instantaneous vehicle state estimates are used together with the path slope in order to provide a feasible input to a trajectory following controller. The nonlinear plant will be controlled by a baseline control system that maintains desired velocities. More details on the control system can be found in [7,8].
The following section starts with the path definition. Based on the geometry of the path the trajectory definition will be presented in section 3. The trajectory definition includes the determination of the velocity profile as well as an approach to account for acceleration limitations. Section 4 presents the trajectory following control system.
A summary of the overall approach and future research directions are discussed in section 5. Path DefinitionIn general, a smooth path should be continuously differentiable. Due to the resulting complexity of generating a collision-free path through narrow passages a function of reduced order is selected. Most path planners avoid obstacles by generating coordinates that divide the path from start to end position into consecutive segments.
These path segments use cubic splines that are continuously differentiable up to the third derivative. A large safety margin accounting for path following errors can therefore be avoided.
Furthermore, the path planner utilized in this work assures a collision-free sphere volume e. Note that it may insert other path geometries into spline segments e.
A univariate, polynomial spline is defined as a piecewise polynomial function. The knots are not equidistantly distributed in the interval [a, b], the spline is therefore called to be non-uniform. If the above described interpolation method is applied to a large number of waypoints, comparatively high-order splines have to be selected for a feasible interpolation. Consequently, oscillations between the support points may occur.
Therefore, a cubic spline is applied piecewise for each segment. This way, a transition condition at each segment boundary ensures smoothness up to the second derivative. Its derivatives w. Each segment contains three spline functions including four spline parameters. To determine four parameters four equations are required.
Note that path segment boundaries at a and b may have an enforced slope as well. Otherwise, the second derivatives at the boundaries a and b are set to zero.
The approach presented enables a smooth transition between the segments up to the second derivative. The parameter sets defined for each spline yield the coordinates between the knots.
In addition to the desired overall path shape, vehicle configurations on that path must be determined. The following section focuses on this task. Trajectory DefinitionThe three-dimensional path defined in the section above has to account for obstacles and to ensure the attainment of a set of global mission goals.
Given a sufficiently low velocity, helicopters are able to fly along paths with arbitrarily sharp turns. Thus, solely the velocity is selected as the timely annotation of a path. The curvature of the path is used to determine the velocity. However, curvature changes may yield larger velocity changes than the vehicle can perform.
Hence, a search for velocity minima along the path must be performed before the vehicle starts to fly the trajectory. From points, where the deceleration to the velocity minimum has to begin, the velocity will be reduced while the vehicle still follows the given path. In this section first the determination of the velocity based on the curvature will follow.
To adopt the velocity profile for the limited acceleration, a search algorithm for velocity minima will be presented afterwards.
The sum of the forces acting on the center of gravity is used to determine the ability to compensate for gravity and centripetal force, and to change the velocity if desired. As explained in more details in the following subsection, the dotted blue line defines the velocity command for a deceleration with a specified deceleration limitation.
Due to the neglected aerodynamic forces and the issue that the vehicle is not able to change the flight path arbitrarily fast a scaling factor will be introduced. Beside the flight velocity the path deviation must be provided to follow the path e. A trajectory following control system to guide the vehicle based on the desired path and the path deviation will be presented subsequently to the following subsection.
Velocity profile for a spline curve. Based on the curvature the maximum velocity is determined solid. Due to the deceleration limitation the dotted velocity profile is commanded to slow down to the velocity minima or stop at the end. Velocity Minima Search AlgorithmIn general, vehicles have acceleration and deceleration limitations. A sole path geometry cannot account for such limitations.
In many cases these functions can be performed by trained humans. However, because of the speed of, for example, a rocket's dynamics, human reaction time is too slow to control this movement. Therefore, systems—now almost exclusively digital electronic—are used for such control. Even in cases where humans can perform these functions, it is often the case that GNC systems provide benefits such as alleviating operator work load, smoothing turbulence, fuel savings, etc. In addition, sophisticated applications of GNC enable automatic or remote control.
Among these systems, the GNC system communicates with all of the other components and controls the behavior of the components in order to complete a given mission. Therefore, a GNC system requires a high processing capability and multitasking capability and must support various communication methods. Sign In or Register. Advanced Search. Sign In. Skip Nav Destination Proceeding Navigation. Close mobile search navigation.
Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. By exploiting the fact that the vehicle hull has a modular structure, a specific payload module was realized containing a single board computer, two acoustic modems, the electronic boards for signal conditioning and data storage of seismic data, and the mechanical interface for the streamer, the array of hydrophones that constitutes the main mission payload. Then, by using the Robot Operating System ROS the mission control system was implemented in the single board computer inside the additional payload segment. Motion control for the AUV was realized designing controllers for surge speed, heading and depth. Design of the depth controller represented one of the major challenges mainly because towing the streamer heavily affects the vehicle dynamics when underwater.
Skip to main content. Table of Contents. No Access Examination of the optimal nonlinear regulator problem S. No Access Nonlinear control of a twin-lift helicopter configuration P.
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Skip to main content. Guidance and Control Conference 11 August - 13 August Table of Contents. No Access Control of self-adjoint distributed-parameter systems L. No Access The synthesis of control logic for parameter-insensitivity and disturbance attenuation A. No Access Optimal decentralized regulators for interconnected systems M. No Access Local distributed estimation D.
Show all documents Bipartite guidance, navigation and control architecture for autonomous aerial inspections under safety constraints 9, 1, 2, 4, 8, 12]. Despite already used for inspection tasks, mainly as remotely piloted vehicles, guidance , navigation and control GNC of drones is the object of continuous industrial research. Tech- niques depend on the performance required: long range UAVs, usually with fixed wing, perform their missions kilometres away from the base: position accuracy and obstacle avoidance are secondary when compared to turn planning  or the tra- jectory generation for these vehicles .
The conference aims at promoting new advances in aerospace GNC theory and technologies for enhancing safety, survivability, efficiency, performance, autonomy and intelligence of aerospace systems. It represents a unique forum for communication and information exchange between specialists in the fields of GNC systems design and operation, including air traffic management. This book contains the forty best papers and gives an interesting snapshot of the latest advances over the following topics:.
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