Category Archives: Robotics

Using deep learning for extracting features from range data

December 14, 2017 09:18 ,

Y. Liao, S. Kodagoda, Y. Wang, L. Shi and Y. Liu, Place Classification With a Graph Regularized Deep Neural Network, IEEE Transactions on Cognitive and Developmental Systems, vol. 9, no. 4, pp. 304-315, DOI: 10.1109/TCDS.2016.2586183.

Place classification is a fundamental ability that a robot should possess to carry out effective human-robot interactions. In recent years, there is a high exploitation of artificial intelligence algorithms in robotics applications. Inspired by the recent successes of deep learning methods, we propose an end-to-end learning approach for the place classification problem. With deep architectures, this methodology automatically discovers features and contributes in general to higher classification accuracies. The pipeline of our approach is composed of three parts. First, we construct multiple layers of laser range data to represent the environment information in different levels of granularity. Second, each layer of data are fed into a deep neural network for classification, where a graph regularization is imposed to the deep architecture for keeping local consistency between adjacent samples. Finally, the predicted labels obtained from all layers are fused based on confidence trees to maximize the overall confidence. Experimental results validate the effectiveness of our end-to-end place classification framework in which both the multilayer structure and the graph regularization promote the classification performance. Furthermore, results show that the features automatically learned from the raw input range data can achieve competitive results to the features constructed based on statistical and geometrical information.

Posted in: Mobile robot mapping , Tagged: ,

A new spatial transformation for planning mobile robot trajectories that guarantees a solution and also time requirement satisfaction

December 6, 2017 09:04 ,

S. G. Loizou, The Navigation Transformation, IEEE Transactions on Robotics, vol. 33, no. 6, pp. 1516-1523, DOI: 10.1109/TRO.2017.2725323.

This work introduces a novel approach to the solution of the navigation problem by mapping an obstacle-cluttered environment to a trivial domain called the point world, where the navigation task is reduced to connecting the images of the initial and destination configurations by a straight line. Due to this effect, the underlying transformation is termed the “navigation transformation.” The properties of the navigation transformation are studied in this work as well as its capability to provide-through the proposed feedback controller designs-solutions to the motion- and path-planning problems. Notably, the proposed approach enables the construction of temporal stabilization controllers as detailed herein, which provide a time abstraction to the navigation problem. The proposed solutions are correct by construction and, given a diffeomorphism from the workspace to a sphere world, tuning free. A candidate construction for the navigation transformation on sphere worlds is proposed. The provided theoretical results are backed by analytical proofs. The efficiency, robustness, and applicability of the proposed solutions are supported by a series of experimental case studies.

Posted in: Robot motion planning , Tagged: ,

A survey in interactive perception in robots: interacting with the environment to improve perception and using internal models and prediction too

December 6, 2017 08:54 ,

J. Bohg et al, Interactive Perception: Leveraging Action in Perception and Perception in Action, IEEE Transactions on Robotics, vol. 33, no. 6, pp. 1273-1291, DOI: 10.1109/TRO.2017.2721939.

Recent approaches in robot perception follow the insight that perception is facilitated by interaction with the environment. These approaches are subsumed under the term Interactive Perception (IP). This view of perception provides the following benefits. First, interaction with the environment creates a rich sensory signal that would otherwise not be present. Second, knowledge of the regularity in the combined space of sensory data and action parameters facilitates the prediction and interpretation of the sensory signal. In this survey, we postulate this as a principle for robot perception and collect evidence in its support by analyzing and categorizing existing work in this area. We also provide an overview of the most important applications of IP. We close this survey by discussing remaining open questions. With this survey, we hope to help define the field of Interactive Perception and to provide a valuable resource for future research.

Posted in: Psycho-physiological bases of engineering, Robotics , Tagged: ,

Experimental comparison of methods for merging line segments in line-segment-based maps for mobile robots

December 1, 2017 08:50 ,

Francesco Amigoni, Alberto Quattrini Li, Comparing methods for merging redundant line segments in maps, Robotics and Autonomous Systems, Volume 99, 2018, Pages 135-147, DOI: 10.1016/j.robot.2017.10.016.

Map building of indoor environments is considered a basic building block for autonomous mobile robots, enabling, among others, self-localization and efficient path planning. While the mainstream approach stores maps as occupancy grids of regular cells, some works have advocated for the use of maps composed of line segments to represent the boundary of obstacles, leveraging on their more compact size. In order to limit both the growth of the corresponding data structures and the effort in processing these maps, a number of methods have been proposed for merging together redundant line segments that represent the same portion of the environment. In this paper, we experimentally compare some of the most significant methods for merging line segments in maps by applying them to publicly available data sets. At the end, we propose some guidelines to choose the appropriate method.

