Abstract

We present an autonomous system for outdoor terrain (road) mapping using a robot equipped with viable low cost sensors (2D laser scanner, cameras, odometry, gyroscope and commercial GPS). The research work on outdoor navigation with known maps has matured well in past few years, but navigation and exploration of roads in an unknown area is still emerging. The presented system is an amalgamation of various computation modules running in parallel and interacting asynchronously with each other through message queues. The goal of the system is for a vehicle to explore and build a road network graph of a given area by autonomously navigating through all the connected roads in the area. The graph nodes contain topographic and semantic properties to allow reconstruction of the road network at a later stage for easier navigation.

Abstract

In this paper, we address the problem of reactive collision avoidance among multiple robots. However we impose the constraint of continuous curvature on the collision avoidance manoeuvre which adds to the complexity of the problem but is necessary for realistic implementation of collision avoidance among multiple non-holonomic robots like tricycles and car-like. Since these non-holonomic robots cannot execute point-turn motion, performing collision avoidance with these robots while maintaining curvature continuity becomes a very challenging problem. Hence the proposed work comes as an enhancement over the existing reactive collision avoidance frameworks which completely neglect the above aspects. We modify the collision cone approach of collision avoidance to the case of multiple robots moving with non-linearly varying velocities. This is done by approximating the vehicle evolution with a smooth function represented as a linear combination of orthogonal basis functions and writing the collision avoidance condition as analytical function with respect to time. As a direct consequence of that, the reactive collision avoidance and goal-reaching could be solved as a constrained optimization problem. The theory is substantiated by extensive simulations for various bench-mark cases.

Abstract

Over the past couple of years, with the development of efficient control algorithms, micro aerial vehicles have come into the picture. In this paper, we consider the problem of creating a map of an indoor environment which provides more information than a 2D map and at the same time is more accurate than the contemporary 3D mapping algorithms. We propose a novel collaborative system, consisting of an unmanned ground vehicle and a micro aerial vehicle, which is used to create augmented 2D maps using the distinct sensing capabilities of these two robots. This system works in a collaborative manner such that the two robots complement each others movement and sensing capabilities.

Abstract

This paper presents a method to capture the orbiting objects using a robotic system mounted on a service satellite. The main objective is to manipulate the robot such that no reaction moment gets transferred to the base satellite. This will avoid use of any attitude controller resulting in fuel savings. Note that the constraints leading to zero reaction moment are nonholonomic, and this makes path planning a complex problem. In this work, first a method based on holonomic distribution of the nonholonomic constraints is discussed. As this method exploits constraints in terms of joint velocities, it does not always ensure successful capture. Next, a method based on task-level constraints, written in terms of end-effector's velocities, has been illustrated. It is shown that the path planned using this method has several singular points. In order to overcome disadvantages of the above two methods a novel approach is proposed which uses holonomic distribution to reach closer to the target and task-level constraints to finally capture the target. Efficacy of the method is shown using a 3-link robot mounted on a service satellite.

Abstract

Single camera mobile robots, wherein the single camera becomes the quintessential sensor for robotic tasks such as localization, mapping and obstacle avoidance are challenging. From such a standpoint, we demonstrate a dense reconstruction as conducted by a navigating robot with a monocular camera. Unlike most other dense reconstruction methods this approach first identifies planar areas through homography. These segments are tracked over multiple views with homography based dense correspondences. The tracked correspondences are reconstructed within a VSLAM formulation, wherein the dense reconstructed points get added to the existing SLAM computed structure. The dense structure is further refined using a modified Bundle Adjustment which minimizes projection error in 3D to align with a inferred model of the scene. A mobile robot can thus make use of the reconstructed ground plane and planar obstacles to compute a collision free trajectory.

Abstract

In this paper we propose a semi-active robot for climbing steep obstacles like steps, curbs, etc. The key novelty of the proposed robot lies in the use of a passive mechanism for climbing steps of smaller heights and motor only while climbing steps of bigger heights. Analysis of the robot's stability during its ascent phase is also investigated. Model based control is used to achieve step climbing. The other novelty of the robot, in contrast to existing active suspension step climbers, is that it does not need the knowledge of step height beforehand. Therefore, the mechanism has the advantage of height-independent climbing motion as in the case of passive mechanism along with the extra freedom of active joints for maintaining vehicle stability, only when required. Efficacy of the mechanism is exhibited through simulations on steps of various heights.

