Abstract

In this paper, we automate the traditional problem of Simultaneous Localization and Mapping (SLAM) by interleaving planning for exploring unknown environments by a mobile robot. We denote such planned SLAM systems as SPLAM (Simultaneous Planning Localization and Mapping). The main aim of SPLAM is to plan paths for the SLAM process such that the robot and map uncertainty upon execution of the path remains minimum and tractable. The planning is interleaved with SLAM and hence the terminology SPLAM. While typical SPLAM routines find paths when the robot traverses amidst known regions of the constructed map, herein we use the SPLAM formulation for an exploration like situation. Exploration is carried out through a frontier based approach where we identify multiple frontiers in the known map. Using Randomized Planning techniques we calculate various possible trajectories to all the known frontiers. We introduce a novel strategy for selecting frontiers which mimics Fast SLAM, selects a trajectory for robot motion that will minimize the map and robot state covariance. By using a Fast SLAM like approach for selecting frontiers we are able to decouple the robot and landmark covariance resulting in a faster selection of next best location, while maintaining the same kind of robustness of an EKF based SPLAM framework. We then compare our results with Shortest Path Algorithm and EKF based Planning. We show significant reduction in covariance when compared with shortest frontier first approach, while the uncertainties are comparable to EKF-SPLAM albeit at much faster planning times.

Abstract

Exploration is a core and important robotics area, whose applications include search and rescue robotics, planetary exploration etc. We know that this exploration task is best performed when using a multi-robot system. In this paper, we present an algorithm for multi-robot exploration of an unknown environment, taking into account the communication constraints between the robots. The aim of the robots is to explore the whole map as a pack, without losing communication throughout. The key task for us here is to allocate the target points for multiple robots so as to maximize the area explored and minimize the time and plan paths for the robots in such a way so as to avoid obstacles. A multi-robot exploration methodology is introduced similar to depth first strategy, that samples frontier points based on a metric function. This function aims to maximize the visibility gain or information gain while minimizing the distance to be travelled to the frontier points, such that the robots are within the limited communication distance of each other. The algorithm has been tested through simulation runs of various maps and results and evaluations have been presented based on it. The results effectively demonstrate that our algorithm allows robot pack to quickly accomplish the task of exploration and without the constraint ever breaking down. Here, we also present a comparative analysis of our algorithm with another exploration approach, which finds new areas based on population generation and utility calculation over the population. The results show tangible performance gain of this method over previous methods reported on exploration with limited communication constraints.

Abstract

In this paper we address the problem of detecting road intersections. We present two approaches to solve the problem of intersection detection in an unstructured outdoor setting. The first is a natural extension of the popular VFH* obstacle avoidance algorithm. It detects intersections and tracks, over a period of time, the angles at which gaps in the robot's certainty grid (CG) are first observed. The second approach uses techniques from image processing and computational geometry on the certainty grid image, to extract a skeleton of the navigable region, thus providing the intersections. We show experimental results portraying intersection detection due to both methods and show the results. On the whole, we found that the robot was able to detect all possible intersections.

Abstract

In this paper we introduce a novel framework of generating trajectories which explicitly satisfies the stability constraints such as no-slip and permanent ground contact on uneven terrain. The main contributions of this paper are: (1) It derives analytical functions depicting the evolution of the vehicle on uneven terrain. These functional descriptions enable us to have a fast evaluation of possible vehicle stability along various directions on the terrain and this information is used to control the shape of the trajectory. (2) It introduces a novel paradigm wherein non-linear time scaling brought about by parametrized exponential functions are used to modify the velocity and acceleration profile of the vehicle so that these satisfy the no-slip and contact constraints. We show that nonlinear time scaling manipulates velocity and acceleration profile in a versatile manner and consequently has exceptional utility not only in uneven terrain navigation but also in general in any problem where it is required to change the velocity of the robot while keeping the path unchanged like collision avoidance.

Abstract

This paper presents a Field Programmable Gate Array (FPGA) based implementation of Acceleration Velocity Obstacle based Collision Avoidance for an omni-directional robot with acceleration constraint. Specifically a parallel architecture for collision avoidance is proposed that portrays the advantages of FPGA implementation over the sequential implementation for same processor or clock speed. FPGA based robotics is seen to gain popularity due to low cost, portability, seamless interface to hardware and most importantly due to inherent parallelism enshrined in various robotic algorithms. FPGA realization of the algorithm in a simulation test bed vindicates its efficacy and comparison with sequential implementation is also highlighted. The paper proposes three different architectures for the implementation of the proposed algorithm viz. sequential architecture; a resource constrained pipelined architecture and a hybrid pipeline parallel architecture. The performances of those three architectures have been evaluated.

Abstract

This paper proposes a robust approach for image based floor detection and segmentation from sequence of images or video. In contrast to many previous approaches, which uses a priori knowledge of the surroundings, our method uses combination of modified sparse optical flow and planar homography for ground plane detection which is then combined with graph based segmentation for extraction of floor from images. We also propose a probabilistic framework which makes our method adaptive to the changes in the surroundings. We tested our algorithm on several common indoor environment scenarios and were able to extract floor even under challenging circumstances. We obtained extremely satisfactory results in various practical scenarios such as where the floor and non floor areas are of same color, in presence of textured flooring, and where illumination changes are steep.

Abstract

We present a probabilistic method of finding the next best viewpoint that maximizes the chances of finding an object in a known environment for an indoor mobile robot. We make use of the information that is available to a robot in the form of potential locations to search for an object. Extraction of these potential locations and their representation for exploration is explained. This work primarily focuses on placing the robot at its best location in the environment to detect, recognize an object and hence do object search. With experiments done on the exploration, object recognition individually we show the robustness of this approach for object search task. We analyse and compare our method with two other strategies for localizing the object empirically and show unequivocally that the strategy based on the probabilistic formalism in general performs better than the other two.

Abstract

A semiconductor ring laser is designed so as to work as an inverted integrator coupling three of such integrators an all-optical Delta Sigma Modulator is designed. The phenomenon of injection locking and switching is used.

Abstract

On the run towards making a photonic processor, which uses the photons of visible light rather than electric current to perform digital computations we propose all optical digital logic gate library consisting of NOR, OR, AND, XOR, and two compound gates. These gates which are designed using semiconductor ring lasers (SRL) are analyzed and modeled by developing rate equations. This digital library can be used in any CAD tool developed to design integrated photonics components. The phenomenon of injection locking is used for the realization of logic gates.

Abstract

espite the advancement in the information extraction area, the task of identifying associated relations among named entities within a text document remains a significant challenge. Existing automated approaches lack human precision and they also struggle to handle erroneous documents. In this paper, we propose a crowdsourcing-based approach to improve the accuracy of the generated relations from the existing extraction techniques. Our idea is to gather judgments on the extracted relations of an article from the interested users. By contributing, the users in return remember the facts related to a document. This paper presents the complete design of the approach along with a user study done with twelve participants. Results show that the users rated the proposed system positively and were willing to contribute their time and energy for the task.