Author Archives: Juan-antonio Fernández-madrigal

A novel method for hard real-time communications using the physical layer of Ethernet and a variation of TDMA

September 20, 2017 08:30 ,

Andrzej Przybył, Hard real-time communication solution for mechatronic systems, Robotics and Computer-Integrated Manufacturing, Volume 49, 2018, Pages 309-316, DOI: 10.1016/j.rcim.2017.08.001.

The paper proposes a method to build a highly efficient real-time communication solution for mechatronic systems. The method is based on the Ethernet physical layer (PHY) and on field programmable gate array (FPGA) technology and offers a better performance when compared to commercially available communication solutions. Although it is not directly compatible with the OSI/ISO model of TCP/IP protocol, vertical integration is done with a gateway. This provides simplicity and safety. Moreover, the use of the FPGA allows for integrating the communication solution with the user algorithm of particular distributed device inside a single chip. Therefore, the proposed solution is efficient and highly integrated.

Posted in: Communication networks, Real-Time Systems , 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: , , ,

Improving efficiency of decision with POMDPs in high-dimension state spaces

September 19, 2017 07:45 ,

Dmitry Kopitkov and Vadim Indelman, No belief propagation required: Belief space planning in high-dimensional state spaces via factor graphs, the matrix determinant lemma, and re-use of calculation, The International Journal of Robotics Research, Vol 36, Issue 10, pp. 1088 – 1130, DOI: 10.1177/0278364917721629.

We develop a computationally efficient approach for evaluating the information-theoretic term within belief space planning (BSP), where during belief propagation the state vector can be constant or augmented. We consider both unfocused and focused problem settings, whereas uncertainty reduction of the entire system or only of chosen variables is of interest, respectively. State-of-the-art approaches typically propagate the belief state, for each candidate action, through calculation of the posterior information (or covariance) matrix and subsequently compute its determinant (required for entropy). In contrast, our approach reduces runtime complexity by avoiding these calculations. We formulate the problem in terms of factor graphs and show that belief propagation is not needed, requiring instead a one-time calculation that depends on (the increasing with time) state dimensionality, and per-candidate calculations that are independent of the latter. To that end, we develop an augmented version of the matrix determinant lemma, and show that computations can be re-used when evaluating impact of different candidate actions. These two key ingredients and the factor graph representation of the problem result in a computationally efficient (augmented) BSP approach that accounts for different sources of uncertainty and can be used with various sensing modalities. We examine the unfocused and focused instances of our approach, and compare it with the state of the art, in simulation and using real-world data, considering problems such as autonomous navigation in unknown environments, measurement selection and sensor deployment. We show that our approach significantly reduces running time without any compromise in performance.

Posted in: Robot task planning , Tagged:

A practical example of mobile robot long term operation

September 14, 2017 08:53 ,

N. Hawes et al., The STRANDS Project: Long-Term Autonomy in Everyday Environments, IEEE Robotics & Automation Magazine, vol. 24, no. 3, pp. 146-156, DOI: 10.1109/MRA.2016.2636359.

Thanks to the efforts of the robotics and autonomous systems community, the myriad applications and capacities of robots are ever increasing. There is increasing demand from end users for autonomous service robots that can operate in real environments for extended periods. In the Spatiotemporal Representations and Activities for Cognitive Control in Long-Term Scenarios (STRANDS) project (http://strandsproject.eu), we are tackling this demand head-on by integrating state-of-the-art artificial intelligence and robotics research into mobile service robots and deploying these systems for long-term installations in security and care environments. Our robots have been operational for a combined duration of 104 days over four deployments, autonomously performing end-user-defined tasks and traversing 116 km in the process. In this article, we describe the approach we used to enable long-term autonomous operation in everyday environments and how our robots are able to use their long run times to improve their own performance.

Posted in: Robot applications , Tagged:

An application of POMDPs to robot surveillance

September 14, 2017 08:47 ,

S. Witwicki et al., Autonomous Surveillance Robots: A Decision-Making Framework for Networked Muiltiagent Systems, IEEE Robotics & Automation Magazine, vol. 24, no. 3, pp. 52-64, DOI: 10.1109/MRA.2017.2662222.

This article proposes an architecture for an intelligent surveillance system, where the aim is to mitigate the burden on humans in conventional surveillance systems by incorporating intelligent interfaces, computer vision, and autonomous mobile robots. Central to the intelligent surveillance system is the application of research into planning and decision making in this novel context. In this article, we describe the robot surveillance decision problem and explain how the integration of components in our system supports fully automated decision making. Several concrete scenarios deployed in real surveillance environments exemplify both the flexibility of our system to experiment with different representations and algorithms and the portability of our system into a variety of problem contexts. Moreover, these scenarios demonstrate how planning enables robots to effectively balance surveillance objectives, autonomously performing the job of human patrols and responders.

Posted in: Robot applications, Robot task planning , Tagged: , ,

Interesting approach to learning the sensorimotor behavior of a robot and of its predictive capabilities through NN

September 14, 2017 08:38 ,

R. Santos, R. Ferreira, Â. Cardoso and A. Bernardino, SNet: Co-Developing Artificial Retinas and Predictive Internal Models for Real Robots, IEEE Transactions on Cognitive and Developmental Systems, vol. 9, no. 3, pp. 213-222, DOI: 10.1109/TCDS.2016.2638885.

