Abstract

This paper describes our submission for FIRE 2014 Shared Task on Transliterated Search. The shared task features two sub-tasks: Query word labeling and Mixed-script Ad hoc retrieval for Hindi Song Lyrics. Query Word Labeling is on token level language identification of query words in code-mixed queries and back-transliteration of identified Indian language words into their native scripts. We have developed letter based language models for the token level language identification of query words and a structured perceptron model for back-transliteration of Indic words. The second subtask for Mixed-script Ad hoc retrieval for Hindi Song Lyrics is to retrieve a ranked list of songs from a corpus of Hindi song lyrics given an input query in Devanagari or transliterated Roman script. We have used edit distance based query expansion and language modeling followed by relevance based reranking for the retrieval of relevant Hindi Song lyrics for a given query.

Abstract

GPUs with their massive Single Instruction Multiple Data (SIMD) capability have become attractive for scientific computation. The Kirchhoff-Helmholtz Transform (KHT) is a two dimensional integral commonly used in numerical reconstruction of the object from its experimentally measured diffraction pattern (called hologram). We explore the evaluation of KHT using GPU at various levels of optimisation. These optimisations are of two types:(a) algorithmic: exploiting the symmetries inherent in KHT, and (b) programmatic: optimizations that are specific to the GPU architecture like the lookup tables, scheduling of read/writes. From the numerical experiments, we report the speedup for each level of optimisation.

Abstract

A novel compliant robot is proposed for traversing on unstructured terrains. The robot consists of modules, each containing a link and an active wheel-pair, and neighboring modules are connected using a passive joint. This type of robots are lighter and provide high durability due to the absence of link-actuators. However, they have limited climbing ability due to tendency of tipping over while climbing big obstacles. To overcome this disadvantage, the use of compliant joints is proposed in this work. Stiffness of each compliant joint is estimated by formulating an optimization problem with an objective to minimize link joint moments while maintaining static-equilibrium. This is one of the key novelties of the proposed work. A design methodology is also proposed for developing an n-module compliant robot for climbing a given height on a known surface. The efficacy of the proposed formulation is illustrated using numerical simulations of the three and five module robots. The robot is successfully able to climb maximum heights upto three times and six times the wheel diameter using three and five modules, respectively. A working prototype was developed and the simulation results were successfully validated on it.

Abstract

This paper presents a novel visual servoing controller for a satellite mounted dual-arm space robot. The controller is designed to complete the task of servoing the robot's endeffectors to the desired pose, while regulating orientation of the base-satellite. Task redundancy approach is utilized to coordinate the servoing process and attitude of the base satellite. The visual task is defined as a primary task, while regulating attitude of the base satellite to zero is defined as a secondary task. The secondary task is formulated as an optimization problem in such a way that it does not affect the primary task, and simultaneously minimizes its cost function. A set of numerical experiments are carried out on a dual-arm space robot showing efficacy of the proposed control methodology.

Abstract

Small obstacles of the order of 0.5-3cms and homogeneous scenes often pose a problem for indoor mobile robots. These obstacles cannot be clearly distinguished even with the state of the art depth sensors or laser range finders using existing vision based algorithms. With the advent of sophisticated image processing algorithms like SLIC [1] and LSD [9], it is possible to extract rich information from an image which led us to develop a novel architecture to detect very small obstacles on the floor using a monocular camera. This information is further processed using a Markov Random Field based graph cut formalism that precisely segments the floor and detects obstacles which are extremely low. We show robust and accurate obstacle detection and floor segmentation in diverse environments over a large variety of objects found indoors. In our case, low lying obstacles, changing floor patterns and extremely homogeneous environments are properly classified which leads to a drastic decrease in the number of obstacles that may not be classified by existing robotic vision algorithms.

Abstract

In this paper, we present a posture control scheme for step climbing by an in-house developed three-segmented tracked robot, miniUGV. The posture control scheme results in minimum torque at the actuated joints of the segments. Non-linear optimization is carried out offline for progressively decreasing distance of the robot from the step with torque minimization as objective function and force balance, motor torque limits, slippage avoidance and interference avoidance constraints. The resulting angles of the joints are fitted to a third degree polynomial as a function of the robot distance from the step and the step height. It is shown that a single set of polynomial functions is sufficient for climbing steps of all permissible heights and angles of attack of the front segment. The methodology has been verified through simulation followed by implementation on the real robot. As a consequence of this optimization we find that the average current reduced by more than thirty percent, reducing power consumption and confirming the efficacy of the optimization framework

