Cooperative Object Tracking for Many-on-Many Engagement
|
|
- Alan Lewis
- 7 years ago
- Views:
Transcription
1 Cooperative Object Tracking for Many-on-Many Engagement Pradeep Bhatta a and Michael A. Paluszek a a Princeton Satellite Systems, 33 Witherspoon Street, Princeton, NJ, USA ABSTRACT This paper presents simulation results of nonlinear filtering algorithms applied to the cooperative object tracking problem. Cooperative tracking refers to observing a object from multiple mobile sensor platforms that communicate with each other, either directly or through a central node. Inter-agent communication also enables cooperative guidance, which can be used to achieve agent formation configurations advantageous to object tracking. Keywords: Cooperative object tracking, IR seeker, nonlinear filters, formation control 1. INTRODUCTION In this paper we consider the application of tracking a dynamic object using cooperating mobile sensor platforms. Object tracking is a challenging problem in many applications due to sensor range and accuracy limitations. An approach to resolve this problem involves fusing information from multiple sensors observing the object. Many aerospace applications involve mobile sensors, which may provide additional degrees of freedom to suitably position the sensors for optimal information collection. The work discussed in this paper is motivated by the problem of determining how sensor characteristics influence desirable sensor formation configurations, and implementation of such formations. Our analysis considers object dynamics to be unknown in general. The position of the sensor platforms is typically known to a very good accuracy, and for the purpose of this study is considered to be given without any uncertainty. However, sensor measurement uncertainty is modeled. Object position estimates are computed using an unscented Kalman filter, a nonlinear estimation algorithm. Tracking performance for various relative configurations of sensor platforms with respect to the object is compared. Further, formation regulation methods for guiding a generic sensor platform group to desired configurations are outlined. We present the system dynamics under consideration in Section 2. In Section 3 we present measurement equations used in the simulations. We briefly summarize nonlinear estimation using unscented Kalman filters in Section 4. We compare simulation results for various sensor group-object relative configurations in Section 5. In Section 6 we outline formation regulation methods for implementing cooperative estimation. Finally, we present concluding remarks in Section SYSTEM DYNAMICS Figure 1 shows an earth-centered inertial reference frame represented by axes (X, Y, Z). Vector R represents the position of an agent (either a object or a mobile sensor platform). Vectors Ṙ and R are the corresponding velocity and acceleration: R = r ê r (1) Ṙ = v r ê r + v θ ê θ + v φ ê φ Further author information: (Send correspondence to P. Bhatta) P.Bhatta: pradeep@psatellite.com, Telephone: = ṙ ê r + r θ cos φ ê θ + r φ ê φ (2) R = a r ê r + a θ ê θ + a φ ê φ, (3) Sensors and Systems for Space Applications III, edited by Joseph L. Cox, Pejmun Motaghedi Proc. of SPIE Vol. 7330, 73300J 2009 SPIE CCC code: X/09/$18 doi: / Proc. of SPIE Vol J-1
2 Z R ê êr ê Y X Figure 1. Earth-centered inertial reference frame where a r = r r φ 2 r θ 2 cos 2 φ (4) a θ = r θ cos φ +2ṙ θ cos φ 2r φ θ sin φ (5) a φ = r φ +2ṙ φ + r θ 2 cos φ sin φ (6) Equations (4)-(6) represent the dynamic equations, which can be expressed in the following state-space form: ẋ = f(x)+gu(x)+d, (7) ( ) where x = r, θ, φ, ṙ, θ, φ is the state vector, f and G are given by: f = 1 r cos φ 1 r ṙ θ φ r θ 2 cos 2 φ + r φ 2 ( 2ṙ θ cos φ +2r φ θ ) sin φ ( 2ṙ φ r θ ) 2 cos φ sin φ, G = [ ] (8) I 3 3 u =(u 1,u 2,u 3 ) is the acceleration vector: u = a r a θ = a G + a D + a C, (9) a φ where, a G = μ r 2 a ê r is the gravity acceleration a D = β 0 ρṙ is a generic (atmospheric) drag term1 a C is either an unknown maneuver (for objects) or guidance (for sensors) acceleration, μ is the gravitational parameter, β 0 is the ballistic coefficient, ρ =exp((r r 0 )/H 0 )istheairdensity,r 0 is the radius of Earth, and H 0 is a coefficient. Vector d contains unknown disturbances. Proc. of SPIE Vol J-2
3 3. MEASUREMENT MODELS We consider mobile sensor platforms equipped with electro-optical sensors measuring line-of-sight angles to objects. Each sensor platform measures the relative azimuth and elevation to each object in its field of view. In terms of cartesian position coordinates (x, y, z) =(r cos φ cos θ, r cos φ sin θ, r sin φ) the measured azimuth and elevation angles are given by ( φ = arcsin z t z s (xt x s ) 2 +(y t y s ) 2 +(z t z s ) 2 ) + e φ, (10) ( ) yt y s θ = arctan + e θ (11) x t x s where the subscripts t and s refer to the object and sensor platforms respectively. Terms e φ and e θ are Gaussian white noise signals modeling measurement uncertainty. Imager optical models may be easily incorporated in the estimation process when nonlinear filtering methods (such as the unscented Kalman filter discussed in the next section) are used. The lowest order approximation to the imager optical model is a pinhole camera model. The imager model returns pixel coordinates (ɛ x,ɛ y )as a function of the effective focal length f, and the relative position coordinates: ( ) ( ) ( ) ɛx f xt x = s eɛx +, (12) ɛ y (xt x s ) 2 +(y t y s ) 2 +(z t z s ) 2 y t y s e ɛy where e ɛx and e ɛy are Gaussian white noise terms abstracting all noise sources of the imaging system. 4. UNSCENTED KALMAN FILTERS The unscented Kalman filter (UKF) 2 4 provides a versatile method for state and parameter estimation of nonlinear systems. The UKF removes some of the shortcomings of the Extended Kalman Filter (EKF), which has been the most commonly used estimation method for nonlinear systems, by applying the unscented transformation (UT). Unlike the EKF, the UKF does not require computation of derivatives of either state equations or measurement equations. Instead of just propagating the state, the filter propagates a set of sample points - called sigma points - that are determined from the a priori mean and covariance of the state. The sigma points undergo the unscented transformation. Then the posterior mean and covariance of the state are determined from the transformed sigma points. The implementation of UKF for a nonlinear system follows a systematic procedure, as described in. 4 The filter is initialized with the following state estimate and covariance: At any time-step k, the2l + 1 the a priori sigma points are ˆx 0 = E[x 0 ], P 0 = E[(x 0 ˆx 0 )(x 0 ˆx 0 ) T ]. (13) Ξ k 1 = [ ˆx k 1 ˆx k 1 + γ P k 1 ˆx k 1 γ P k 1 ]. (14) The next step in implementing UKF involves propagating all the sigma points (including the state vector): Ξ k k 1 = F [Ξ k 1]. (15) In the above equation F represents the discrete-time mapping corresponding to propagation of the state vector and sigma points through one time-step (from k 1tok). Ξ k k 1 contains the transformed sigma points. They are used for the computation of posterior mean and covariance as follows: ˆx k = P k = 2L i=0 2L i=0 W (m) i Ξ i,k k 1 (16) W (c) i [Ξ i,k k 1 ˆx k ][Ξ i,k k 1 ˆx k ]T + R v, (17) Proc. of SPIE Vol J-3