This paper focuses on a recently developed biologically inspired architecture, here denoted as sensorimotor network (SNet), able to co-develop sensorimotor structures directly from data acquired by a robot interacting with its environment. Such networks learn efficient internal models of the sensorimotor system, developing simultaneously sensor and motor representations as well as predictive models of the sensorimotor relationships adapted to their operating environment. Here, we describe our recent model of sensorimotor development and compare its performance with neural network models in predicting self-induced stimuli. In addition, we illustrate the influence of available resources and environment characteristics in the development of the SNet structures. Finally, an SNet is trained using real data recorded during a quadricopter drone flight.

Posted in: Developmental robotics , Tagged: ,

A new method to obtain WCET from binary code and to analyze the execution paths

September 3, 2017 08:25 ,

Thomas Sewell, Felix KamGernot Heiser, High-assurance timing analysis for a high-assurance real-time operating system, Real-Time Systems, Volume 53, Issue 5, pp 812–853, DOI: 10.1007/s1124.

Worst-case execution time (WCET) analysis of real-time code needs to be performed on the executable binary code for soundness. Obtaining tight WCET bounds requires determination of loop bounds and elimination of infeasible paths. The binary code, however, lacks information necessary to determine these bounds. This information is usually provided through manual intervention, or preserved in the binary by a specially modified compiler. We propose an alternative approach, using an existing translation-validation framework, to enable high-assurance, automatic determination of loop bounds and infeasible paths. We show that this approach automatically determines all loop bounds and many (possibly all) infeasible paths in the seL4 microkernel, as well as in standard WCET benchmarks which are in the language subset of our C parser. We also design and validate an improvement to the seL4 implementation, which permits a key part of the kernel’s API to be available to users in a mixed-criticality setting.

Posted in: Real-Time Systems , Tagged: ,

Evidence of the dicotomy reactive/predictive control in the brain

August 26, 2017 11:45 ,

Mattie Tops, Markus Quirin, Maarten A.S. Boksem, Sander L. Koole, Large-scale neural networks and the lateralization of motivation and emotion, International Journal of Psychophysiology, Volume 119, 2017, Pages 41-49, DOI: 10.1016/j.ijpsycho.2017.02.004.

Several lines of research in animals and humans converge on the distinction between two basic large-scale brain networks of self-regulation, giving rise to predictive and reactive control systems (PARCS). Predictive (internally-driven) and reactive (externally-guided) control are supported by dorsal versus ventral corticolimbic systems, respectively. Based on extant empirical evidence, we demonstrate how the PARCS produce frontal laterality effects in emotion and motivation. In addition, we explain how this framework gives rise to individual differences in appraising and coping with challenges. PARCS theory integrates separate fields of research, such as research on the motivational correlates of affect, EEG frontal alpha power asymmetry and implicit affective priming effects on cardiovascular indicators of effort during cognitive task performance. Across these different paradigms, converging evidence points to a qualitative motivational division between, on the one hand, angry and happy emotions, and, on the other hand, sad and fearful emotions. PARCS suggests that those two pairs of emotions are associated with predictive and reactive control, respectively. PARCS theory may thus generate important new insights on the motivational and emotional dynamics that drive autonomic and homeostatic control processes.

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

Chaos theory for modeling behavior of mobile robots that solve tasks evolutionarily

August 26, 2017 08:09 ,

Federico Da Rold, Chaotic analysis of embodied and situated agents, Robotics and Autonomous Systems, Volume 95, 2017, Pages 143-159, DOI: 10.1016/j.robot.2017.06.004.

Embodied and situated view of cognition is a transdisciplinary framework which stresses the importance of real time and dynamical interaction of an agent with the surrounding environment. This article presents a series of evolutionary robotics experiments that operationalize such concept, training miniature two-wheeled mobile robots to autonomously solve a temporal task. In order to provide a numerical description of the robots’ behavior, chaotic measures are estimated on the attractor reconstructed from the recorded positions of the agent. Chaos theory provides a rigorous mathematical framework consistent with an antireductionist approach, useful for understanding embodied and situated systems while avoiding a decomposition of the integrated system brain–body–environment. Time series are analyzed in detail using nonlinear mathematical tools in order to verify the presence of low-dimensional deterministic dynamical systems, a fundamental prerequisite for chaos theory. In particular, the recorded time series are evaluated with nonlinear prediction error to unveil deterministic dynamics, cross-prediction error to determine the stationarity of the signal, and surrogate data testing to verify the existence of nonlinear components in the underlying system. Estimators for quantifying level of chaos and fractal dimension are applied to suitable datasets. Results show that robots governed by a chaotic dynamic are more efficient at adapting to environments never experience during evolution, demonstrating robustness towards novel and unpredictable situations. Furthermore, chaotic measures, in particular fractal dimension, are correlated with the performance if robots exhibit a similar behavioral strategy.

Posted in: Developmental robotics , Tagged: , ,