Considerations for Automated Nap‐of‐the‐Earth Rotorcraft Flight

Cheng, Victor H. L.; Sridhar, B.

doi:10.4050/jahs.36.61

Cited by 18 publications

(7 citation statements)

References 8 publications

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“…O<u, <1; i=1,2,...,K, j=1,2,...,N (1) j=1,2,...,N (2) O<u1<N; i=1,2,...,K This fuzzy K-partition can be defined as a fuzzy segmentation. Furthermore, if we define a uniformity predicate P(u13) such that it assigns the value true or false to the sample point x based on its membership value, we will have paralleled the crisp segmentation.…”

Section: Iterative Methodsmentioning

confidence: 99%

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Clustering methods for removing outliers from vision-based range estimates

Hussien

Suorsa

1992

SPIE Proceedings

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The automation of rotorcraft low-altitude flight presents challenging problems in flight control and sensor systems. The currently explored approach uses one or more passive sensors, such as a television camera, to extract environmental obstacle information. Obstacle imagery can be processed using a variety of computer vision techniques to produce a time-varying map of range to obstacles in the sensor's field of view along the helicopter flight path. To maneuver in tight space, obstacle-avoidance methods would need very reliable range map information by which to guide the helicopter through the environment. In general, most low level computer vision techniques generate sparse range maps which include at least a small percentage of bad estimates (outliers). This paper examines two related techniques which can be used to eliminate outliers from a sparse range map. Each method clusters sparse range map information into different spatial classes relying on a segmented and labeled image to help in spatial classification within the image plane.

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Section: Iterative Methodsmentioning

confidence: 99%

“…Automated rotorcraft low-altitude flight using passive range estimation algorithms is described in the literature [1,2,3]. Some range estimation algorithms produce range information corresponding to small discrete sparse areas (features) in the image.…”

Section: Introductionmentioning

confidence: 99%

Clustering methods for removing outliers from vision-based range estimates

Hussien

Suorsa

1992

SPIE Proceedings

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“…The reference state and control vectors, x 0 and u 0 , are provided by the inverse solution of the equations of motion. A design condition of rectilinear level flight at 30ms- 1 , where the unaugmented system is slightly unstable, is chosen for the present study, which corresponds to the velocity value indicated in Ref. 1 for a typical NOE flight.…”

Section: ^=F((y-yo) R Q(y-yo)+(«-«o) R R(«-u"))^ •••05)mentioning

confidence: 99%

Trajectory tracking for a helicopter model

Avanzini

2001

Aeronaut. j.

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In this paper an algorithm for the trajectory tracking of a three-dimensional trajectory, assigned as a function of time, is presented. The proposed control system is suitable for application on unmanned aerial vehicles (UAVs) or for aircraft that require accurate path tracking, as in the case of rotorcraft in nap-of-the-earth (NOE) flight conditions. The control system logic features (i) an external loop based on a simple guidance scheme and a two-time-scale inverse simulation algorithm, and (ii) an inner loop, based on a linear-quadratic (LQ) full-state-feedback controller. In this way the control action is split into two contributions, i.e. a feedforward command, in order to follow the trajectory generated by the guidance scheme, and a feedback increment, for compensating external disturbances and model uncertainties. A rotorcraft model is used to demonstrate the algorithm capability in a NOE–like flight task. System robustness is analysed and control system performance are discussed in terms of the error between vehicle state and desired trajectory at a given time. Simulation of a representative manoeuvre shows that the feedforward estimate of the control action is accurate and only minor compensation is required from the LQ tracker. The algorithm is suitable for a number of applications, as (i) no simplifying assumptions are postulated for the model, (ii) there are no restrictions on the flight condition, and (iii) the computational time should allow for real–time implementation.

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“…4 The prediction can be integrated in a ground proximity warning system, to reveal a possible ground collision with a time margin sufficient for undertaking an appropriate control action for avoiding obstacles on the prescribed flight path. In this case, the knowledge of an envelope of feasible future positions is also useful, if an efficient guidance law for obstacle avoidance is sought.…”

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confidence: 99%

Frenet-Based Algorithm for Trajectory Prediction

Avanzini

2004

Journal of Guidance, Control, and Dynamics

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An algorithm for aircraft trajectory prediction is presented that is based on two algebraic representations of the aircraft c.g. trajectory in the Frenet frame: (1) a cylindrical helix, during steady flight segments, and (2) a third-order accurate expansion, during transient maneuvering phases. This technique allows for an efficient evaluation of future c.g. positions in a time interval the extension of which depends on the aircraft maneuver state, without requiring the implementation of the aircraft dynamic model. A Kalman filtering technique with a fixed-lag smoother is used for simultaneously filtering the sensor noise and estimating accelerometer and rate-gyro signal derivatives. The effect of a step variation of commanded load factor and/or roll angle on the aircraft position at following times is displayed to the pilot as a visual aid for representing achievable future positions during the maneuver. The algorithm is demonstrated by computer simulation of reverse turn maneuvers of an F-16 fighter aircraft model. NomenclatureA = agility vector (time derivative of the acceleration); state matrix of the continuous-time Markov process a = acceleration vector a acc = accelerometer output, (a acc,x , a acc,y , a acc,z ) T a i, j = element of A a n = normal component of the acceleration B = input matrix of the continuous-time Markov process b = binormal unit vector C = output matrix D = Kalman filter dynamic matrix g = gravity acceleration vector h = altitude I = identity matrix K = steady-state Kalman filter gain matrix K ARI = aileron-rudder interconnect gain K W = weight coefficient k = discrete time k 1 , k 2 = intrinsic variables (curvature and torsion) L = Kalman smoother lag L B I , L B F , L F I = rotation matrices m = number of estimated time derivatives of the signal x(t) n = normal unit vector n z = normal load factor Q = covariance matrix of the discrete-time system q i, j = element of Q R = position vector of c.g. in the inertial frame R A = position of the accelerometers in body frame, (x A , y A , z A ) T r = autocorrelation function of the innovation process s = curvilinear abscissa T = sampling time t = time t = tangent unit vector V = velocity modulus V = velocity vector v = observation (Gaussian white) noise W = process (Gaussian white) noise of the discrete-time system w = process (Gaussian white) noise X = discrete-time state vector, x(kT ) x = estimated variable x = continuous-time state vector, (x, dx/dt, d 2 x/dt 2 , . . . , d m x/dt m ) T (x F , y F , z F ) T = position vector in the Frenet frame y = noise corrupted measure of x α = angle of attack δ A , δ R = aileron and rudder deflection λ max = dominant eigenvalue of D ρ = radius of the helix σ 2 v = measurement noise variance of the continuous-time Markov process σ 2 w = process noise variance of the continuous-time Markov process Φ = state matrix of the discrete-time system φ = roll angle ω = angular velocity, ( p, q, r ) T Subscripts A = in the accelerometer position acc = accelerometers B = body frame est = estimated F = Frenet frame...

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Considerations for Automated Nap‐of‐the‐Earth Rotorcraft Flight

Cited by 18 publications

References 8 publications

Clustering methods for removing outliers from vision-based range estimates

Clustering methods for removing outliers from vision-based range estimates

Trajectory tracking for a helicopter model

Frenet-Based Algorithm for Trajectory Prediction

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