4 where, R v is the process noise covariance, W (m) i and W (c) i are weights given by W (m) 0 = W (c) 0 = W (m) i = W (c) i = λ L + λ (18) λ L + λ +1 α2 + β (19) 1 2(L + λ), (20) and, γ = L + λ and λ = α 2 (L + κ) L. (21) In the above equations α, β and κ are adjustable parameters of the filter. The parameter α determines the spread of sigma points around the state vector, and is usually set to 1e 4 α 1. The parameter κ also influences scaling, but is normally set to 0 for state estimation. The parameter β incorporates prior knowledge of the distribution of x. For gaussian distributions, β is set to 2. The sigma points corresponding to the posterior state and covariance estimates are computed next: [ ] Ξ k k 1 = ˆx k ˆx k + γ P k ˆx k P γ. (22) k The estimated measurement matrix is computed by transforming the sigma points using the nonlinear measurement model, Υ k k 1 = H[Ξ k k 1 ]. (23) The mean measurement, y k, the measurement covariance, P y k y k, and the cross-correlation covariance, P xk y k, are calculated based on the statistics of the transformed sigma points: ŷ k = P yk y k = P xk y k = 2L i=0 2L i=0 2L i=0 where R n is the measurement covariance matrix. The Kalman gain matrix is W (m) i Υ i,k k 1 (24) W (c) i [Υ i,k k 1 ŷ k ][Υ i,k k 1 ŷ k ]T + R n (25) W (c) i [Ξ i,k k 1 ˆx k ][Υ i,k k 1 ŷ k ]T, (26) K xk = P xk y k P 1 y k y k. (27) Finally, the measurement update equations are used to determine the mean, ˆx k, and the covariance, P xk,of the filtered state: ˆx k = ˆx k + K x k (y k ŷ k ) (28) P xk = Px k K xk P yk y k Kx T k. (29) 5. SIMULATION RESULTS We present simulation results for four different configurations consisting up to three mobile sensor platforms: 1. Configuration 1 consists of just one sensor platform. 2. Configuration 2 consists of three sensor platforms aligned along a straight line. Proc. of SPIE Vol J-4
5 SENSOR2 SENSOR3 SENSOR1 SENSOR OBJECT go OBJECT Configuration 1 Configuration 2 SENSOR1 -so -Iso -1 SENSOR3 OBJECT SENSOR1 SENSOR3 OBJECT SENSOR SENSOR Configuration 3 Configuration 4 Figure 2. Relative sensors-object configurations 3. Configuration 3 consists of three sensor platforms at the vertices of an equilateral triangle with a side length of 283 km. 4. Configuration 4 consists of three sensor platforms at the vertices of an equilateral triangle with a side length of 495 km. Figure 2 shows the four sensor configurations. Sensor configurations 2 through 4 may be stabilized using cooperative guidance algorithms such as those reviewed in Section 6. In all simulations we consider the measurement model of equations (10)-(11), with measurement noise components having a standard deviation of 0.1 radians. The initial position estimation error vector is (20,-20,-) km. We consider two cases of object motion: (a) non-maneuvering, and (b) maneuvering with random accelerations. 5.1 Non-maneuvering Object Figure 3 shows the evolution of position tracking error for the four configurations under consideration. As expected, the tracking performance improves when the number of sensors is increased from 1 to 3 in Configuration 2. We note that performance improves markedly when the three sensors are splayed about the object as in Configuration 3. This is a consequence of using observations from different vantage points or relative orientations with respect to the object. Furthermore the sensor formation size plays a significant role also. There is an optimal formation size for given measurement statistics. Configuration 4 is close to the optimal size. Tracking Proc. of SPIE Vol J-5
6 Configuration 1: 62 km Configuration 2: 47 km Configuration 3: 34 km Configuration 4: 28 km Figure 3. Position estimation error evolution for a non-maneuvering object. Final position error for each sensor configuration is shown below the plots. performance deteriorates for larger formation sizes. Figure 4 shows the individual estimation error components for Configuration Maneuvering Object Figure 3 shows the evolution of position tracking error for the four configurations under consideration when the object is maneuvering. Random object accelerations of the order of 2 percent of the total acceleration were included in these simulations. Tracking performance follows the same trend as in the case of non-maneuvering object, but performance improvements in Configurations 2 through 4 are more marked in this case. 6. FORMATION REGULATION In this section we discuss two methods that can be used for regulating formations of mobile sensor networks for cooperative estimation. We note that several formation control (or collective motion) paradigms have been introduced in the literature over the last decade. While many of the paradigms can be adapted to implement formation regulation, the two methods that we review are particularly suited for application to object tracking problems. 6.1 Virtual Bodies and Artificial Potentials (VBAP) Framework 5 7 The VBAP framework was developed in Princeton University during the Autonomous Ocean Sampling Network project (AOSN) 8 for coordinating groups of autonomous underwater vehicles. This framework provides a means Proc. of SPIE Vol J-6
7 25 Tracking Error x (km) y (km) z(km) Time (sec) Figure 4. Position estimation error components corresponding to Configuration Configuration 1: 265 km 0 Configuration 2: 140 km Configuration 3: 44 km Configuration 4: 26 km Figure 5. Position estimation error evolution for a maneuvering object. Final position error for each sensor configuration is shown below the plots. Proc. of SPIE Vol J-7
8 for encoding coordination rules of motion for each vehicle, so that the group can maintain a desired formation, and at the same time can collectively respond to measurements in order to locate interesting features in the ocean. The strength of the framework is the systematic way in which it can be implemented. Furthermore, the approach decouples formation regulation and mission guidance, and can be implemented in a decentralized manner Formation Stabilization Consider a group of N mobile sensors. The position of each sensor with respect to an inertial frame may be given by a vector x i R 3. The control force on each sensor is given by u i R 3. Then, the dynamics of the sensor are ẍ i = u i (30) AwebofM reference points, called virtual leaders may be introduced. 