Abstract

This paper presents the design of an in-pipe climbing robot that operates using a novel ‘Three-output open differential’(3-OOD) mechanism to traverse complex networks of pipes. Conventional wheeled/tracked in-pipe climbing robots are prone to slip and drag while traversing in pipe bends. The 3-OOD mechanism helps in achieving the novel result of eliminating slip and drag in the robot tracks during motion. The proposed differential realizes the functional abilities of the traditional two-output differential, which is achieved the first time for a differential with three outputs. The 3-OOD mechanism mechanically modulates the track speeds of the robot based on the forces exerted on each track inside the pipe network, by eliminating the need for any active control. The simulation of the robot traversing in the pipe network in different orientations and in pipe-bends without slip shows the proposed design’s effectiveness.

Abstract

Human-robot interaction, particularly in wheeled mobile robots that can autonomously assist humans to traverse dynamically changing environments is a field of active research. Integrated motion planning and obstacle-avoidance pose a considerable challenge for an autonomous person-following robot (PFR). And, scenarios with intersections and occlusions along the path only increase the complexity in sustained tracking. In this paper, we use model predictive control (MPC) with earlyrelocation (ER) strategy to formulate a prioritized tracking scheme and implement it for a differential-drive system. Our approach ensures that the target person stays within the field of view (FOV) of the PFR consistently, even while it maneuvers intersections or crowded spots, by adding new locations to its updated path. As trajectory generation in such cases must be incremental to accommodate new information, the use of efficient representations is key. To that end, we build this social representation of following a person directly into the controller itself. MPC can naturally handle such state and input limitations as constraints to solve an on-line optimization at each time step. A non-linear MPC with ER is thus devised and tested with increasing levels of complexity arising from occlusions due to the map and its dynamic actors. By using 2D simulations, we show that for slow and medium walking speeds of the target person, the controller can plan maneuvers with an adequate margin of over 20 Hz apt for achieving a near real-time personfollowing behaviour.

Abstract

Given a monocular color image of a warehouse rack, we aim to predict the bird's-eye view layout for each shelf in the rack, which we term as' multi-layer'layout prediction. To this end, we present RackLay, a deep neural network for real-time shelf layout estimation from a single image. Unlike previous layout estimation methods which provide a single layout for the dominant ground plane alone, RackLay estimates the top-viewand front-view layout for each shelf in the considered rack populated with objects. RackLay's architecture and its variants are versatile and estimate accurate layouts for diverse scenes characterized by varying number of visible shelves in an image, large range in shelf occupancy factor and varied background clutter. Given the extreme paucity of datasets in this space and the difficulty involved in acquiring real data from warehouses, we additionally release a flexible synthetic dataset generation …

Abstract

Surface normal estimation is an essential component of several computer and robot vision pipelines. While this problem has been extensively studied, most approaches are geared towards indoor scenes and often rely on multiple modalities (depth, multiple views) for accurate estimation of normal maps. Outdoor scenes pose a greater challenge as they exhibit significant lighting variation, often contain occluders, and structures like building facades are often ridden with numerous windows and protrusions. Conventional supervised learning schemes excel in indoor scenes, but do not exhibit competitive performance when trained and deployed in outdoor environments. Furthermore, they involve complex network architectures and require many more trainable parameters. To tackle these challenges, we present an adversarial learning scheme that regularizes the output normal maps from a neural network to appear more realistic, by using a small number of precisely annotated examples. Our method presents a lightweight and simpler architecture, while improving performance by at least 1.5x across most metrics. We evaluate our approaches against the state-of-the-art on normal map estimation, on a synthetic and a real outdoor dataset, and observe significant performance enhancements.

Abstract

It is critical for aerial robots flying in city scale urban environments to make very quick estimates of a building depth with respect to itself. It should be done in a matter of few views to navigate itself, avoiding collisions with such a towering structure. As such, no one has attacked this problem. We bring together several modules combining deep learning and 3D vision to showcase a quick reconstruction in a few views. We exploit the inherent planar structure in the buildings (facades, windows) for this purpose. We evaluate the efficacy of our pipeline with various constraints and errors from multi-view geometry using ablation studies. We then retrieve the skyline of the buildings in synthetic as well as real-world scenes.