Posted in: Mobile robot mapping , Tagged: ,

Layered learning: how to learn hierarchically more complex behaviors based on simpler ones, applied to robot soccer

November 14, 2017 09:13 ,

Patrick MacAlpine, Peter Stone, Overlapping layered learning, Artificial Intelligence, Volume 254, 2018, Pages 21-43, DOI: 10.1016/j.artint.2017.09.001.

Layered learning is a hierarchical machine learning paradigm that enables learning of complex behaviors by incrementally learning a series of sub-behaviors. A key feature of layered learning is that higher layers directly depend on the learned lower layers. In its original formulation, lower layers were frozen prior to learning higher layers. This article considers a major extension to the paradigm that allows learning certain behaviors independently, and then later stitching them together by learning at the “seams” where their influences overlap. The UT Austin Villa 2014 RoboCup 3D simulation team, using such overlapping layered learning, learned a total of 19 layered behaviors for a simulated soccer-playing robot, organized both in series and in parallel. To the best of our knowledge this is more than three times the number of layered behaviors in any prior layered learning system. Furthermore, the complete learning process is repeated on four additional robot body types, showcasing its generality as a paradigm for efficient behavior learning. The resulting team won the RoboCup 2014 championship with an undefeated record, scoring 52 goals and conceding none. This article includes a detailed experimental analysis of the team’s performance and the overlapping layered learning approach that led to its success.

Posted in: Developmental robotics , Tagged: , ,

Qualitative maps for mobile robots

October 30, 2017 08:59 ,

Jennifer Padgett, Mark Campbell, Probabilistic qualitative mapping for robots, Robotics and Autonomous Systems, Volume 98, 2017, Pages 292-306, DOI: 10.1016/j.robot.2017.09.013.

A probabilistic qualitative relational mapping (PQRM) algorithm is developed to enable robots to robustly map environments using noisy sensor measurements. Qualitative state representations provide soft, relative map information which is robust to metrical errors. In this paper, probabilistic distributions over qualitative states are derived and an algorithm to update the map recursively is developed. Maps are evaluated for convergence and correctness in Monte Carlo simulations. Validation tests are conducted on the New College dataset to evaluate map performance in realistic environments.

Posted in: Mobile robot mapping, Mobile robot SLAM , Tagged: ,

The security problems of ROS

October 30, 2017 08:55 ,

Bernhard Dieber, Benjamin Breiling, Sebastian Taurer, Severin Kacianka, Stefan Rass, Peter Schartner, Security for the Robot Operating System, Robotics and Autonomous Systems,
Volume 98, 2017, Pages 192-203, DOI: 10.1016/j.robot.2017.09.017.

Future robotic systems will be situated in highly networked environments where they communicate with industrial control systems, cloud services or other systems at remote locations. In this trend of strong digitization of industrial systems (also sometimes referred to as Industry 4.0), cyber attacks are an increasing threat to the integrity of the robotic systems at the core of this new development. It is expected, that the Robot Operating System (ROS) will play an important role in robotics outside of pure research-oriented scenarios. ROS however has significant security issues which need to be addressed before such products should reach mass markets. In this paper we present the most common vulnerabilities of ROS, attack vectors to exploit those and several approaches to secure ROS and similar systems. We show how to secure ROS on an application level and describe a solution which is integrated directly into the ROS core. Our proposed solution has been implemented and tested with recent versions of ROS, and adds security to all communication channels without being invasive to the system kernel itself.

Posted in: Computer science, Robotic software , Tagged:

Interesting survey on Visual SLAM without filtering and of its future lines of research

October 30, 2017 08:50 ,

Georges Younes, Daniel Asmar, Elie Shammas, John Zelek, Keyframe-based monocular SLAM: design, survey, and future directions, Robotics and Autonomous Systems, Volume 98, 2017, Pages 67-88, DOI: 10.1016/j.robot.2017.09.010.