5 Let the position of the lth virtual leader with respect to the inertial frame be b l R 3. The virtual leader motion may be specified to provide guidance to the cooperating group of sensors. Let x ij = x i x j R 3 represent the distance between the ith and the jth sensor, and let h il = x i b l R 3 represent the distance between the ith sensor and the lth virtual leader. Between every pair of sensors i and j, an artificial potential V I (x ij ) can be defined. Similarly an artificial potential V h (h il ) can be defined between the ith sensor and the lth virtual leader. The cooperative guidance law for the ith sensor is essentially the negative of the gradient of the sum of these potentials plus a linear damping term: N M u i = xi V I (x ij ) xi V h (h il ) Kẋ i = j i N j i f I (x ij ) x ij x ij l=1 M l=1 f h (h il ) h il h il Kẋ i, (31) where f I and f h are magnitudes of forces derived from the artificial potentials V I and V h respectively. The artificial potentials are chosen such that the sensors maintain a nominal separation between each other, and between themselves and the virtual bodies Formation Reconfiguration and Guidance In 7 the motion of the formation is introduced by prescribing the motion of the virtual body. The motion includes translation, rotation, expansion and contraction of the formation, as well as sensor-driven tasks and mission trajectories. Formation reconfiguration and guidance is decoupled from the problem of formation stabilization by way of parameterizing the virtual body motion by a scalar variable s. An augmented state space for the system is given by (x, s, r, R, k) where(r, r) SO(3) R 3 represents the position and orientation of the virtual body, and k R is a scale factor that can be regulated for expansion/contraction of the formation. The total vector fields of the virtual body motion are expressed as: dr dt dr dt dk dt = dr ds ṡ (32) = dr ds ṡ (33) = dk ds ṡ (34) In 7 the magnitude of the virtual body vector fields, ṡ, which controls the speed of the virtual body is chosen to guarantee formation stabilization and convergence properties. The direction vectors can be chosen independently depending on the formation mission. Proc. of SPIE Vol J-8
9 6.2 Klein-Morgansen (KM) Approach9, 10 Researchers at the University of Washington have applied oscillator models to synthesize cooperative guidance algorithms for object tracking. Consider a group of N unit speed agents. Their motion may be described using the natural Frenet-Serret framework. In a cartesian reference frame with inertial coordinates, the position of each agent is represented by a vector r. In order to describe direction of the velocity of each agent an orthonormal frame formed of a unit tangent vector, t, a unit normal vector, n, and a unit binormal vector, b is used. Vector t represents the instantaneous velocity of the agent. The agent can be steered gyroscopically using two inputs u and v towards the normal and binormal vectors, respectively. The dynamical system representing the motion of the agent can be described using the following model: d dt r t n b = u v 0 u v 0 0 r t n b, (35) There have been several (earlier and later) approaches using the same model for developing collective motion control laws, such as. 11 Reference 10 directly addresses the problem of object tracking by making the following assumptions 1. The path of the object is considered to be at least twice continuously differentiable. 2. Each sensor agent has full information about the object, including its position, velocity and acceleration. 3. Each sensor has undelayed access to all states of every other pursuer. 4. Without loss of generality, the speed of each pursuer vehicle is set to one, and the speed of the object vehicle is restricted to ṙ t [0, 1) Cooperative Guidance Design Strategy The cooperative guidance design strategy is broken into three steps: S1: Velocity mathcing: Steer each pursuer vehicle such that the velocity of the group centroid matches a known dynamic reference velocity, ṙ ref. The acceleration r ref is assumed to be known. S2: Centroid guidance: Define an outer loop controller to generate an appropriate velocity to stabilize the position and velocity of the group centroid to the position and velocity of the object vehicle. S3: Spacing control: Apply a spacing controller to keep each vehicle near the collective motion centroid without interfering with velocity matching controls. Figure 6 shows a simulation result in which the centroid of three mobile sensor platforms follows a commanded straight line, while each individual sensor moves in helical trajectories about the commanded trajectory of the centroid. 7. CONCLUDING REMARKS In this paper we have presented simulation demonstrations illustrating tracking error performance improvements through cooperative estimation. Our results indicate that certain formation configurations are better than others for achieving good tracking performance. We have indicated methods for regulating mobile sensor platforms to such desirable formation configurations. Further work in this area will consider sensor reliability models and effects of limited sensor-to-sensor communications, including communication latencies. Proc. of SPIE Vol J-9
10 z x y Figure 6. Simulation of the KM cooperative guidance law REFERENCES [1] Zang, W., Shi, Z. G., Du, S. C., and Chen, K. S., Novel roughening method for reentry vehicle tracking using particle filter, Journal of Electromagnetic Waves and Applications 21(14), (2007). [2] Julier, S. J. and Uhlmann, J. K., A new extension of the Kalman filter to nonlinear systems, in [Proc. 11th International Symposium on Aerospace/Defence Sensing, Simulation and Controls], (1997). [3] Wan, E. A. and van der Merwe, R., The unscented kalman filter for nonlinear estimation, in [Proc. IEEE Symposium 2000], (2000). [4] van der Merwe, R. and Wan, E. A., The Square-Root Unscented Kalman Filter for State and Parameter- Estimation, in [Proc. IEEE International Conference on Acoustics, Speech and Signal Processing], (2001). [5] Fiorelli, E. and Leonard, N. E., Formations with a mission: Stable coordination of vehicle group maneuvers, in [Proc. IEEE Conference on Decision and Control], (2001). [6] Ögren, P., Fiorelli, E., and Leonard, N. E., Formations with a mission: Stable coordination of vehicle group maneuvers, in [Proc. Symposium of Mathematical Theory of Networks and Systems], (2002). [7] Ögren, P., Fiorelli, E., and Leonard, N. E., Coperative control of mobile sensor networks: Adaptive gradient climbing in a distributed environment, IEEE Transactions on Automatic Control 49(8), (2004). [8] Autonomous Ocean Sampling Network II (AOSN-II), collaborative project. [9] Klein, D. J. and Morgansen, K. A., Controlled collective motion for trajectory tracking, in [Proc. American Control Conference], (2006). [10] Klein, D. J., Matlack, C., and Morgansen, K. A., Cooperative target tracking using oscillator models in three dimensions, in [Proc. American Control Conference], (2007). [11] Sepulchre, R., Paley, D. A., and Leonard, N. E., Stabilization of planar collective motion with limited communication, IEEE Transactions on Automatic Control 53(3), (2008). Proc. of SPIE Vol J-10
Mathieu St-Pierre. Denis Gingras Dr. Ing.
