Abstract

In this paper we consider the problem of servoing across different instances of an object category, in which given any exemplar from an object category the robot is required to attain a desired pose. The problem becomes relevant in practical scenarios where robots are entailed to handle a wide range of objects. The challenge here is to address the large intra-category variation in the shape of object instances. We propose a two-phase graph based visual servoing (GBVS) framework for instance invariant visual servoing. The first offline phase consists of constructing a dense graph from a large dataset of images of numerous object instances viewed under various camera poses. The vertices in the graph are images themselves and the edges represent visual servoing trajectory length predicted by our metric learning framework. The second online step requires computation of the shortest path and navigation over it through a succession of image based visual servoing (IBVS) manoeuvres. By considering cup as running example to represent an object category we validate the our approach qualitatively on images downloaded from Internet and quantitatively in terms of camera pose error on synthetic images. We report translation and rotation errors under 11% and 13% respectively.

Abstract

In this paper, we present an algorithm for navigating multiple robots under perception and ego-motion uncertainty. Our approach is based on the concept of the Reciprocal Velocity Obstacle which defines a set of constraints for characterizing the space of collision avoidance velocities available to each robot at a given instant in a multi-robot setting. We present a probabilistic variant of RVO obtained by defining chance constraints over the deterministic RVO constraints. Since chance constraints are in general computationally intractable, we present a family of surrogate constraints that can be used as a substitution for the original chance constraints. We show that satisfaction of surrogate constraints ensures satisfaction of original chance constraints with a specific low bound probability. We validate our formulations through numerical simulations in which we highlight the advantages of the proposed formulation over the existing methods, which handle the effect of uncertainty by using conservative bounding volumes.

Abstract

This paper discusses the design of a novel compliant in-pipe climbing modular robot for small diameter pipes. The robot consists of a kinematic chain of 3 OmniCrawler modules with a link connected in between 2 adjacent modules via compliant joints. While the tank-like crawler mechanism provides good traction on low friction surfaces, its circular cross-section makes it holonomic. The holonomic motion assists it to re-align in a direction to avoid obstacles during motion as well as overcome turns with a minimal energy posture. Additionally, the modularity enables it to negotiate T-junction without motion singularity. The compliance is realized using 4 torsion springs incorporated in joints joining 3 modules with 2 links. For a desirable pipe diameter (textØ 75mm), the springs' stiffness values are obtained by formulating a constraint optimization problem which has been simulated in ADAMS MSC and further validated on a real robot prototype. In order to negotiate smooth vertical bends and friction coefficient variations in pipes, the design was later modified by replacing springs with series elastic actuators (SEA) at 2 of the 4 joints.

Abstract

We propose a novel pipeline for detecting, localizing, and recognizing trees with a quadcoptor equipped with monocular camera. The quadcoptor flies in an area of semidense plantation filled with many trees of more than 5 meter in height. Trees are detected on a per frame basis using state of the art Convolutional Neural Networks inspired by recent rapid advancements showcased in Deep Learning literature. Once detected, the trees are tagged with a GPS coordinate through our global localizing and positioning framework. Further the localized trees are segmented, characterized by feature descriptors, and stored in a database by their GPS coordinates. In a subsequent run in the same area, the trees that get detected are queried to the database and get associated with the trees in the database. The association problem is posed as a dynamic programming problem and the optimal association is inferred. The algorithm has been verified in various zones in our campus infested with trees with varying density on the Bebop 2 drone equipped with omnidirectional vision. High percentage of successful recognition and association of the trees between two or more runs is the cornerstone of this effort. The proposed method is also able to identify if trees are missing from their expected GPS tagged locations thereby making it possible to immediately alert concerned authorities about possible unlawful felling of trees. We also propose a novel way of obtaining dense disparity map for quadcopter with monocular camera.

