Abstract

SPACE robots can be used to perform several onorbit tasks such as capturing space debris, servicing of satellites, and refueling. The proliferation of satellites as well as the growing interest in debris capture make it necessary to have robots that can perform these tasks autonomously [1]. The dynamics of robots in space differ from that of a grounded robot. The coupling of the arms and the base of a space robot creates reaction forces and moments on the base whenever the arms execute a maneuver, causing the base to rotate and translate in accordance with the laws of conservation of linear and angular momenta. However, it is generally desirable to keep the attitude of the base fixed relative to the sun and the Earth (or other bodies) for navigation and communication purposes, or to maintain the target in the field of view of the sensors. A change in celestial orientation may also result in loss of communication with the data relay satellite or the ground station. While attitude control using thrusters may be used, such operations consume fuel which is mainly reserved for orbital maneuvers. It has also been shown that manipulation without the use of attitude controllers is more robust [2]. The translation of the base however, does not pose signicant side eects [3]. Hence, researchers have focused on robotic manipulation with zero or minimal change in attitude, which is termed reactionless manipulation

Abstract

Field Programmable Gate Arrays (FPGA) exceed the computing power of software based implementations by breaking the paradigm of sequential execution and accomplishing more per clock cycle by enabling hardware level parallelization at an architectural level. Introducing parallel architectures for a computationally intensive algorithm like Rapidly Exploring Random Trees (RRT) will result in an exploration that is fast, dense and uniform. Through a cost function delineated in later sections, FPGA based combinatorial architecture delivers superlative speed-up but consumes very high power while hierarchical architecture delivers relatively lower speed-up with acceptable power consumption levels. To combine the qualities of both, a hybrid architecture, that encompasses both combinatorial and hierarchical architecture, is designed. To determine the number of RRT nodes to be allotted to the combinatorial and hierarchical blocks of the hybrid architecture, a cost function, comprised of fundamentally inversely related speed-up and power parameters, is formulated. This maximization of cost function, with its associated constraints, is then mathematically solved using a modified branch and bound, that leads to optimal allocation of RRT modules to both blocks. It is observed that this hybrid architecture delivers the highest performance-per-watt out of the three architectures for differential, quad-copter and fixed wing kinematics.

Abstract

In this paper, we present a framework for generating smooth and stable trajectories for wheeled mobile robots moving on uneven terrains. Instead of relying on static stability measures, the paper incorporates velocity and acceleration based constraints like no-slip and permanent wheel ground contact conditions in the planning framework. The paper solves this complicated problem in a computationally efficient manner with full 3D dynamics of the robot. The two major aspects of the proposed work are: Firstly, closed form functional relationships are derived which describes the 6 dof evolution of the robot’s state on an arbitrary 2.5D uneven terrain. This enables us to have a fast evaluation of robot’s dynamics along any candidate trajectory. Secondly, a novel concept of non-linear time scaling is introduced through which simple algebraic relations defining the bounds on velocities and accelerations are obtained. Moreover, non-linear time scaling also provides a new approach for manipulating velocities and accelerations along given geometric paths. It is thus exploited to obtain stable velocity and acceleration profiles. Extensive simulation results are presented to demonstrate the efficacy of the proposed methodology.

Abstract

Curb detection is a critical component of driver assistance and autonomous driving systems. In this paper, we present a discriminative approach to the problem of curb detection under diverse road conditions. We define curbs as the intersection of drivable and non-drivable area which are classified using dense Conditional random fields(CRF). In our method, we fuse output of a neural network used for pixel-wise semantic segmentation with depth and color information from stereo cameras. CRF fuses the output of a deep model and height information available in stereo data and provides improved segmentation. Further we introduce temporal smoothness using a weighted average of Segnet output and output from a probabilistic voxel grid as our unary potential. Finally, we show improvements over the current state of the art neural networks. Our proposed method shows accurate results over large range of variations in curb curvature and appearance, without the need of retraining the model for the specific dataset.

Abstract

Frontier detection is a critical component in autonomous exploration, wherein the robot decides the next best location to move in order to continue its mapping process. The existing frontier detection methods require dense reconstruction which is difficult to attain in a poorly textured indoor environment using a monocular camera. In this effort, we present an alternate method of detecting frontiers during the course of robot motion that circumvents the requirement of dense mapping. Based on the observation that frontiers typically occur around areas with sudden change in texture (zero-crossings), we propose a novel linear chain Conditional Random Field(CRF) formulation that is able to detect the presence or absence of frontier regions around such areas. We use cues like spread of 3D points and scene change around these areas as an observation to CRF. We demonstrate that this method gives us more relevant frontiers compared to other monocular camera based methods in the literature. Finally, we present results in an indoor environment, wherein frontiers are reliably detected around walls leading to new corridors, doors leading to new rooms or corridors and tables and other objects that open up to a new space in rooms.

