Abstract

Overtaking of a vehicle moving on structured roads is one of the most frequent driving behavior. In this work, we have described a Real Time Control System based framework for overtaking maneuver of autonomous vehicles. Proposed framework incorporates Intelligent Planning and Modular control modules. Intelligent Planning module of the framework enables the vehicle to intelligently select the most appropriate behavioral characteristics given the perceived operating environment. Subsequently, Modular control module reduces the search space of overtaking trajectories through an SVM based learning approach. These trajectories are then examined for possible future time collision using Velocity Obstacle. It employs non linear time scaling that provides for continuous trajectories in the space of linear and angular velocities to achieve continuous curvature overtaking maneuvers respecting velocity and acceleration bounds. Further time scaling also can scale velocities to avoid collisions and can compute a time optimal trajectory for the learned behavior. The preliminary results show the appropriateness of our proposed framework in virtual urban environment.

Abstract

Autonomous navigation of generic monocular quadcopter in the natural environment requires sophisticated mechanism for perception, planning and control. In this work, we have described a framework which performs perception using monocular camera and generates minimum time collision free trajectory and control for any commercial quadcopter flying through cluttered unknown environment. The proposed framework first utilizes supervised learning approach to estimate the dense depth map for video stream obtained from frontal monocular camera. This depth map is initially transformed into Ego Dynamic Space and subsequently, is used for computing locally traversable way-points utilizing binary integer programming methodology. Finally, trajectory planning and control module employs a convex programming technique to generate collision-free trajectory which follows these way-points and produces appropriate control inputs for the quadcopter. These control inputs are computed from the generated trajectory in each update. Hence, they are applicable to achieve closed-loop control similar to model predictive controller. We have demonstrated the applicability of our system in controlled indoors and in unstructured natural outdoors environment.

Abstract

Quality of work life is the extent of relationships between individuals and organizational factors that exist in the working environment. Quality of work life is the extent to which workers can satisfy important personal needs through their experiences in the organization. It focuses strongly on providing a work environment conducive to satisfying individual needs. It is assumed that if employees have more positive attitudes about the organization and their productivity increases, everything else being equal, the organization should be more effective. The present study was conducted to examine whether the work related factors and demographic factors have any relationship with the perception of quality of work life and to explore the relationship between quality of work and quality of life in textile industries located at East Godavari district. The work related factors are combined in six categories: working environment, welfare measures, safety measures, supervision, participation in decision making and intercommunication. The results hold that demographic factors and work related factors have significant relationship with perception of quality of work life.

Abstract

Computing time optimal motions along specified paths forms an integral part of the solution methodology for many motion planning problems. Conventionally, this optimal control problem is solved considering piece-wise constant parametrization for the control input which leads to convexity and sparsity in the optimization structure. However, it also results in discontinuous control trajectory which is difficult to track. Thus, in this paper we revisit this time optimal control problem with the primary motivation of ensuring a high degree of smoothness in the resulting motion profile. In particular, we solve it with continuity constraints in control and higher order motion derivatives like jerk, snap etc. It is clear that such constraints would necessitate the use of time varying control inputs over the commonly used piece-wise constant form. The primary contribution of the current work lies in the introduction of a C ∞ class of time scaling functions represented as parametric exponentials. This in turn allows us to represent time varying control inputs as products of parametric exponential and a polynomial functions. We present the motivation behind adopting such representation of time scaling function over more common polynomial forms, both from mathematical as well as implementation standpoint. We also show that the proposed representation of time scaling function and control input leads to a very simple optimization structure where most of the constraints are linear. The non-linearity has a quasi-convex structure which can be reformulated into a simple difference of convex form. Thus, the resulting optimization can be efficiently solved through sequential convex programming where, at each iteration, the constraints in difference of convex form are further simplified to more conservative linear constraints.

Abstract

In this paper we present a non-linear time scaling based formulation for computing time optimal motions along specified paths. The primary motivation behind the current work is to introduce acceleration continuity constraints within the time optimal framework. Such constraint necessitates the use of time varying controls instead of commonly used piecewise constant controls. We propose a novel extension of our previously developed concept of non-linear time scaling through which it is possible to parametrize controls as piece-wise product of a exponential and a linear function. We show that such representation leads to a very simple optimization structure with primarily linear constraints. The non-linearity has a quasi-convex structure which we reformulate into a simple difference of convex form. A sequential convex programming framework is utilised to solve the optimization as a sequence of sparse quadratic programmes. The proposed optimization is an improvement over the current state of the art frameworks which introduces acceleration continuity constraints in the time optimal framework through highly non-linear and non-convex optimizations.

Abstract

Cepstral analysis has been widely used in audio and speech processing applications because of its ability to reveal periodicities in a signal. The presence of cyclic prefix (CP) in orthogonal frequency division multiplexing (OFDM) signals induces periodicities. Motivated by this, the paper focuses on cepstral analysis of OFDM signal. The distributions of cepstral coefficients are de-rived for two scenarios of noise only and OFDM signal in noise. Itis shown that the OFDM cepstrum is significantly different from the additive white Gaussian noise (AWGN) cepstrum and can be used to detect OFDM waveforms. It is also shown that the cepstrum of OFDM is rich in features and can be used to estimate OFDM parameters such as number of sub carriers and length of the CPin an OFDM symbol. These OFDM waveform parameters can be used to automatically recognize or classify different OFDM waveforms, which are important for cognitive radios, coexistence of heterogeneous networks and signal intelligence. Two schemes are proposed to detect OFDM based primary user (PU) signalsin cognitive radio systems. The distributions of the test statistics under the two hypotheses are established.Neyman–Pearson detec-tions trategy is employed.Algorithms for estimating the number of subcarrier and the length of the CP are proposed and their performances studied through simulations. Later the proposed schemes are extended to cooperative sensing scenario with multiple secondary users (SUs) and it is shown that the collaboration between them significantly improve the performance of the proposed cepstrum based detection and estimation schemes.