Abstract

Owing to the inherent geometry, the extent of map reconstructed using line-based SfM(structure from motion) is superior to point-based SfM. However, with the existing methods, estimation of structure and motion from observed 2D line segments in images is more complex than that of points. To overcome this, we propose a simple and robust 1-parameter approach for Structure and Motion Estimation from line features in Manhattan Scenes from three views. We leverage the vanishing point directions to estimate the relative rotations as well as to fix the 3D line direction. In consequence we build a constraints matrix, which has the relative translations and 3D line depth as its null space. We then perform 1-parameter line BA using factor graph based cost function. We compare the efficacy of our method with standard line triangulation in synthetic as well as real-world scenes. Keywords-manhattan scenes, line features, Structure and motion estimation, vanishing points, vanishing line, Plucker coordinates, orthonormal representation, factor graph optimization

Abstract

This paper presents a novel passive three-output differential with three degrees of freedom (3DOF), that translates motion and torque from a single input to three outputs. The proposed ThreeOutput Open Differential is designed such that its functioning is analogous to the functioning of a traditional two-output open differential. That is, the differential translates equal motion and torque to all its three outputs when the outputs are unconstrained or are subjected to equivalent load conditions. The introduced design is the first differential with three outputs to realise this outcome. The differential action between the three outputs is realised passively by a symmetric arrangement of three two-output open differentials and three two-input open differentials. The resulting differential mechanism achieves the novel result of equivalent input to output angular velocity and torque relations for all its three outputs. Furthermore, Three-Output Open Differential achieves the novel result for differentials with more than two outputs where each of its outputs shares equivalent angular velocity and torque relations with all the other outputs. The kinematics and dynamics of the Three-Output Open Differential are derived using the bond graph method. In addition, the merits of the differential mechanism along with its current and potential applications are presented.

Abstract

Control barrier function (CBF) constraints provide a rigorous characterization of the space of control inputs that ensure the satisfaction of state constraints, such as collision avoidance, at all time instants. However, CBFs are highly nonlinear and non-convex and thus, when incorporated within an optimization-based algorithm such as Model Predictive Control (MPC), leads to a computationally challenging problem. Existing works by-pass the computational intractability by collapsing the horizon of the MPC to a single step, although this comes at the cost of severe degradation of performance. In this paper, we present two contributions to ensure the real-time performance of CBFs based MPC over long horizons in the context of multi-robot collision avoidance. First, we propose a customized Project Gradient Descent Method that incurs minimal computational overhead over existing one-step approaches but leads to a substantial improvement in trajectory smoothness, time to reach the goal, etc. Our second contribution lies in applying the proposed MPC to both quadrotors and fixed-wing aerial aircrafts (FWA). In particular, we show that the formulation for the quadrotors can be readily extended to the latter by deriving additional CBFs for the curvature and forward velocity constraints. We validate our algorithm with an extensive simulation of up to 10 robots in challenging benchmark scenarios.

Abstract

Recent data-driven approaches to visual servoing have shown improved performances over classical methods due to precise feature matching and depth estimation. Some recent servoing approaches use a model predictive control (MPC) framework which generalise well to novel environments and are capable of incorporating dynamic constraints, but are computationally intractable in real-time, making it difficult to deploy in real-world scenarios. On the contrary, single-step methods optimise greedily and achieve high servoing rates, but lack the benefits of the MPC multi-step ahead formulation. In this paper, we make the best of both worlds and propose a lightweight visual servoing MPC framework which generates optimal control near real-time at a frequency of 10.52 Hz. This work utilises the differential cross-entropy sampling method for quick and effective control generation along with a lightweight neural network, significantly improving the servoing frequency. We also propose a flow depth normalisation layer which amelio- rates the issue of inferior predictions of two view depth from the flow network. We conduct extensive experimentation on the Habitat simulator and show a notable decrease in servoing time in comparison with other approaches that optimise over a time horizon. We achieve the right balance between time and performance for visual servoing in six degrees of freedom (6DoF), while retaining the advantageous MPC formulation. Our code and dataset are publicly available

Abstract

Exploring shared autonomy in assistive and service robots makes human-aware navigation of a person following robot (PFR) a required behaviour. In this paper, we propose a control framework for a non-holonomic wheeled robot that not only tracks a target person in sight but also anticipates the human movement patterns to predict the future sequence of its path when the person is out of sight. Human beings form a crowd and can exhibit complex random movement compared to vehicles while indoor environments with intersections can pose serious challenges for long-distance path-following without the breakdown of tracking. Thus, a nonlinear model predictive controller is designed with long-term prediction and socially compliant rules for natural person-following behaviour. It can generate the collision-free path and optimized control inputs using a single optimization framework Finally, the integrated navigation-control stack is evaluated using simulations for real-time operation. We also present a hardware configuration for its real-world implementation.