Abstract

In this paper we develop an algorithm to generate gait sequences to negotiate a discontinuous terrain for a hybrid 4-wheeled legged robot. The gait sequence comprises two main steps - normal force redistribution and hybrid position-force control. The robot climbs the discontinuity one leg at a time. This requires that the entire load of the robot is taken up by the other three legs so that the leg climbing the discontinuity is free. For this purpose a load redistribution methodology is used which makes the center of gravity of chassis coincide with the desired center of pressure (CoP). Subsequently the free leg moves in hybrid position and force control to climb the discontinuity. Force sensing ensures constant contact with the terrain and detection of stand and end of the discontinuity without using any perception sensor. The methodology is validated using multi-body dynamic simulation.

Abstract

Modern research in India on dams has entered a distinct phase subsequent to the damage to Koyna dam, located in hitherto considered stable continental part, due to an earthquake on 11th December, 1967 in Koyna district, India. Out of about 5,100 large dams in India, a significant number are in moderately high and high seismic zones. As such they may be prone to probable instability to the dam’s structure due to earthquake resultant impacts. Even though none of the dams other than Koyna got damaged in India due to high magnitude earthquake activity, since 1967, it is felt necessary to understand the behaviour of dams due to water load coupled with dynamics of faults that are present in the vicinity of the dam, as dam failures due to earthquake activity are reported from different parts of the world. In this paper, damage to Koyna dam due to a fault motion caused nearby has been studied. A concrete gravity dam is selected from the National Importance Dams of India. Structure-1(S1) as a concrete gravity dam with foundation and structure-2 (S2) as a concrete gravity dam with foundation and soil strata are numerically modeled using Applied Element Method (AEM). Displacement at the base of soil strata is given to generate reverse fault effect on the dam. Behavior of dam is studied by direct and sub-structure methods. In the direct method, the effect of fault motion on dam is studied in S2, by modeling dam at different locations on foot wall and hanging wall. In sub-structure method, free-field accelerations are recorded at different locations on foot wall and hanging wall while only the sub-structure is subjected to fault motion. The recorded accelerations on the surface are used as input to analyze super structure S1. Comparison on behavior of dam in models S1 and S2 is later drawn.

Abstract

This paper proposes a novel method of integrating planning with Monocular Simultaneous Localization and Mapping (SLAM) systems. Monocular SLAM, typically referred to as VSLAM systems in literature consists of recovering trajectory estimates of the camera and stationary world features from a single moving camera. Such VSLAM systems are significantly more difficult than SLAM performed with depth sensors, such as using an accurate Laser Range Finder (LRF). When the camera motion is subject to steep changes in orientation, tracked features over the previous instances are lost, making VSLAM estimates highly unreliable, erroneous that cannot be recovered. Most often a complete breakdown occurs, which entails a new sequence of images to be captured from a fresh camera trajectory. Herein we propose an optimization based path planning formulation for such VSLAM systems that reduces occurence of such errors through paths that are not subject to high orientation changes. Further we plan a velocity profile over the path that prevents features from getting significantly displaced over successive images, often considered a critical criteria for robust feature tracking. The velocity profile is computed using the novel concept of non linear time scaling proposed in our earlier work. The VSLAM system is also sufficiently innovated to provide for dense mapping over planar segments. The efficacy of the formulation is verified over real experiments on a camera mounted robot.

Abstract

Depth information has been shown to affect identification of visually salient regions in images. In this paper, we investigate the role of depth in saliency detection in the presence of (i) competing saliencies due to appearance,(ii) depth-induced blur and (iii) centre-bias. Having established through experiments that depth continues to be a significant contributor to saliency in the presence of these cues, we propose a 3D-saliency formulation that takes into account structural features of objects in an indoor setting to identify regions at salient depth levels. Computed 3D saliency is used in conjunction with 2D saliency models through non-linear regression using SVM to improve saliency maps. Experiments on benchmark datasets containing depth information show that the proposed fusion of 3D saliency with 2D saliency models results in an average improvement in ROC scores of about 9% over state-of-the-art 2D saliency models.