Category Archives: Robotics

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: ,

Automatic hierarchization for the recognition of places in images

October 19, 2017 09:00 ,

Chen Fan, Zetao Chen, Adam Jacobson, Xiaoping Hu, Michael Milford, Biologically-inspired visual place recognition with adaptive multiple scales,Robotics and Autonomous Systems, Volume 96, 2017, Pages 224-237, DOI: 10.1016/j.robot.2017.07.015.

In this paper we present a novel adaptive multi-scale system for performing visual place recognition. Unlike recent previous multi-scale place recognition systems that use manually pre-fixed scales, we present a system that adaptively selects the spatial scales. This approach differs from previous multi-scale methods, where place recognition is performed through a non-optimized distance metric in a fixed and pre-determined scale space. Instead, we learn an optimized distance metric which creates a new recognition space for clustering images with similar features while separating those with different features. Consequently, the method exploits the natural spatial scales present in the operating environment. With these adaptive scales, a hierarchical recognition mechanism with multiple parallel channels is then proposed. Each channel performs place recognition from a coarse match to a fine match. We present specific techniques for training each channel to recognize places at varying spatial scales and for combining the place recognition hypotheses from these parallel channels. We also conduct a systematic series of experiments and parameter studies that determine the effect on performance of using different numbers of combined recognition channels. The results demonstrate that the adaptive multi-scale approach outperforms the previous fixed multi-scale approach and is capable of producing better than state of the art performance compared to existing robotic navigation algorithms. The system complexity is linear in the number of places in the reference static map and can realize the online place recognition in mobile robotics on typical dataset sizes We analyze the results and provide theoretical analysis of the performance improvements. Finally, we discuss interesting insights gained with respect to future work in robotics and neuroscience in this area.

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

On the need of integrating emotions in robotic architectures

October 18, 2017 08:49 ,

Luiz Pessoa, Do Intelligent Robots Need Emotion?,Trends in Cognitive Sciences, Volume 21, Issue 11, 2017, Pages 817-819, DOI: 10.1016/j.tics.2017.06.010.

What is the place of emotion in intelligent robots? Researchers have advocated the inclusion of some emotion-related components in the information-processing architecture of autonomous agents. It is argued here that emotion needs to be merged with all aspects of the architecture: cognitive–emotional integration should be a key design principle.

Posted in: Psycho-physiological bases of engineering, Robotic architectures , Tagged: ,

Using EKF to estimate the state of a quadcopter in SE(3)

October 16, 2017 08:36 ,

Goodarzi, F.A. & Lee, Global Formulation of an Extended Kalman Filter on SE(3) for Geometric Control of a Quadrotor UAV, J Intell Robot Syst (2017) 88: 395, DOI: 10.1007/s10846-017-0525-6.

An extended Kalman filter (EKF) is developed on the special Euclidean group, S E(3) for geometric control of a quadrotor UAV. It is obtained by performing an intrinsic form of linearization on S E(3) to estimate the state of the quadrotor from noisy measurements. The proposed estimator considers all of the coupling effects between rotational and translational dynamics, and it is developed in a coordinate-free fashion. The desirable features of the proposed EKF are illustrated by numerical examples and experimental results for several scenarios. The proposed estimation scheme on S E(3) has been unprecedented and these results can be particularly useful for aggressive maneuvers in GPS denied environments or in situations where parts of onboard sensors fail.

Posted in: Mobile Robot Localization , Tagged: ,

Survey on visual attention in 3D for robotics

September 23, 2017 08:42 ,

Ekaterina Potapova, Michael Zillich, and Markus Vincze, Survey of recent advances in 3D visual attention for robotics, The International Journal of Robotics Research, Vol 36, Issue 11, pp. 1159 – 1176, DOI: 10.1177/0278364917726587.

3D visual attention plays an important role in both human and robotics perception that yet has to be explored in full detail. However, the majority of computer vision and robotics methods are concerned only with 2D visual attention. This survey presents findings and approaches that cover 3D visual attention in both human and robot vision, summarizing the last 30 years of research and also looking beyond computational methods. First, we present work in such fields as biological vision and neurophysiology, studying 3D attention in human observers. This provides a view of the role attention plays at the system level for biological vision. Then, we cover computer and robot vision approaches that take 3D visual attention into account. We compare approaches with respect to different categories, such as feature-based, data-based, or depth-based visual attention, and draw conclusions on what advances will help robotics to cope better with complex real-world settings and tasks.

Posted in: Computer vision, Robot sensors , Tagged:

Calibrating a robotic manipulator through photogrammetry, and a nice state-of-the-art in the issue of robot calibration

September 20, 2017 08:03 ,

Alexandre Filion, Ahmed Joubair, Antoine S. Tahan, Ilian A. Bonev, Robot calibration using a portable photogrammetry system, Robotics and Computer-Integrated Manufacturing, Volume 49, 2018, Pages 77-87, DOI: 10.1016/j.rcim.2017.05.004.

This work investigates the potential use of a commercially-available portablephotogrammetry system (the MaxSHOT 3D) in industrial robot calibration. To demonstrate the effectiveness of this system, we take the approach of comparing the device with a laser tracker (the FARO laser tracker) by calibrating an industrial robot, with each device in turn, then comparing the obtained robot position accuracy after calibration. As the use of a portablephotogrammetry system in robot calibration is uncommon, this paper presents how to proceed. It will cover the theory of robot calibration: the robot’s forward and inverse kinematics, the elasto-geometrical model of the robot, the generation and ultimate selection of robot configurations to be measured, and the parameter identification. Furthermore, an experimental comparison of the laser tracker and the MaxSHOT3D is described. The obtained results show that the FARO laser trackerION performs slightly better: The absolute positional accuracy obtained with the laser tracker is 0.365mm and 0.147mm for the maximum and the mean position errors, respectively. Nevertheless, the results obtained by using the MaxSHOT3D are almost as good as those obtained by using the laser tracker: 0.469mm and 0.197mm for the maximum and the mean position errors, respectively. Performances in distance accuracy, after calibration (i.e. maximum errors), are respectively 0.329mm and 0.352mm, for the laser tracker and the MaxSHOT 3D. However, as the validation measurements were acquired with the laser tracker, bias favors this device. Thus, we may conclude that the calibration performances of the two measurement devices are very similar.

Posted in: Industrial robots, Robot models , Tagged: ,

Interesting implementation of visual graph SLAM in C++ for educational purposes

September 19, 2017 07:59 ,

Dominik Schlegel, Mirco Colosi, Giorgio Grisetti, ProSLAM: Graph SLAM from a Programmer’s Perspective/strong>, arXiv:1709.04377.

In this paper we present ProSLAM, a lightweight stereo visual SLAM system designed with simplicity in mind. Our work stems from the experience gathered by the authors while teaching SLAM to students and aims at providing a highly modular system that can be easily implemented and understood. Rather than focusing on the well known mathematical aspects of Stereo Visual SLAM, in this work we highlight the data structures and the algorithmic aspects that one needs to tackle during the design of such a system. We implemented ProSLAM using the C++ programming language in combination with a minimal set of well known used external libraries. In addition to an open source implementation, we provide several code snippets that address the core aspects of our approach directly in this paper. The results of a thorough validation performed on standard benchmark datasets show that our approach achieves accuracy comparable to state of the art methods, while requiring substantially less computational resources.

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