anup katake

Jet Propulsion Laboratory, California Institute of Technology, Guidance, Nav and Control, PhD, Research Engineer

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Address: Pasadena, California, USA

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Steven Pinker

Harvard University

Armando Marques-Guedes

UNL - New University of Lisbon

Fabio Cuzzolin

Oxford Brookes University

Roshan Chitrakar

Nepal College of Information Technology

Lev Manovich

Graduate Center of the City University of New York

Dr.Yousery Sherif

Mansoura University

Irina Kolesnik

Moscow State University

Kiran Dasari

University of Illinois at Urbana-Champaign

Isad Saric

University of Sarajevo

Professor Dr. Loutfy H . Madkour

Al-Baha University, Kingdom of Saudi Arabia

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Papers by anup katake

Study of underactuated mechanisms in the presence of holonomic constraints: the constrained Acrobot

by anup katake and Ravi N Banavar

Proceedings of IEEE International Conference on Industrial Technology 2000 (IEEE Cat. No.00TH8482), 2000

Study of underactuated mechanisms in the presence of holonomic constraints: the constrained Acrobot

Applied Thermal Engineering, 2000

Underactuated mechanical systems are mechanical systems with fewer actuators than degrees-of-free... more Underactuated mechanical systems are mechanical systems with fewer actuators than degrees-of-freedom. Examples of underactuated mechanisms of engineering importance are mobile robots, wheeled vehicles and space robotics. One such example of interest is the Acrobot. In this paper the Acrobot is modified so as to impose holonomic constraints on the system. The problem of formulating the equations of motions of the system along with the holonomic constraints using traditional methods is then addressed. An alternate method is then used to incorporate the holonomic constraints. This is followed by design of a control law for the swing up of the constrained Acrobot.

Modeling, image processing and attitude estimation of high speed star sensors

Attitude estimation and angular velocity estimation are the most critical components of a spacecr... more Attitude estimation and angular velocity estimation are the most critical components of a spacecraft's guidance, navigation and control. Usually, an array of tightly-coupled sensors (star trackers, gyroscopes, sun sensors, magnetometers) is used to estimate these quantities. The cost (financial, mass, power, time, human resources) for the integration of these separate sub-systems is a major deterrent towards realizing the goal of smaller, cheaper and faster to launch spacecrafts/satellites. In this work, we present a novel stellar imaging system that is capable of estimating attitude and angular velocities at true update rates of greater than 100Hz, thereby eliminating the need for a separate star tracker and gyroscope sub-systems. High image acquisition rates necessitate short integration times and large optical apertures, thereby adding mass and volume to the sensor. The proposed high speed sensor overcomes these difficulties by employing light amplification technologies coupled with fiber optics. To better understand the performance of the sensor, an electro-optical model of the sensor system is developed which is then used to design a high-fidelity night sky image simulator. Novel star position estimation algorithms based on a two-dimensional Gaussian fitting to the star pixel intensity profiles are then presented. These algorithms are non-iterative, perform local background estimation in the vicinity of the star and lead to significant improvements in the star centroid determination. Further, a new attitude determination algorithm is developed that uses the inter-star angles of the identified stars as constraints to recompute the body measured vectors and provide a higher accuracy estimate of the attitude as compared to existing methods. The spectral response of the sensor is then used to develop a star catalog generation method that results in a compact on-board star catalog. Finally, the use of a fiber optic faceplate is proposed as an additional means of stray light mitigation for the system. This dissertation serves to validate the conceptual design of the high update rate star sensor through analysis, hardware design, algorithm development and experimental testing.

StarCam SG100: a high-update rate, high-sensitivity stellar gyroscope for spacecraft

A typical configuration of attitude estimation system for a spacecraft consists of multiple star ... more A typical configuration of attitude estimation system for a spacecraft consists of multiple star trackers, coupled with gyroscopes and coarse sensors such as Sun or Earth sensors. The combination of these sensors results in high mass, volume and power requirements. StarVision has been actively developing an unique sensor that is capable of providing attitude and angular rate information from star measurements at update rates of 100Hz. The SG100 stellar gyroscope is built around an image intensified imaging core that is sensitive to stars as faint as visual magnitude 10 at very small exposure times. In this paper we report the development of the SG100 sensor. Results from sensitivity analysis as well as night sky tests at angular rates as high as 20deg/s will be presented. Test data shows that the SG100 far exceeds the requirements of star sensitivity and noise levels even under non-ideal imaging conditions.