Abstract

Periodic-frequent patterns (or itemsets) are an important class of regularities that exist in a transactional database. Finding these patterns involves discovering all frequent patterns that satisfy the user specified maximum periodicity constraint. This constraint controls the maximum inter-arrival time of a pattern in a database. The time complexity to measure periodicity of a pattern is O(n), where n represents the number of timestamps at which the corresponding pattern has appeared in a database. As unusually represents a high value in voluminous databases, determining the periodicity of every candidate pattern in the itemset lattice makes the periodic-frequent pattern mining a computationally expensive process. This paper introduces a novel approach to address this problem. Our approach determines the periodic interestingness of a pattern by adopting greedy search. The basic idea of our approach is to discover all periodic-frequent patterns by eliminating a periodic patterns based on suboptimal solutions. The best and worst case time complexities of our approach to determine the periodic interestingness of a frequent pattern are O(1) and O(n), respectively. We introduce two pruning techniques and propose a pattern-growth algorithm to find these patterns efficiently. Experimental results show that our algorithm is runtime efficient and highly scalable as well.

Abstract

Recommender system (RS) helps customers to select appropriate products from millions of products and has become a key component in e-commerce systems. Collaborative filtering (CF) based approaches are widely employed to build RSs. In CF, recommendation to the target user is computed after forming the corresponding neighbour-hood of users. Neighborhood of a target user is extracted based on the similarity between the product rating vector of the target user and the product rating vectors of individual users. In CF, the methodology employed for neighborhood formation influences the performance. In this paper, we have made an effort to improve the performance of CF by proposing a different approach to compute recommendations by considering two kinds of neighborhood. One is the neighborhood by considering the product ratings of the user as a single vector and the other is based on the neighborhood of the corresponding virtual users. For the target user,the virtual users are formed by dividing the ratings based on the category of products. We have proposed a combined approach to compute better recommendations by considering both kinds of neighborhoods.The experiments results on real world Movie Lens dataset show that the proposed approach improves the performance over CF.

Abstract

Building efficient search systems to extract relevant in-formation from a huge volume of legal judgments is a research is-sue. In the literature, efforts are being made to build efficient search systems in the legal domain by extending information retrieval approaches. We are making efforts to investigate improved approaches to extract relevant legal judgments for a given input judgment by exploiting text and citation information of legal judgments. Typically,legal judgments are very large text documents and contain several intricate legal concepts. In this paper, we analyze how the paragraph-level and citation information of the judgments could be exploited for retrieving relevant legal judgments for the given judgment. In this paper, we have proposed improved ranking approach to find the relevant legal judgments of a given judgment based on the similarity be-tween the paragraphs of the judgments by employing Okapi retrieval model and citation information. The user evaluation study on legal judgments data set delivered by Supreme Court of India shows that the proposed approach improves the ranking performance over the baseline approach. Overall, the analysis shows that there is a scope to exploit the paragraph-level and citation information of the judgments to improve the search performance.

Abstract

Periodic-frequent pattern mining involves finding all frequent patterns that have occurred at regular intervals in a transactional database. The basic model considers a pattern as periodic-frequent, if it satisfies the user-specified minimum support (min Sup) and maximum periodicity (maxP er) constraints. The usage of a single minSupandmaxP erfor an entire database leads to the are-item problem.When confronted with this problem in real-world applications, researchers have tried to address it using the item-specific minSupandmaxP er con-straints. It was observed that this extended model still generates a sig-nificant number of uninteresting patterns, and moreover, suffers from the issue of specifying item-specific minSupandmaxP er constraints.This paper proposes a novel model to address the rare-item problem in periodic-frequent pattern mining. The proposed model considers a pat-tern as interesting if its support and periodicity are close to that of its individual items. The al l-confidence is used as an interestingness measure to filter out uninteresting patterns in support dimension. In addition, anew interestingness measure, called periodic-al l-confidence, is being pro-posed to filter out uninteresting patterns in periodicity dimension. We have proposed a model by combining both measures and proposed a pattern-growth approach to resolve the rare-item problem and extract interesting periodic-frequent patterns. Experimental results show that the proposed model is efficient

Abstract

Periodic-frequent patterns are an important class of regularities which exists in a transactional database. A frequent pattern is called periodic-frequent if it appears at regular intervals in a transactional database. In the literature, a model of periodic-frequent patterns was proposed and pattern growth like approaches to extract patterns are being explored. In these approaches, a periodic-frequent pattern tree is built in which a transaction-id list is maintained at each path’s tail-node. As the typical size of transactional database is very huge in the modern e-commerce era, extraction of periodic-frequent patterns by maintaining transaction-ids in the tree requires more memory.In this paper, to reduce the memory requirements, we introduced a notion of period summary by capturing the periodicity of the patterns in a sequence of transaction-ids. While building the tree,the period summary of the transactions is computed and stored at the tail-node of the tree instead of the transaction-ids. We have also proposed a merging framework for period summaries for mining periodic-frequent patterns. The performance could be improved significantly as the memory required to store the period summaries is significantly less than the memory required to store the transaction-id list. Experimental results show that the pro-posed approach reduces the memory consumption significantly and also improves the runtime efficiency considerably over the existing approaches.