Abstract

In this paper we present two mechanisms of linear force actuator based actively articulated suspension vehicles and propose a strategy to control the wheel–ground contact forces to improve traction and to increase the no-slip margin and hence enhance the mobility of the vehicle on uneven terrain. We present the quasi-static analysis of each of the mechanisms to depict the ability of the systems to control the wheel–ground contact forces while negotiating uneven terrain with the help of feasibility plots. The first model is a vehicle with a 1-dof leg (referred to as LFA-V1) and can climb slopes upto 40 degrees but to further increase the capability of the robot we come with a modified design of the vehicle which has a 2-dof leg (referred to as LFA-V2) and can negotiate slopes with discontinuities greater than twice the wheel diameter.

Abstract

In this paper a novel suspension mechanism for rough terrain mobility is proposed. The proposed mechanism is simpler than the existing suspension mechanism in the sense that the number of links and joints has been significantly reduced without compromising the climbing ability of the rover. We explore the use of compliant elements like springs for passively controlling the degree of freedom of the proposed mechanism and a framework for optimizing the spring parameters has been proposed. A performance evaluation of the proposed mechanism has been shown in terms of extensive simulations.

Abstract

An adaptive partition based Random Forests classifier for outdoor terrain classification is presented in this paper. The classifier is a combination of two underlying classifiers. One of which is a random forest learnt over bootstrapped or offline dataset, the second is another random forest that adapts to changes on the fly. Posterior probabilities of both the static and changing/online classifiers are fused to assign the eventual label for the online image data. The online classifier learns at frequent intervals of time through a sparse and stable set of tracked patches, which makes it lightweight and real-time friendly. The learning which is actuated at frequent intervals during the sojourn significantly improves the performance of the classifier vis-a-vis a scheme that only uses the classifier learnt offline or at bootstrap. The method is well suited and finds immediate applications for outdoor autonomous driving where the classifier needs to be updated frequently based on what shows up recently on the terrain and without largely deviating from those learnt at bootstrapping. The role of the partition based classifier to enhance the performance of a regular multi class classifier such as random forests and multi class SVMs is also summarized in this paper.

Abstract

A vision based exploration algorithm that invokes semantic cues for constructing a hybrid map of images - a combination of semantic and topological maps is presented in this paper. At the top level the map is a graph of semantic constructs. Each node in the graph is a semantic construct or label such as a room or a corridor, the edge represented by a transition region such as a doorway that links the two semantic constructs. Each semantic node embeds within it a topological graph that constitutes the map at the middle level. The topological graph is a set of nodes, each node representing an image of the higher semantic construct. At the low level the topological graph embeds metric values and relations, where each node embeds the pose of the robot from which the image was taken and any two nodes in the graph are related by a transformation consisting of a rotation and translation. The exploration algorithm explores a semantic construct completely before moving or branching onto a new construct. Within each semantic construct it uses a local feature based exploration algorithm that uses a combination of local and global decisions to decide the next best place to move. During the process of exploring a semantic construct it identifies transition regions that serve as gateways to move from that construct to another. The exploration is deemed complete when all transition regions are marked visited. Loop detection happens at transition regions and graph relaxation techniques are used to close loops when detected to obtain a consistent metric embedding of the robot poses. Semantic constructs are labeled using a visual bag of words (VBOW) representation with a probabilistic SVM classifier.

Abstract

A multi-robotic exploration with the requirement of communication link to a fixed base station is presented in this paper. The robots organize themselves into roles of maintainers of communication (hinged robots or robot nodes) or explorers of the environment ensuring that every robot is in contact with the base station directly or through the hinged robots. A two phased strategy for the same is presented. The first phase is characterized by a recursive growth of trees that starts from the root node or the base station and then repeated from other nodes of the hitherto grown tree in a depth first fashion. The second phase constitutes the recursive tree growth invoked repeatedly from the frontier nodes. While the first phase rapidly explores areas around the base station in a concentric fashion, the second phase extends the depth of the explored area to increase the limits of coverage. The strategy is consistent in that none of the robots loose contact with the base station. Extensive simulations confirm the efficacy of the method and comparisons portray performance gain in terms of exploration time and absence of deadlocks vis-a-vis the few methods previously reported in the literature.

