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

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

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

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

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

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

Reconstruction of dynamic objects in a scene is a highly challenging problem in the context of SLAM. In this paper, we present a real-time monocular object localization system that estimates the shape and pose of dynamic objects in real-time, using video frames captured from a moving monocular camera. Although the problem seems to be ill-posed, we demonstrate that, by incorporating prior knowledge of the object category, we can obtain more detailed instance-level reconstructions. As opposed to earlier object model specifications, the proposed shape-prior model leads to the formulation of a Bundle Adjustment-like optimization problem for simultaneous shape and pose estimation. Leveraging recent successes of Convolutional Neural Networks (CNNs) for object keypoint localization, we present a CNN architecture that performs precise keypoint localization. We then demonstrate how these keypoints can be used to recover 3D object properties, while accounting for any 2D localization errors and self-occlusion. We show significant performance improvements compared to state-of-the-art monocular competitors for 2D keypoint detection, as well as 3D localization and reconstruction of dynamic objects.

Abstract

This paper considers the problem of finding pose matches between trajectories of multiple robots in their respective coordinate frames or equivalent matches between trajectories obtained during different sessions. Pose correspondences between trajectories are mediated by common landmarks represented in a topological map lacking distinct metric coordinates. Despite such lack of explicit metric level associations, we mine preliminary pose level correspondences between trajectories through a novel multi-scale heat-kernel descriptor and correspondence graph framework. These serve as an improved initialization for ICP (Iterative Closest Point) to yield dense pose correspondences. We perform extensive analysis of the proposed method under varying levels of pose and landmark noise and showcase its superiority in obtaining pose matches in comparison with standard ICP like methods. To the best of our knowledge, this is the first work of the kind that brings in elements from spectral graph theory to solve the problem of pose correspondences in a multi-robotic setting and differentiates itself from other works.