Abstract

At the cross section of the fields of Uneven Terrain Navigation and Multi Agent Systems (MAS), in this work, a Detachable Compliant Modular Robot (DCMR) which can perform concurrent scene exploration by detaching into numerous parts, while preserving its ability to climb stairs is proposed and built. A spring is designed and used in the modular robot taking the worst-case-scenario of stairs encountered in an urban setting. In addition to the actuators at the wheels, an additional set of actuators per module are introduced to enable the detachment and re-attachment. The design additions and their trade-offs are discussed. Potential applications are presented with special focus on improving coverage of a map with obstacles/slabs large enough to merit exploration by climbing them. The problem of turning in crammed spaces is solved using the ability to detach of DCMR. The detaching & re-attaching capability, and stair climbing of the composite modular robot are demonstrated through experimentation using the prototype

Abstract

Present image based visual servoing approaches rely on extracting hand crafted visual features from an image. Choosing the right set of features is important as it directly affects the performance of any approach. Motivated by recent breakthroughs in performance of data driven methods on recognition and localization tasks, we aim to learn visual feature representations suitable for servoing tasks in unstructured and unknown environments. In this paper, we present an end-to-end learning based approach for visual servoing in diverse scenes where the knowledge of camera parameters and scene geometry is not available a priori. This is achieved by training a convolutional neural network over color images with synchronised camera poses. Through experiments performed in simulation and on a quadrotor, we demonstrate the efficacy and robustness of our approach for a wide range of camera poses in both indoor as well as outdoor environments.

Abstract

We present an approach for reconstructing vehicles from a single (RGB) image, in the context of autonomous driving. Though the problem appears to be ill-posed, we demonstrate that prior knowledge about how 3D shapes of vehicles project to an image can be used to reason about the reverse process, i.e., how shapes (back-)project from 2D to 3D. We encode this knowledge in shape priors, which are learnt over a small keypoint-annotated dataset. We then formulate a shape-aware adjustment problem that uses the learnt shape priors to recover the 3D pose and shape of a query object from an image. For shape representation and inference, we leverage recent successes of Convolutional Neural Networks (CNNs) for the task of object and keypoint localization, and train a novel cascaded fully-convolutional architecture to localize vehicle keypoints in images. The shape-aware adjustment then robustly recovers shape (3D locations of the detected keypoints) while simultaneously filling in occluded keypoints. To tackle estimation errors incurred due to erroneously detected keypoints, we use an Iteratively Re-weighted Least Squares (IRLS) scheme for robust optimization, and as a by-product characterize noise models for each predicted keypoint. We evaluate our approach on autonomous driving benchmarks, and present superior results to existing monocular, as well as stereo approaches.

Abstract

With the advent of autonomous vehicles, LiDAR and cameras have become an indispensable combination of sensors. They both provide rich and complementary data which can be used by various algorithms and machine learning to sense and make vital inferences about the surroundings. We propose a novel pipeline and experimental setup to find accurate rigid-body transformation for extrinsically calibrating a LiDAR and a camera. The pipeling uses 3D-3D point correspondences in LiDAR and camera frame and gives a closed form solution. We further show the accuracy of the estimate by fusing point clouds from two stereo cameras which align perfectly with the rotation and translation estimated by our method, confirming the accuracy of our method's estimates both mathematically and visually. Taking our idea of extrinsic LiDAR-camera calibration forward, we demonstrate how two cameras with no overlapping field-of-view can also be calibrated extrinsically using 3D point correspondences. The code has been made available as open-source software in the form of a ROS package, more information about which can be sought here: this https URL.

Abstract

Learning a complex task like robot maneuver while preventing Monocular SLAM failure is challenging for both robots and humans. We devise a computational model for representing and inferring strategies for this task, formulated as a Markov Decision Process (MDP). We show how the reward function can be learned using Inverse Reinforcement Learning. The resulting framework allows us to understand how chosen parameters affect the quality of Monocular SLAM. A significant improvement in performance as compared to other state-of-the-art methods is also shown.

Abstract

This paper presents a novel and generic reactionless visual servo controller for a satellite-based multi-arm space robot. The controller is designed to complete the task of visually servoing the robot’s end-effectors to a desired pose, while maintaining minimum attitude disturbance on the base-satellite. Task function approach is utilized to coordinate the servoing process and attitude of the base satellite. A redundancy formulation is used to define the tasks. The visual serving 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 defined through a quadratic optimization problem, in such a way that it does not affect the primary task, and simultaneously minimizes its cost function. Stability analysis of the proposed control methodology is also discussed. A set of numerical experiments are carried out on different multi-arm space robotic systems. These systems are a planar dual-arm robot, a spatial dual-arm robot, and a three-arm planar robot. The results of the simulation experiments show efficacy, generality and applicability of the proposed control methodology.