Abstract

Matroidal networks were introduced by Doughert yet al.and have been well studied in the recent past. It was shown that a network has a scalar linear network coding solution if and only if it is matroidal associated with a representable matroid. A particularly interesting feature of this development is the ability to construct (scalar and vector)linearly solvable networks using certain classes of matroids.Furthermore, it was shown through the connection between network coding and matroid theory that linear network coding is not always sufficient for general network coding scenarios.The current work attempts to establish a connection between matroid theory and network-error correcting and detecting codes. In a similar vein to the theory connecting matroids and network coding, we abstract the essential aspects of linear network-error detecting codes to arrive at the definition of a matroidal error detecting network(and similarly, a matroidal error correcting networkabstracting from network-error correcting codes). An acyclic network (with arbitrary sink demands) is then shown to possess a scalar linear error detecting (correcting)network code if and only if it is a matroidal error detecting(correcting) network associated with a representable matroid.Therefore, constructing such network-error correcting and detecting codes implies the construction of certain representable matroids that satisfy some special conditions, and vice versa.We then present algorithms that enable the construction of matroidal error detecting and correcting networks with a specified capability of network-error correction. Using these construction algorithms, a large class of hitherto unknown scalar linearly solvable networks with multi source, multicast,and multiple- unicast network-error correcting codes is made available for theoretical use and practical implementation,with parameters, such as number of information symbols,number of sinks, number of coding nodes, error correcting capability, and so on, being arbitrary but for computing power(for the execution of the algorithms). The complexity of the construction of these networks is shown to be comparable with the complexity of existing algorithms that design multi cast scalar linear network-error correcting codes. Finally, we also show that linear network coding is not sufficient for the general network-error correction (detection) problem with arbitrary demands. In particular, for the same number of network errors,we show a network for which there is a nonlinear network-error detecting code satisfying the demands at the sinks, where as there are no linear network-error detecting codes that do the same.

Abstract

A code is said to be data-local maximally recoverable if (i) all the information symbols have locality and (ii) any erasure pattern which can be potentially recovered (i.e., the number of equations is equal to the number of unknowns) is recovered by the code. A code is said to be local maximally recoverable if(i) all the symbols of the code have locality and (ii) from the above holds. In this paper, we establish the matroid structures corresponding to data-local and local maximally recoverable codes (MRC). The matroid structures of these codes can be used to determine the associated Tutte polynomial. Greene proved that the weight enumerators of any code can be determined from its associated Tutte polynomial. We will use this result to derive explicit expressions for the weight enumerators of data-local MRC. Also, Britz proved that the higher support weights of any code can be determined from its associated Tutte polynomial.We will use this result to derive expressions for the higher support weights of data-local and local MRC with two local codes.

Abstract

Image based reconstruction of urban environments is a challenging problem that deals with optimization of large number of variables, and has several sources of errors like the presence of dynamic objects. Since most large scale approaches make the assumption of observing static scenes, dynamic objects are relegated to the noise modelling section of such systems. This is an approach of convenience since the RANSAC based framework used to compute most multiview geometric quantities for static scenes naturally confine dynamic objects to the class of outlier measurements. However, reconstructing dynamic objects along with the static environment helps us get a complete picture of an urban environment. Such understanding can then be used for important robotic tasks like path planning for autonomous navigation, obstacle tracking and avoidance, and other areas. In this paper, we propose a system for robust SLAM that works in both static and dynamic environments. To overcome the challenge of dynamic objects in the scene, we propose a new model to incorporate semantic constraints into the reconstruction algorithm. While some of these constraints are based on multi-layered dense CRFs trained over appearance as well as motion cues, other proposed constraints can be expressed as additional terms in the bundle adjustment optimization process that does iterative refinement of 3D structure and camera / object motion trajectories. We show results on the challenging KITTI urban dataset for accuracy of motion segmentation and reconstruction of the trajectory and shape of moving objects relative to ground truth. We are able to show average relative error reduction by 41 % for moving object trajectory reconstruction relative to state-of-the-art methods like TriTrack[16], as well as on standard bundle adjustment algorithms with motion segmentation.

Abstract

We propose a novel collision avoidance formulation in the intent space, suitable for navigation of non-holonomic robots in human centered environments. The intent space is characterized by various bands of trajectories wherein each band can be thought to be a representation of a possible human intended motion and the uncertainty associated with it. We ascribe probabilities to human intentions and characterize the uncertainty around it through Gaussian state transition and its concomitant Gaussian parametric distribution. Given an intent space we design avoidance maneuvers based on our recent works on time scaled collision cone concept which provides analytical characterization of collision free velocities in dynamic environments. In this paper, we present a probabilistic variant of the time scaled collision cone which allows us to relate space of collision free velocities to an associated confidence measure. We also develop an optimization framework which extract such specific solutions from the entire solution space that achieves an elegant balance between the objective of minimizing risk and ease of avoidance maneuver. We further show that by accounting for possible intents in human motion, the method transcends the realm of reactive avoidance to proactive anticipation of collisions and its effective avoidance, thereby increasing the overall safety of navigation.