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

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

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

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

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 presents a modular in-pipeline climbing robot with a novel compliant foldable OmniCrawler mechanism. The robot has a series of 3 compliant foldable OmniCrawler modules interconnected by links via passive joints. The circular cross-section of the module enables a holonomic motion to facilitate the alignment of the robot in the direction of bends. Additionally, the crawler mechanism provides a fair amount of traction, even on slippery pipe surfaces. These advantages of crawler modules have been further augmented by incorporating active compliance in the module, which helps to negotiate sharp bends in small diameter pipes. Introducing compliance in the crawler module with a single chain-lugs assembly is the the key novelty of this design. For the desirable pipe diameter and curvature of the bends, the spring stiffness value for each passive joint is determined by formulating a constrained optimization problem using the quasi-static model of the robot. Moreover, a minimum friction coefficient value between the module-pipe surface which can be vertically climbed by the robot without slipping is estimated. The numerical simulation results have further been validated by experiments on real robot prototype.

Abstract

We present PRVO, a probabilistic variant of Reciprocal Velocity Obstacle (RVO) for decentralized multi-robot navigation under uncertainty. PRVO characterizes the space of velocities that would allow each robot to fulfill its share in collision avoidance with a specified probability. PRVO is modeled as chance constraints over the velocity level constraints defined by RVO and takes into account the uncertainty associated with both state estimation as well as the actuation of each robot. Since chance constraints are in general computationally intractable, we propose a series of reformulations which when combined with time scaling based concepts leads to a closed form characterization of solution space of PRVO for a given probability of collision avoidance. We validate our formulation through numerical simulations in which we highlight the advantages of PRVO over the related existing formulations

Abstract

Present visual servoing approaches are instance specific i.e. they control camera motion between two views of the same object. However, in practical scenarios where a robot is required to handle various instances of a category, classical visual servoing techniques are less suitable. We formulate across instance visual servoing as a pose induction and pose alignment problem. Initially, the desired pose given for any known instance is transferred to the novel instance through pose induction. Then the pose alignment problem is solved by estimating the current pose using the part aware keypoints reconstruction followed by a pose based visual servoing (PBVS) iteration. To tackle large variation in appearance across object instances in a category, we employ visual features that uniquely correspond to locations of object's parts in images. These part-aware keypoints are learned from annotated images using a convolutional neural network (CNN). Advantages of using such part-aware semantics are two-fold. Firstly, it conceals the illumination and textural variations from the visual servoing algorithm. Secondly, semantic keypoints enables us to match descriptors across instances accurately. We validate the efficacy of our approach through experiments in simulation as well as on a quadcopter. Our approach results in acceptable desired camera pose and smooth velocity profile. We also show results for large camera transformations with no overlap between current and desired pose for 3D objects, which is desirable in servoing context.

Abstract

This paper presents a novel design of an Omnidirectional bendable Omnicrawler module-CObRaSO. Along with the longitudinal crawling and sideways rolling motion, the performance of the OmniCrawler is further enhanced by the introduction of Omnidirectional bending within the module, which is the key contribution of this paper. The Omnidirectional bending is achieved by an arrangement of two independent 1-DOF joints aligned at 90? wrt each other. The unique characteristic of this module is its ability to crawl in Omnidirectionally bent configuration which is achieved by a novel design of a 2-DOF roller chain and a backbone of a hybrid structure of a soft-rigid material. This hybrid structure provides compliant pathways for the lug-chain assembly to passively conform with the orientation of the module and crawl in Omnidirectional bent configuration, which makes this module one of its kind. Furthermore, we show that the unique modular design of CObRaSO unveils its versatility by achieving active compliance on an uneven surface, demonstrating its applications in different robotic platforms (an in-pipeline robot, Quadruped and snake robot) and exhibiting hybrid locomotion modes in various configurations of the robots. The mechanism and mobility characteristics of the proposed module have been verified with the aid of simulations and experiments on real robot prototype.

Abstract

We study the binary Ehrenfeucht Mycielski sequence seeking a balance between the number of occurrences of different binary strings. There have been numerous attempts to prove the balance conjecture of the sequence, which roughly states that 1 and 0 occur equally often in it. Our contribution is twofold. First, we study weaker forms of the conjecture proved in the past and lay out detailed proofs for many lemmas which were stated without proofs. Secondly, we extend the claim of balance to that of normality and prove a weaker form of simple normality to word length 2.