Abstract

In the field of active perception, object search is a widely studied problem. To search for an object in large rooms, it would be expensive to explore and check each object's similarity with the object of interest. The expense could uncontrollably bloat as the number of objects to be searched increases. If the objects are of the order of a 2-5cm, they appear very small, making it difficult for the present algorithms to recognize them. A general human strategy in such cases is to sparsely identify, from far away (4-6m), if the object of interest is present in the scene. Subsequently, each of the possible objects is analysed from closer proximity to recognize, for further manipulation. In this work, we present a similar framework. We reduce search-space, by identifying existential probability of a small object from a distance followed by a closer 3-D analysis of its point cloud to accurately recognize it. This is achieved by 2-D modelling of the objects using Gaussian Mixture Models followed by recognizing objects using efficient RGB-Depth based algorithm.

Abstract

The current paper proposes a trajectory optimization approach for navigating a non-holonomic wheeled mobile robot in dynamic environments. The dynamic obstacle's motion is not known and hence is represented by a band of predicted trajectories. The trajectory optimization can account for large number of predicted obstacle trajectories and seeks to avoid each predicted trajectory of every obstacle in the sensing range of the robot. The two primary contributions of the proposed trajectory optimization are (1): A computationally efficient method for computing the intersection space of collision avoidance constraints of large number of predicted obstacle trajectories. (2): A optimization framework to connect the current state to the solution space in time optimal fashion. The intersection/solution space computation is build on our earlier proposed concept of time scaled collision cone, which can be solved in closed form to obtain a set of formulae. These formulae describe how much and in what manner the temporal specification of a trajectory needs to be changed to avoid a given set of dynamic obstacles. This allows us to quickly evaluate solution space of time scaled collision cone over various candidate trajectories, thus reducing the problem of computing the intersection space to that of generating multiple homotopic trajectories. The optimization framework used to connect the current state to the solution space in time optimal fashion is based on the concept of non-linear time scaling, which induces a difference of convex form structure. Thus, on the theoretical side, we show that the various components of the proposed framework are computationally simple and involves solving sets of linear equations and using state of the art convex programming techniques. On the practical side we show that the proposed planner performs better than sampling based planners which treat dynamic obstacles as static over a short duration of time.

Abstract

In indoor environments, there would be several small objects lying around on the floor. In this work, we develop an efficient strategy to search for a set of queried objects amongst a large number of small objects lying around. Small objects of the order of 1cm – 5cm, appear very small, making it difficult for the present algorithms to recognize them from far away. A human like strategy in such cases is to infer each object's similarity to the queried objects, from far away. Subsequently, the objects of interest are approached and analyzed from a closer proximity through an optimal plan. We develop an optimal plan for the robot, to strategically visit a selected few among all the objects. From far away, we assign Existential Probabilities to the objects, indicating their similarity to queried objects. A Bayes' Net is constructed over the probabilities, to overlay and orient a Viewpoint Object Potential(VOP) map over potential search objects. VOP quantifies the probability of accurately recognizing an object through its RGB-D Point Cloud at various viewpoints. The belief from the Bayes' Net and the discriminative viewpoints from the VOP are utilized to formulate a Decision Tree which helps in building an optimal control plan. Hence, the robot reaches strategic viewpoints around potential objects, to recognize them through their RGB-D point clouds. The framework is experimentally evaluated using Kinect mounted on a Turtlebot using ROS platform.

Abstract

Frontier detection is a critical component in indoor mobile robot exploration, wherein the robot decides the next best location to move in order to continue with its mapping process. All frontier detection algorithms to the best of our knowledge require 3D locations of occupied regions as its input. In a monocular setting this entails a backend VSLAM algorithm that reconstructs the scene as the robot moves. Most monocular SLAM algorithms however provide sparse scene reconstruction from which frontiers cannot be reliably detected and estimated. In this effort we provide 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 vertical edges of walls, doors or tables we propose a novel linear chain CRF formulation that is able to detect the presence or absence of such frontier regions around such vertical edges. We used cues like increase in number of ground plane pixels and change in the spreading of optical flow vector, around those vertical edges. We also demonstrate that this method gives us more relevant frontiers as compared to methods based on reconstructing the scene through state-of-the art such SLAM algorithms such as PTAM. Finally, we present results in indoor scenes wherein frontiers are reliably detected around wall edges leading to new corridors, door edges leading to new rooms or corridors and table edges that opens up to a new space in rooms.