Comparison between the unscented Kalman filter and the extended Kalman filter for the position estimation module of an integrated navigation information system Mathieu St-Pierre Electrical engineering
More informationPhysics 235 Chapter 1. Chapter 1 Matrices, Vectors, and Vector Calculus
Chapter 1 Matrices, Vectors, and Vector Calculus In this chapter, we will focus on the mathematical tools required for the course. The main concepts that will be covered are: Coordinate transformations
More informationLecture L6 - Intrinsic Coordinates
S. Widnall, J. Peraire 16.07 Dynamics Fall 2009 Version 2.0 Lecture L6 - Intrinsic Coordinates In lecture L4, we introduced the position, velocity and acceleration vectors and referred them to a fixed
More informationDeterministic Sampling-based Switching Kalman Filtering for Vehicle Tracking
Proceedings of the IEEE ITSC 2006 2006 IEEE Intelligent Transportation Systems Conference Toronto, Canada, September 17-20, 2006 WA4.1 Deterministic Sampling-based Switching Kalman Filtering for Vehicle
More informationUnderstanding and Applying Kalman Filtering
Understanding and Applying Kalman Filtering Lindsay Kleeman Department of Electrical and Computer Systems Engineering Monash University, Clayton 1 Introduction Objectives: 1. Provide a basic understanding
More informationA Multi-Model Filter for Mobile Terminal Location Tracking
A Multi-Model Filter for Mobile Terminal Location Tracking M. McGuire, K.N. Plataniotis The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 1 King s College
More informationLecture L5 - Other Coordinate Systems
S. Widnall, J. Peraire 16.07 Dynamics Fall 008 Version.0 Lecture L5 - Other Coordinate Systems In this lecture, we will look at some other common systems of coordinates. We will present polar coordinates
More informationChapter 2. Parameterized Curves in R 3
Chapter 2. Parameterized Curves in R 3 Def. A smooth curve in R 3 is a smooth map σ : (a, b) R 3. For each t (a, b), σ(t) R 3. As t increases from a to b, σ(t) traces out a curve in R 3. In terms of components,
More informationLecture L2 - Degrees of Freedom and Constraints, Rectilinear Motion
S. Widnall 6.07 Dynamics Fall 009 Version.0 Lecture L - Degrees of Freedom and Constraints, Rectilinear Motion Degrees of Freedom Degrees of freedom refers to the number of independent spatial coordinates
More informationVEHICLE TRACKING USING ACOUSTIC AND VIDEO SENSORS
VEHICLE TRACKING USING ACOUSTIC AND VIDEO SENSORS Aswin C Sankaranayanan, Qinfen Zheng, Rama Chellappa University of Maryland College Park, MD - 277 {aswch, qinfen, rama}@cfar.umd.edu Volkan Cevher, James
More informationEE 570: Location and Navigation
EE 570: Location and Navigation On-Line Bayesian Tracking Aly El-Osery 1 Stephen Bruder 2 1 Electrical Engineering Department, New Mexico Tech Socorro, New Mexico, USA 2 Electrical and Computer Engineering
More informationLecture L22-2D Rigid Body Dynamics: Work and Energy
J. Peraire, S. Widnall 6.07 Dynamics Fall 008 Version.0 Lecture L - D Rigid Body Dynamics: Work and Energy In this lecture, we will revisit the principle of work and energy introduced in lecture L-3 for
More informationLecture L17 - Orbit Transfers and Interplanetary Trajectories
S. Widnall, J. Peraire 16.07 Dynamics Fall 008 Version.0 Lecture L17 - Orbit Transfers and Interplanetary Trajectories In this lecture, we will consider how to transfer from one orbit, to another or to
More informationAPPLIED MATHEMATICS ADVANCED LEVEL
APPLIED MATHEMATICS ADVANCED LEVEL INTRODUCTION This syllabus serves to examine candidates knowledge and skills in introductory mathematical and statistical methods, and their applications. For applications
More informationLeast-Squares Intersection of Lines
Least-Squares Intersection of Lines Johannes Traa - UIUC 2013 This write-up derives the least-squares solution for the intersection of lines. In the general case, a set of lines will not intersect at a
More informationLecture L3 - Vectors, Matrices and Coordinate Transformations
S. Widnall 16.07 Dynamics Fall 2009 Lecture notes based on J. Peraire Version 2.0 Lecture L3 - Vectors, Matrices and Coordinate Transformations By using vectors and defining appropriate operations between
More informationIn order to describe motion you need to describe the following properties.