Abstract

The task of the robot in localization is to find out where it is, through sensing and motion. In environments which possess relatively few features that enable a robot to unambiguously determine its location, global localization algorithms can result in ‘multiple hypotheses’ locations of a robot. This is inevitable with global localization algorithms, as the local environment seen by a robot repeats at other parts of the map. Thus, for effective localization, the robot has to be actively guided to those locations where there is a maximum chance of eliminating most of the ambiguous states — which is often referred to as ‘active localization’. When extended to multi-robotic scenarios where all robots possess more than one hypothesis of their position, there is an opportunity to do better by using robots, apart from obstacles, as ‘hypotheses resolving agents’. The paper presents a unified framework which accounts for the map structure as well as measurement amongst robots, while guiding a set of robots to locations where they can singularize to a unique state. The strategy shepherds the robots to places where the probability of obtaining a unique hypothesis for a set of multiple robots is a maximum. Another aspect of framework demonstrates the idea of dispatching localized robots to locations where they can assist a maximum of the remaining unlocalized robots to overcome their ambiguity, named as ‘coordinated localization’. The appropriateness of our approach is demonstrated empirically in both simulation & real-time (on Amigo-bots) and its efficacy verified. Extensive comparative analysis portrays the advantage of the current method over others that do not perform active localization in a multi-robotic sense. It also portrays the performance gain by considering map structure and robot placement to actively localize over methods that consider only one of them or neither. Theoretical backing stems from the proven completeness of the method for a large category of diverse environments.

Abstract

The process of scancorrelation is very useful in map building and localization in environments without any easily discernible landmarks. It involves estimating the relative transform between two given scans of data. This can be formulated as a posterior estimation problem over the transformation space. Since the posterior, the action model and the likelihood function are mostly nonlinear, a particle filter is used to estimate this posterior [1]. A GMM based correlation scheme has been used in [2] to drive the scan correlation process. In this paper, we propose a 3 step particle filtering approach that combines the simplicity and efficiency of the particle filter with the robustness of the GMM based correlation scheme. Experimental results are provided to verify the effectiveness of this approach.

Abstract

The Modular Legged robotic system [1] "Mod-Leg" presented here has been bio-inspired from a Snake's vertebrae and a caterpillar's legged structure. The system can be configured to a 4-legged robotic dog, a hexapod, a caterpillar and a Snake robot. This robot's novel design achieves compliance to the terrain using a combination of legs and electronically actuated universal spine. A unique simulator has been designed for this purpose. Some of the things we learned while developing this robotic system have been presented below.

Abstract

Capturing the shape and texture of large structures such as monuments and statues at very high resolution is extremely expensive, both in terms of time as well as storage space. In many cases the finer details are generated by surface properties of the material, and the appearance is statistically uniform. In this paper, we present an approach to add surface details to a coarse 3D model of an object based on two additional information: a set of images of the object and a high resolution model of the material that the object is made of. The material model that we employ is the Polynomial Texture Map (PTM), which captures the appearance of a surface under various illumination conditions. We use the observed images of the object as constraints to synthesize texture samples for each triangle of the object under any given illumination. The primary challenge is to synthesize a polynomial model of the texture, where the constraints arise in the image domain. We use the knowledge of object illumination to map the texture models into image space and compute the optimal patch. The texture transfer then happens as a complete 3D texturemodel. We also consider the problems of pose, scale, reflectance and smoothness of surface while carrying out the texture transfer. We synthesize the texture of an object at a per-triangle basis while carrying out operations such as normalization and blending to take care of discontinuities at the edges.

Abstract

We present a complete online handwritten character recognition system for Indian languages that handles the ambiguities in segmentation as well as recognition of the strokes. The recognition is based on a generative model of handwriting formation, coupled with a discriminative model for classification of strokes. Such an approach can seamlessly integrate language and script information in the generative model and deal with similar strokes using the discriminative stroke classification model. The recognition is performed in a purely bottomup fashion, starting with the strokes, and the ambiguities at each stage are preserved and transferred to the next stage for obtaining the most probable results at each stage. We also present the results of various preprocessing, feature selection and classification studies on a large data set collected from native language writers in two different Indian languages: Malayalam and Telugu. The system achieves a stroke level accuracy of 95.78% and 95.12% on Malayalam and Telugu data, respectively. The akshara level accuracy of the system is around 78% on a corpus of 60, 492 words from 367 writers.