STARNAV I: Star Tracker Experiment on the Space Shuttle Mission STS107

ABSTRACT We report the results of a flight experiment aboard the ill-fated STS-107 Columbia Space... more ABSTRACT We report the results of a flight experiment aboard the ill-fated STS-107 Columbia Space Shuttle. Our results were telemetered during the first 10 days of the mission. The main purpose of the experiment was to test an advanced star pattern recognition algorithm. While the overall experiment was a success, we experienced un-anticipated difficulties. Due to the scattered light reflections off the SpaceHab roof, several image frames were not useable. Also, due to non-functioning thermo-electrical cooler, the background noise was higher than anticipated and varied during thermal cycling. However, these challenges were overcome and we report herein successful star identification and attitude determination results using a novel LISA (Lost-In-Space-Algorithm).

LandingNav: a precision autonomous landing sensor for robotic platforms on planetary bodies

Increased interest in the exploration of extra terrestrial planetary bodies calls for an increase... more Increased interest in the exploration of extra terrestrial planetary bodies calls for an increase in the number of spacecraft landing on remote planetary surfaces. Currently, imaging and radar based surveys are used to determine regions of interest and a safe landing zone. The purpose of this paper is to introduce LandingNav, a sensor system solution for autonomous landing on planetary bodies that enables landing on unknown terrain. LandingNav is based on a novel multiple field of view imaging system that leverages the integration of different state of the art technologies for feature detection, tracking, and 3D dense stereo map creation. In this paper we present the test flight results of the LandingNav system prototype. Sources of errors due to hardware limitations and processing algorithms were identified and will be discussed. This paper also shows that addressing the issues identified during the post-flight test data analysis will reduce the error down to 1-2%, thus providing for a high precision 3D range map sensor system.

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Attitude and Interlock Angle Estimation Using Split-Field-of-View Star Tracker1

An efficient Kalman filter based algorithm has been proposed for the spacecraft attitude estimati... more An efficient Kalman filter based algorithm has been proposed for the spacecraft attitude estimation problem using a novel split-field-of-view star camera and three-axis rate gyros. The conventional spacecraft attitude algorithm has been modified for on-orbit estimation of interlock angles between the two fields of view of star camera, gyro axis, and the spacecraft body frame. Real time estimation of the interlock angles makes the attitude estimates more robust to thermal and environmental effects than in-ground estimation, and makes the overall system more tolerant of off-nominal structural, mechanical, and optical assembly anomalies.

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Probabilistic Combination of Multiple Evidence

In pattern recognition systems, data fusion is an important issue and evidence theory is one such... more In pattern recognition systems, data fusion is an important issue and evidence theory is one such method that has been successful. Many researchers have proposed different rules for evidence theory, and recently, a variety of averaging rules emerged that are better than others. In these methods, the key issue becomes how to give the weights to the multiple contributing factors, in order to calculate the average. To get better weights for the multiple bodies of evidence, we propose the use of structural information of the evidence. The bodies of evidence lie on a certain informational structure which can be described by a probability distribution and the probability of each evidence can serve as a weight for the evidence. Our experimental results show that our method outperforms other previous methods.

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Manifold Alpha-Integration

Manifold learning has been successfully used for finding dominant factors (low-dimensional manifo... more Manifold learning has been successfully used for finding dominant factors (low-dimensional manifold) in a high-dimensional data set. However, most existing manifold learning algorithms only consider one manifold based on one dissimilarity matrix. For utilizing multiple manifolds, a key question is how different pieces of information can be integrated when multiple measurements are available. Amari proposed α-integration for stochastic model integration, which is a generalized averaging method that includes as a special case arithmetic, geometric, and harmonic averages. In this paper, we propose a new generalized manifold integration algorithm equipped with α-integration, manifold α -integration (MAI). Interestingly, MAI can be shown to be a generalization of other integration methods (that may or may not use manifolds) like kernel fusion or mixture of random walk. Our experimental results also confirm that integration of multiple sources of information on individual manifolds is superior to the use of individual manifolds separately, in tasks including classification and sensorimotor integration.