Chapter 2 One Dimensional Kinematics How would you describe the following motion? Ex: random 1-D path speeding up and slowing down In order to describe motion you need to describe the following properties.
More informationGeometric Camera Parameters
Geometric Camera Parameters What assumptions have we made so far? -All equations we have derived for far are written in the camera reference frames. -These equations are valid only when: () all distances
More informationNonlinear Systems of Ordinary Differential Equations
Differential Equations Massoud Malek Nonlinear Systems of Ordinary Differential Equations Dynamical System. A dynamical system has a state determined by a collection of real numbers, or more generally
More informationAn Introduction to the Kalman Filter
An Introduction to the Kalman Filter Greg Welch 1 and Gary Bishop 2 TR 95041 Department of Computer Science University of North Carolina at Chapel Hill Chapel Hill, NC 275993175 Updated: Monday, July 24,
More informationMobile Robot FastSLAM with Xbox Kinect
Mobile Robot FastSLAM with Xbox Kinect Design Team Taylor Apgar, Sean Suri, Xiangdong Xi Design Advisor Prof. Greg Kowalski Abstract Mapping is an interesting and difficult problem in robotics. In order
More informationRotation: Moment of Inertia and Torque
Rotation: Moment of Inertia and Torque Every time we push a door open or tighten a bolt using a wrench, we apply a force that results in a rotational motion about a fixed axis. Through experience we learn
More informationCOORDINATED groups of autonomous underwater vehicles
IEEE JOURNAL OF OCEANIC ENGINEERING, VOL. 31, NO. 4, OCTOBER 2006 935 Multi-AUV Control and Adaptive Sampling in Monterey Bay Edward Fiorelli, Member, IEEE, Naomi Ehrich Leonard, Senior Member, IEEE, Pradeep
More informationIsaac Newton s (1642-1727) Laws of Motion
Big Picture 1 2.003J/1.053J Dynamics and Control I, Spring 2007 Professor Thomas Peacock 2/7/2007 Lecture 1 Newton s Laws, Cartesian and Polar Coordinates, Dynamics of a Single Particle Big Picture First
More informationDifferentiation of vectors
Chapter 4 Differentiation of vectors 4.1 Vector-valued functions In the previous chapters we have considered real functions of several (usually two) variables f : D R, where D is a subset of R n, where
More informationOscillator Models and Collective Motion: Splay State Stabilization of Self-Propelled Particles
To appear in Proc. 44th IEEE Conf. Decision and Control, 005 Oscillator Models and Collective Motion: Splay State Stabilization of Self-Propelled Particles Derek Paley and aomi Ehrich Leonard Mechanical
More informationBasic Principles of Inertial Navigation. Seminar on inertial navigation systems Tampere University of Technology
Basic Principles of Inertial Navigation Seminar on inertial navigation systems Tampere University of Technology 1 The five basic forms of navigation Pilotage, which essentially relies on recognizing landmarks
More informationStatic Environment Recognition Using Omni-camera from a Moving Vehicle
Static Environment Recognition Using Omni-camera from a Moving Vehicle Teruko Yata, Chuck Thorpe Frank Dellaert The Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213 USA College of Computing
More informationUNIVERSITETET I OSLO
UNIVERSITETET I OSLO Det matematisk-naturvitenskapelige fakultet Exam in: FYS 310 Classical Mechanics and Electrodynamics Day of exam: Tuesday June 4, 013 Exam hours: 4 hours, beginning at 14:30 This examination
More informationHSC Mathematics - Extension 1. Workshop E4
HSC Mathematics - Extension 1 Workshop E4 Presented by Richard D. Kenderdine BSc, GradDipAppSc(IndMaths), SurvCert, MAppStat, GStat School of Mathematics and Applied Statistics University of Wollongong
More informationTorque Analyses of a Sliding Ladder
Torque Analyses of a Sliding Ladder 1 Problem Kirk T. McDonald Joseph Henry Laboratories, Princeton University, Princeton, NJ 08544 (May 6, 2007) The problem of a ladder that slides without friction while
More informationCooperative Vehicle Control, Feature Tracking and Ocean Sampling
Cooperative Vehicle Control, Feature Tracking and Ocean Sampling Edward A. Fiorelli A Dissertation Presented to the Faculty of Princeton University in Candidacy for the Degree of Doctor of Philosophy Recommended
More informationMapping an Application to a Control Architecture: Specification of the Problem
Mapping an Application to a Control Architecture: Specification of the Problem Mieczyslaw M. Kokar 1, Kevin M. Passino 2, Kenneth Baclawski 1, and Jeffrey E. Smith 3 1 Northeastern University, Boston,
More informationA Multi-Sensor Object Localization System
A Multi-Sensor Object Localization System S. Spors, R. Rabenstein and N. Strobel Telecommunications Laboratory University of Erlangen-Nuremberg Cauerstrasse 7, 91058 Erlangen, Germany E-mail: {spors, rabe,
More informationEnhancing the SNR of the Fiber Optic Rotation Sensor using the LMS Algorithm
1 Enhancing the SNR of the Fiber Optic Rotation Sensor using the LMS Algorithm Hani Mehrpouyan, Student Member, IEEE, Department of Electrical and Computer Engineering Queen s University, Kingston, Ontario,
More informationJPEG compression of monochrome 2D-barcode images using DCT coefficient distributions
Edith Cowan University Research Online ECU Publications Pre. JPEG compression of monochrome D-barcode images using DCT coefficient distributions Keng Teong Tan Hong Kong Baptist University Douglas Chai
More informationOrbits of the Lennard-Jones Potential
Orbits of the Lennard-Jones Potential Prashanth S. Venkataram July 28, 2012 1 Introduction The Lennard-Jones potential describes weak interactions between neutral atoms and molecules. Unlike the potentials
More informationUnderstanding Poles and Zeros
MASSACHUSETTS INSTITUTE OF TECHNOLOGY DEPARTMENT OF MECHANICAL ENGINEERING 2.14 Analysis and Design of Feedback Control Systems Understanding Poles and Zeros 1 System Poles and Zeros The transfer function
More informationMulti-Robot Tracking of a Moving Object Using Directional Sensors
Multi-Robot Tracking of a Moving Object Using Directional Sensors Manuel Mazo Jr., Alberto Speranzon, Karl H. Johansson Dept. of Signals, Sensors & Systems Royal Institute of Technology SE- 44 Stockholm,
More informationA Reliability Point and Kalman Filter-based Vehicle Tracking Technique
A Reliability Point and Kalman Filter-based Vehicle Tracing Technique Soo Siang Teoh and Thomas Bräunl Abstract This paper introduces a technique for tracing the movement of vehicles in consecutive video
More informationLet s first see how precession works in quantitative detail. The system is illustrated below: ...