Extensive research in the field of monocular SLAM for the past fifteen years has yielded workable systems that found their way into various applications in robotics and augmented reality. Although filter-based monocular SLAM systems were common at some time, the more efficient keyframe-based solutions are becoming the de facto methodology for building a monocular SLAM system. The objective of this paper is threefold: first, the paper serves as a guideline for people seeking to design their own monocular SLAM according to specific environmental constraints. Second, it presents a survey that covers the various keyframe-based monocular SLAM systems in the literature, detailing the components of their implementation, and critically assessing the specific strategies made in each proposed solution. Third, the paper provides insight into the direction of future research in this field, to address the major limitations still facing monocular SLAM; namely, in the issues of illumination changes, initialization, highly dynamic motion, poorly textured scenes, repetitive textures, map maintenance, and failure recovery.

Posted in: Computer vision, Mobile robot SLAM , Tagged: ,

A theoretical framework based on hybrid models and logical verification to prove the guarantees for obstacle avoidance in mobile robot navigation

October 28, 2017 08:40 ,

Stefan Mitsch, Khalil Ghorbal, David Vogelbacher, and André Platzer, Formal verification of obstacle avoidance and navigation of ground robots, The International Journal of Robotics Research Vol 36, Issue 12, pp. 1312 – 1340, DOI: 0.1177/0278364917733549.

This article answers fundamental safety questions for ground robot navigation: under which circumstances does which control decision make a ground robot safely avoid obstacles? Unsurprisingly, the answer depends on the exact formulation of the safety objective, as well as the physical capabilities and limitations of the robot and the obstacles. Because uncertainties about the exact future behavior of a robot’s environment make this a challenging problem, we formally verify corresponding controllers and provide rigorous safety proofs justifying why the robots can never collide with the obstacle in the respective physical model. To account for ground robots in which different physical phenomena are important, we analyze a series of increasingly strong properties of controllers for increasingly rich dynamics and identify the impact that the additional model parameters have on the required safety margins. We analyze and formally verify: (i) static safety, which ensures that no collisions can happen with stationary obstacles; (ii) passive safety, which ensures that no collisions can happen with stationary or moving obstacles while the robot moves; (iii) the stronger passive-friendly safety, in which the robot further maintains sufficient maneuvering distance for obstacles to avoid collision as well; and (iv) passive orientation safety, which allows for imperfect sensor coverage of the robot, i.e., the robot is aware that not everything in its environment will be visible. We formally prove that safety can be guaranteed despite sensor uncertainty and actuator perturbation. We complement these provably correct safety properties with liveness properties: we prove that provably safe motion is flexible enough to let the robot navigate waypoints and pass intersections. To account for the mixed influence of discrete control decisions and the continuous physical motion of the ground robot, we develop corresponding hybrid system models and use differential dynamic logic theorem-proving techniques to formally verify their correctness. Since these models identify a broad range of conditions under which control decisions are provably safe, our results apply to any control algorithm for ground robots with the same dynamics. As a demonstration, we also synthesize provably correct runtime monitor conditions that check the compliance of any control algorithm with the verified control decisions.

Posted in: Robot motion planning , Tagged: , ,

First end-to-end implementation of (monocular) Visual Odometry with deep neural networks, including output with the uncertainty of the result

October 25, 2017 09:05 ,

Sen Wang, Ronald Clark, Hongkai Wen, and Niki Trigoni, End-to-end, sequence-to-sequence probabilistic visual odometry through deep neural networks, The International Journal of Robotics Research Vol 37, Issue 4-5, pp. 513 – 542, DOI: 0.1177/0278364917734298.

This paper studies visual odometry (VO) from the perspective of deep learning. After tremendous efforts in the robotics and computer vision communities over the past few decades, state-of-the-art VO algorithms have demonstrated incredible performance. However, since the VO problem is typically formulated as a pure geometric problem, one of the key features still missing from current VO systems is the capability to automatically gain knowledge and improve performance through learning. In this paper, we investigate whether deep neural networks can be effective and beneficial to the VO problem. An end-to-end, sequence-to-sequence probabilistic visual odometry (ESP-VO) framework is proposed for the monocular VO based on deep recurrent convolutional neural networks. It is trained and deployed in an end-to-end manner, that is, directly inferring poses and uncertainties from a sequence of raw images (video) without adopting any modules from the conventional VO pipeline. It can not only automatically learn effective feature representation encapsulating geometric information through convolutional neural networks, but also implicitly model sequential dynamics and relation for VO using deep recurrent neural networks. Uncertainty is also derived along with the VO estimation without introducing much extra computation. Extensive experiments on several datasets representing driving, flying and walking scenarios show competitive performance of the proposed ESP-VO to the state-of-the-art methods, demonstrating a promising potential of the deep learning technique for VO and verifying that it can be a viable complement to current VO systems.

Posted in: Computer vision, Mobile Robot Localization , Tagged: ,