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Alpha-integration of multiple evidence

In pattern recognition, data integration is a processing method to combine multiple sources so th... more In pattern recognition, data integration is a processing method to combine multiple sources so that the combined result can be more accurate than a single source. Evidence theory is one of the methods that have been successfully applied to the data integration task. Since Dempster-Shafer theory as the first evidence theory can be against our intuitive reasoning with some data sets, many researchers have proposed different rules for evidence theory. Among all these rules, the averaging rule is known to be better than others. On the other hand, α-integration was proposed by Amari as a principled way of blending multiple positive measures. It is a generalized averaging algorithm including arithmetic, geometric and harmonic means as its special case. In this paper, we generalize evidence theory with αintegration. Our experimental results show how our proposed methods work.

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VisNAV 100: a robust, compact imaging sensor for enabling autonomous air-to-air refueling of aircraft and unmanned aerial vehicles

To enable autonomous air-to-refueling of manned and unmanned vehicles a robust high speed relativ... more To enable autonomous air-to-refueling of manned and unmanned vehicles a robust high speed relative navigation sensor capable of proving high accuracy 3DOF information in diverse operating conditions is required. To help address this problem, StarVision Technologies Inc. has been developing a compact, high update rate (100Hz), wide field-of-view (90deg) direction and range estimation imaging sensor called VisNAV 100. The sensor is fully autonomous requiring no communication from the tanker aircraft and contains high reliability embedded avionics to provide range, azimuth, elevation (3 degrees of freedom solution 3DOF) and closing speed relative to the tanker aircraft. The sensor is capable of providing 3DOF with an error of 1% in range and 0.1deg in azimuth/elevation up to a range of 30m and 1 deg error in direction for ranges up to 200m at 100Hz update rates. In this paper we will discuss the algorithms that were developed in-house to enable robust beacon pattern detection, outlier rejection and 3DOF estimation in adverse conditions and present the results of several outdoor tests. Results from the long range single beacon detection tests will also be discussed.

Learning alpha-integration with partially-labeled data

Sensory data integration is an important task in human brain for multimodal processing as well as... more Sensory data integration is an important task in human brain for multimodal processing as well as in machine learning for multisensor processing. α-integration was proposed by Amari as a principled way of blending multiple positive measures (e.g., stochastic models in the form of probability distributions), providing an optimal integration in the sense of minimizing the α-divergence. It also encompasses existing integration methods as its special case, e.g., weighted average and exponential mixture. In α-integration, the value of α determines the characteristics of the integration and the weight vector w assigns the degree of importance to each measure. In most of the existing work, however, α and w are given in advance rather than learned. In this paper we present two algorithms, for learning α and w from data when only a few integrated target values are available. Numerical experiments on synthetic as well as real-world data confirm the proposed method's effectiveness.

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Study of underactuated mechanisms in the presence of holonomic constraints: the constrained Acrobot

by anup katake and Ravi N Banavar

Proceedings of IEEE International Conference on Industrial Technology 2000 (IEEE Cat. No.00TH8482), 2000

Study of underactuated mechanisms in the presence of holonomic constraints: the constrained Acrobot

Applied Thermal Engineering, 2000

Modeling, image processing and attitude estimation of high speed star sensors

StarCam SG100: a high-update rate, high-sensitivity stellar gyroscope for spacecraft

STARNAV I: Star Tracker Experiment on the Space Shuttle Mission STS107

LandingNav: a precision autonomous landing sensor for robotic platforms on planetary bodies

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Attitude and Interlock Angle Estimation Using Split-Field-of-View Star Tracker1

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Probabilistic Combination of Multiple Evidence

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Manifold Alpha-Integration

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Alpha-integration of multiple evidence

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VisNAV 100: a robust, compact imaging sensor for enabling autonomous air-to-air refueling of aircraft and unmanned aerial vehicles

Learning alpha-integration with partially-labeled data

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