lecture 20 Topics: Precession of tops Nutation Vectors in the body frame The free symmetric top in the body frame Euler s equations The free symmetric top ala Euler s The tennis racket theorem As you know,
More informationAn Introduction to Applied Mathematics: An Iterative Process
An Introduction to Applied Mathematics: An Iterative Process Applied mathematics seeks to make predictions about some topic such as weather prediction, future value of an investment, the speed of a falling
More informationParametric Equations and the Parabola (Extension 1)
Parametric Equations and the Parabola (Extension 1) Parametric Equations Parametric equations are a set of equations in terms of a parameter that represent a relation. Each value of the parameter, when
More informationMOBILE ROBOT TRACKING OF PRE-PLANNED PATHS. Department of Computer Science, York University, Heslington, York, Y010 5DD, UK (email:nep@cs.york.ac.
MOBILE ROBOT TRACKING OF PRE-PLANNED PATHS N. E. Pears Department of Computer Science, York University, Heslington, York, Y010 5DD, UK (email:nep@cs.york.ac.uk) 1 Abstract A method of mobile robot steering
More informationHuman-like Arm Motion Generation for Humanoid Robots Using Motion Capture Database
Human-like Arm Motion Generation for Humanoid Robots Using Motion Capture Database Seungsu Kim, ChangHwan Kim and Jong Hyeon Park School of Mechanical Engineering Hanyang University, Seoul, 133-791, Korea.
More informationACCELERATION OF HEAVY TRUCKS Woodrow M. Poplin, P.E.
ACCELERATION OF HEAVY TRUCKS Woodrow M. Poplin, P.E. Woodrow M. Poplin, P.E. is a consulting engineer specializing in the evaluation of vehicle and transportation accidents. Over the past 23 years he has
More informationBiggar High School Mathematics Department. National 5 Learning Intentions & Success Criteria: Assessing My Progress
Biggar High School Mathematics Department National 5 Learning Intentions & Success Criteria: Assessing My Progress Expressions & Formulae Topic Learning Intention Success Criteria I understand this Approximation
More informationLecture L29-3D Rigid Body Dynamics
J. Peraire, S. Widnall 16.07 Dynamics Fall 2009 Version 2.0 Lecture L29-3D Rigid Body Dynamics 3D Rigid Body Dynamics: Euler Angles The difficulty of describing the positions of the body-fixed axis of
More information19 LINEAR QUADRATIC REGULATOR
19 LINEAR QUADRATIC REGULATOR 19.1 Introduction The simple form of loopshaping in scalar systems does not extend directly to multivariable (MIMO) plants, which are characterized by transfer matrices instead
More informationTorgerson s Classical MDS derivation: 1: Determining Coordinates from Euclidean Distances
Torgerson s Classical MDS derivation: 1: Determining Coordinates from Euclidean Distances It is possible to construct a matrix X of Cartesian coordinates of points in Euclidean space when we know the Euclidean
More informationLECTURE 6: Fluid Sheets
LECTURE 6: Fluid Sheets The dynamics of high-speed fluid sheets was first considered by Savart after his early work on electromagnetism with Biot, and was subsequently examined in a series of papers by
More informationRelating Vanishing Points to Catadioptric Camera Calibration
Relating Vanishing Points to Catadioptric Camera Calibration Wenting Duan* a, Hui Zhang b, Nigel M. Allinson a a Laboratory of Vision Engineering, University of Lincoln, Brayford Pool, Lincoln, U.K. LN6
More informationFigure 1.1 Vector A and Vector F
CHAPTER I VECTOR QUANTITIES Quantities are anything which can be measured, and stated with number. Quantities in physics are divided into two types; scalar and vector quantities. Scalar quantities have
More informationRobotics. Chapter 25. Chapter 25 1
Robotics Chapter 25 Chapter 25 1 Outline Robots, Effectors, and Sensors Localization and Mapping Motion Planning Motor Control Chapter 25 2 Mobile Robots Chapter 25 3 Manipulators P R R R R R Configuration
More informationInner Product Spaces
Math 571 Inner Product Spaces 1. Preliminaries An inner product space is a vector space V along with a function, called an inner product which associates each pair of vectors u, v with a scalar u, v, and
More informationContent. Professur für Steuerung, Regelung und Systemdynamik. Lecture: Vehicle Dynamics Tutor: T. Wey Date: 01.01.08, 20:11:52
1 Content Overview 1. Basics on Signal Analysis 2. System Theory 3. Vehicle Dynamics Modeling 4. Active Chassis Control Systems 5. Signals & Systems 6. Statistical System Analysis 7. Filtering 8. Modeling,
More informationv v ax v a x a v a v = = = Since F = ma, it follows that a = F/m. The mass of the arrow is unchanged, and ( )
Week 3 homework IMPORTANT NOTE ABOUT WEBASSIGN: In the WebAssign versions of these problems, various details have been changed, so that the answers will come out differently. The method to find the solution
More informationTexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AAAAA
2015 School of Information Technology and Electrical Engineering at the University of Queensland TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AAAAA Schedule Week Date
More informationChapter 2. Mission Analysis. 2.1 Mission Geometry
Chapter 2 Mission Analysis As noted in Chapter 1, orbital and attitude dynamics must be considered as coupled. That is to say, the orbital motion of a spacecraft affects the attitude motion, and the attitude
More informationUnified Lecture # 4 Vectors
Fall 2005 Unified Lecture # 4 Vectors These notes were written by J. Peraire as a review of vectors for Dynamics 16.07. They have been adapted for Unified Engineering by R. Radovitzky. References [1] Feynmann,
More informationMechanics 1: Conservation of Energy and Momentum
Mechanics : Conservation of Energy and Momentum If a certain quantity associated with a system does not change in time. We say that it is conserved, and the system possesses a conservation law. Conservation
More informationLecture 2: Homogeneous Coordinates, Lines and Conics
Lecture 2: Homogeneous Coordinates, Lines and Conics 1 Homogeneous Coordinates In Lecture 1 we derived the camera equations λx = P X, (1) where x = (x 1, x 2, 1), X = (X 1, X 2, X 3, 1) and P is a 3 4
More informationOrbital Mechanics. Angular Momentum
Orbital Mechanics The objects that orbit earth have only a few forces acting on them, the largest being the gravitational pull from the earth. The trajectories that satellites or rockets follow are largely
More informationKristine L. Bell and Harry L. Van Trees. Center of Excellence in C 3 I George Mason University Fairfax, VA 22030-4444, USA kbell@gmu.edu, hlv@gmu.
