Abstract

This paper presents a novel approach for algorithmic singularity avoidance for reactionless visual servoing of a satellite mounted space robot. Task priority approach is used to perform visual servoing while reactionless manipulation of the space robot. Algorithmic singularity is prominent in such cases of prioritizing two tasks. The algorithmic singularity is different from the kinematic and dynamic singularities as the former is an artefact of the tasks at hand, and difficult to predict. In this paper, we present a geometric interpretation of its occurrence, and propose a method to avoid it. The method involves path planning in image space, and generates a sequence of images that guides the robot towards goal avoiding algorithmic singularity. The method is illustrated through numerical studies on a 6-DOF planar dual-arm robot mounted on a service satellite.

Abstract

This paper presents a new hierarchical architecture for parallelizing the computation intensive rapidly exploring random tree problem. The architecture resembles a tree like structure that agglutinates minimal inter-module communication of a shared memory with data integrity of a distributed memory. Another novelty of this research has been in quantitatively analysing the performance metrics of the RRT algorithm across numerous embedded hardware solutions and ultimately implementing this algorithm on an FPGA to achieve hardware level optimization that offers real time performance and economical power consumption levels. We then analyse our implementation against hardware implementation of other scalable parallel RRT methods for motion planning.

Abstract

This work focuses on enhancing step descending ability of the modular robot proposed in [16]. The proposed robot consists of three modules connected with each other through passive joints. It is propelled using an active pair of wheels per module. Since there are no actuators at the joints, the joints are not susceptible to losing operability while traversing on rugged terrain. However with the absence of actuators, we face the issue of the robot toppling over when an abnormally large obstacle is encountered. This shortcoming is overcome with the use of compliant joints. The compliant joints are designed by employing springs of optimal stiffness, which is calculated through an optimization formulation aided with the constraints presented by the static analysis of the robot. The novelty lies in the systematic design of compliant joint for step descent. The robot is successful in climbing and descending obstacles of dimension 17 cm. Simulations of the mathematically modelled robot are carried out. The results from the same are validated on a working prototype and presented.

Abstract

In this paper, we propose a better planning technique of the standard walking gaits for a quadruped robot than the conventional successive gait transition method to realize omnidirectional static walking. The technique involved planning the sequence as well as motion of the swinging and supporting legs. The relationship between the stability margin, the stride length and the duty factor are also formulated mathematically. The proposed modified crawl gait is compared to the conventional method with respect to the above parameters geometrically as well as mathematically and is shown to have positive stability margin at all times. The successive gait transition is demonstrated on the modified crawl and rotation gaits. Computer simulations of a model quadruped robot were performed to validate the theory proposed. Experiments were performed on an actual quadruped robot to realize the omnidirectional static walking with increased stability margin.

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.