POSERIOR CRAMÉR-RAO BOUND FOR RACKING ARGE BEARING Kristine L. Bell and Harry L. Van rees Center of Excellence in C 3 I George Mason University Fairfax, VA 22030-4444, USA bell@gmu.edu, hlv@gmu.edu ABSRAC
More informationA PHD filter for tracking multiple extended targets using random matrices
A PHD filter for tracing multiple extended targets using random matrices Karl Granström and Umut Orguner Linöping University Post Print N.B.: When citing this wor, cite the original article. IEEE. Personal
More informationAPPENDIX D. VECTOR ANALYSIS 1. The following conventions are used in this appendix and throughout the book:
APPENDIX D. VECTOR ANALYSIS 1 Appendix D Vector Analysis The following conventions are used in this appendix and throughout the book: f, g, φ, ψ are scalar functions of x,t; A, B, C, D are vector functions
More informationPhysics Notes Class 11 CHAPTER 3 MOTION IN A STRAIGHT LINE
1 P a g e Motion Physics Notes Class 11 CHAPTER 3 MOTION IN A STRAIGHT LINE If an object changes its position with respect to its surroundings with time, then it is called in motion. Rest If an object
More informationForce/position control of a robotic system for transcranial magnetic stimulation
Force/position control of a robotic system for transcranial magnetic stimulation W.N. Wan Zakaria School of Mechanical and System Engineering Newcastle University Abstract To develop a force control scheme
More informationTime Domain and Frequency Domain Techniques For Multi Shaker Time Waveform Replication
Time Domain and Frequency Domain Techniques For Multi Shaker Time Waveform Replication Thomas Reilly Data Physics Corporation 1741 Technology Drive, Suite 260 San Jose, CA 95110 (408) 216-8440 This paper
More informationStabilizing a Gimbal Platform using Self-Tuning Fuzzy PID Controller
Stabilizing a Gimbal Platform using Self-Tuning Fuzzy PID Controller Nourallah Ghaeminezhad Collage Of Automation Engineering Nuaa Nanjing China Wang Daobo Collage Of Automation Engineering Nuaa Nanjing
More informationSpecial Theory of Relativity
June 1, 2010 1 1 J.D.Jackson, Classical Electrodynamics, 3rd Edition, Chapter 11 Introduction Einstein s theory of special relativity is based on the assumption (which might be a deep-rooted superstition
More informationAP Physics C. Oscillations/SHM Review Packet
AP Physics C Oscillations/SHM Review Packet 1. A 0.5 kg mass on a spring has a displacement as a function of time given by the equation x(t) = 0.8Cos(πt). Find the following: a. The time for one complete
More informationConstruction and Control of an Educational Lab Process The Gantry Crane
Construction and Control of an Educational Lab Process The Gantry Crane Per-Ola Larsson, Rolf Braun Department of Automatic Control Lund University Box 8, SE-2 Lund, Sweden E-mail: {perola.larsson, rolf.braun}@control.lth.se
More informationActive Vibration Isolation of an Unbalanced Machine Spindle
UCRL-CONF-206108 Active Vibration Isolation of an Unbalanced Machine Spindle D. J. Hopkins, P. Geraghty August 18, 2004 American Society of Precision Engineering Annual Conference Orlando, FL, United States
More informationPenn State University Physics 211 ORBITAL MECHANICS 1
ORBITAL MECHANICS 1 PURPOSE The purpose of this laboratory project is to calculate, verify and then simulate various satellite orbit scenarios for an artificial satellite orbiting the earth. First, there
More informationPhysics 41 HW Set 1 Chapter 15
Physics 4 HW Set Chapter 5 Serway 8 th OC:, 4, 7 CQ: 4, 8 P: 4, 5, 8, 8, 0, 9,, 4, 9, 4, 5, 5 Discussion Problems:, 57, 59, 67, 74 OC CQ P: 4, 5, 8, 8, 0, 9,, 4, 9, 4, 5, 5 Discussion Problems:, 57, 59,
More informationLecture L25-3D Rigid Body Kinematics
J. Peraire, S. Winall 16.07 Dynamics Fall 2008 Version 2.0 Lecture L25-3D Rigi Boy Kinematics In this lecture, we consier the motion of a 3D rigi boy. We shall see that in the general three-imensional
More informationUniversal Law of Gravitation
Universal Law of Gravitation Law: Every body exerts a force of attraction on every other body. This force called, gravity, is relatively weak and decreases rapidly with the distance separating the bodies
More informationANALYTICAL METHODS FOR ENGINEERS
UNIT 1: Unit code: QCF Level: 4 Credit value: 15 ANALYTICAL METHODS FOR ENGINEERS A/601/1401 OUTCOME - TRIGONOMETRIC METHODS TUTORIAL 1 SINUSOIDAL FUNCTION Be able to analyse and model engineering situations
More informationDetermination of source parameters from seismic spectra
Topic Determination of source parameters from seismic spectra Authors Michael Baumbach, and Peter Bormann (formerly GeoForschungsZentrum Potsdam, Telegrafenberg, D-14473 Potsdam, Germany); E-mail: pb65@gmx.net
More informationCourse 8. An Introduction to the Kalman Filter
Course 8 An Introduction to the Kalman Filter Speakers Greg Welch Gary Bishop Kalman Filters in 2 hours? Hah! No magic. Pretty simple to apply. Tolerant of abuse. Notes are a standalone reference. These
More informationInformation regarding the Lockheed F-104 Starfighter F-104 LN-3. An article published in the Zipper Magazine #48. December-2001. Theo N.M.M.
Information regarding the Lockheed F-104 Starfighter F-104 LN-3 An article published in the Zipper Magazine #48 December-2001 Author: Country: Website: Email: Theo N.M.M. Stoelinga The Netherlands http://www.xs4all.nl/~chair
More informationLogistic Regression. Jia Li. Department of Statistics The Pennsylvania State University. Logistic Regression
Logistic Regression Department of Statistics The Pennsylvania State University Email: jiali@stat.psu.edu Logistic Regression Preserve linear classification boundaries. By the Bayes rule: Ĝ(x) = arg max
More informationArtificial Intelligence
Artificial Intelligence Robotics RWTH Aachen 1 Term and History Term comes from Karel Capek s play R.U.R. Rossum s universal robots Robots comes from the Czech word for corvee Manipulators first start
More informationLecture 3: Coordinate Systems and Transformations
Lecture 3: Coordinate Systems and Transformations Topics: 1. Coordinate systems and frames 2. Change of frames 3. Affine transformations 4. Rotation, translation, scaling, and shear 5. Rotation about an
More informationASEN 3112 - Structures. MDOF Dynamic Systems. ASEN 3112 Lecture 1 Slide 1
19 MDOF Dynamic Systems ASEN 3112 Lecture 1 Slide 1 A Two-DOF Mass-Spring-Dashpot Dynamic System Consider the lumped-parameter, mass-spring-dashpot dynamic system shown in the Figure. It has two point
More informationDesign-Simulation-Optimization Package for a Generic 6-DOF Manipulator with a Spherical Wrist
Design-Simulation-Optimization Package for a Generic 6-DOF Manipulator with a Spherical Wrist MHER GRIGORIAN, TAREK SOBH Department of Computer Science and Engineering, U. of Bridgeport, USA ABSTRACT Robot
More informationCopyright 2011 Casa Software Ltd. www.casaxps.com
Table of Contents Variable Forces and Differential Equations... 2 Differential Equations... 3 Second Order Linear Differential Equations with Constant Coefficients... 6 Reduction of Differential Equations
More informationSome Comments on the Derivative of a Vector with applications to angular momentum and curvature. E. L. Lady (October 18, 2000)
Some Comments on the Derivative of a Vector with applications to angular momentum and curvature E. L. Lady (October 18, 2000) Finding the formula in polar coordinates for the angular momentum of a moving
More informationWhen the fluid velocity is zero, called the hydrostatic condition, the pressure variation is due only to the weight of the fluid.
Fluid Statics When the fluid velocity is zero, called the hydrostatic condition, the pressure variation is due only to the weight of the fluid. Consider a small wedge of fluid at rest of size Δx, Δz, Δs
More informationElectrical Engineering 103 Applied Numerical Computing
UCLA Fall Quarter 2011-12 Electrical Engineering 103 Applied Numerical Computing Professor L Vandenberghe Notes written in collaboration with S Boyd (Stanford Univ) Contents I Matrix theory 1 1 Vectors
More informationProgettazione Funzionale di Sistemi Meccanici e Meccatronici
Camme - Progettazione di massima prof. Paolo Righettini paolo.righettini@unibg.it Università degli Studi di Bergamo Mechatronics And Mechanical Dynamics Labs November 3, 2013 Timing for more coordinated
More informationwww.mathsbox.org.uk Displacement (x) Velocity (v) Acceleration (a) x = f(t) differentiate v = dx Acceleration Velocity (v) Displacement x
Mechanics 2 : Revision Notes 1. Kinematics and variable acceleration Displacement (x) Velocity (v) Acceleration (a) x = f(t) differentiate v = dx differentiate a = dv = d2 x dt dt dt 2 Acceleration Velocity
More informationA MONTE CARLO DISPERSION ANALYSIS OF A ROCKET FLIGHT SIMULATION SOFTWARE
A MONTE CARLO DISPERSION ANALYSIS OF A ROCKET FLIGHT SIMULATION SOFTWARE F. SAGHAFI, M. KHALILIDELSHAD Department of Aerospace Engineering Sharif University of Technology E-mail: saghafi@sharif.edu Tel/Fax:
More informationAP1 Oscillations. 1. Which of the following statements about a spring-block oscillator in simple harmonic motion about its equilibrium point is false?
1. Which of the following statements about a spring-block oscillator in simple harmonic motion about its equilibrium point is false? (A) The displacement is directly related to the acceleration. (B) The
More informationBasic Equations, Boundary Conditions and Dimensionless Parameters
Chapter 2 Basic Equations, Boundary Conditions and Dimensionless Parameters In the foregoing chapter, many basic concepts related to the present investigation and the associated literature survey were
More informationUnderstanding Purposeful Human Motion
M.I.T Media Laboratory Perceptual Computing Section Technical Report No. 85 Appears in Fourth IEEE International Conference on Automatic Face and Gesture Recognition Understanding Purposeful Human Motion
More informationF = ma. F = G m 1m 2 R 2
Newton s Laws The ideal models of a particle or point mass constrained to move along the x-axis, or the motion of a projectile or satellite, have been studied from Newton s second law (1) F = ma. In the
More informationState of Stress at Point
State of Stress at Point Einstein Notation The basic idea of Einstein notation is that a covector and a vector can form a scalar: This is typically written as an explicit sum: According to this convention,
More information