9 research outputs found

    An efficient visual fiducial localisation system

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    With use cases that range from external localisation of single robots or robotic swarms to self-localisation in marker-augmented environments and simplifying perception by tagging objects in a robot's surrounding, fiducial markers have a wide field of application in the robotic world. We propose a new family of circular markers which allow for both computationally efficient detection, tracking and identification and full 6D position estimation. At the core of the proposed approach lies the separation of the detection and identification steps, with the former using computationally efficient circular marker detection and the latter utilising an open-ended `necklace encoding', allowing scalability to a large number of individual markers. While the proposed algorithm achieves similar accuracy to other state-of-the-art methods, its experimental evaluation in realistic conditions demonstrates that it can detect markers from larger distances while being up to two orders of magnitude faster than other state-of-the-art fiducial marker detection methods. In addition, the entire system is available as an open-source package at \url{https://github.com/LCAS/whycon}

    Constructing Parsimonious Analytic Models for Dynamic Systems via Symbolic Regression

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    Developing mathematical models of dynamic systems is central to many disciplines of engineering and science. Models facilitate simulations, analysis of the system's behavior, decision making and design of automatic control algorithms. Even inherently model-free control techniques such as reinforcement learning (RL) have been shown to benefit from the use of models, typically learned online. Any model construction method must address the tradeoff between the accuracy of the model and its complexity, which is difficult to strike. In this paper, we propose to employ symbolic regression (SR) to construct parsimonious process models described by analytic equations. We have equipped our method with two different state-of-the-art SR algorithms which automatically search for equations that fit the measured data: Single Node Genetic Programming (SNGP) and Multi-Gene Genetic Programming (MGGP). In addition to the standard problem formulation in the state-space domain, we show how the method can also be applied to input-output models of the NARX (nonlinear autoregressive with exogenous input) type. We present the approach on three simulated examples with up to 14-dimensional state space: an inverted pendulum, a mobile robot, and a bipedal walking robot. A comparison with deep neural networks and local linear regression shows that SR in most cases outperforms these commonly used alternative methods. We demonstrate on a real pendulum system that the analytic model found enables a RL controller to successfully perform the swing-up task, based on a model constructed from only 100 data samples

    Open-Source Drone Programming Course for Distance Engineering Education.

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    This article presents a full course for autonomous aerial robotics inside the RoboticsAcademy framework. This “drone programming” course is open-access and ready-to-use for any teacher/student to teach/learn drone programming with it for free. The students may program diverse drones on their computers without a physical presence in this course. Unmanned aerial vehicles (UAV) applications are essentially practical, as their intelligence resides in the software part. Therefore, the proposed course emphasizes drone programming through practical learning. It comprises a collection of exercises resembling drone applications in real life, such as following a road, visual landing, and people search and rescue, including their corresponding background theory. The course has been successfully taught for five years to students from several university engineering degrees. Some exercises from the course have also been validated in three aerial robotics competitions, including an international one. RoboticsAcademy is also briefly presented in the paper. It is an open framework for distance robotics learning in engineering degrees. It has been designed as a practical complement to the typical online videos of massive open online courses (MOOCs). Its educational contents are built upon robot operating system (ROS) middleware (de facto standard in robot programming), the powerful 3D Gazebo simulator, and the widely used Python programming language. Additionally, RoboticsAcademy is a suitable tool for gamified learning and online robotics competitions, as it includes several competitive exercises and automatic assessment toolspost-print5214 K

    Lifelong information-driven exploration for mobile robots to complete and Refine spatio-temporal maps in changing environments

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    Recent improvements in the ability of mobile robots to operate safely in human populated environments have allowed their deployment in households, offices and public buildings, such as museums and hospitals. However, the structure of these environments is typically not known a priori, which requires the robots to build their own models of their operational environments. This process is commonly known as "exploration" in mobile robotics. Moreover, real-world environments tend to change over time, which means that to achieve long-term autonomous operation, robots must also update their environment models as a part of their daily routine. The assumption of a perpetually-changing world adds a temporal dimension to the exploration problem, making exploration a never-ending lifelong learning process. To the best of our knowledge, this process termed "lifelong exploration" has never been studied in detail before and forms the main topic of the work presented in this thesis. Effcient lifelong exploration requires a robot to choose the right locations and times at which to collect observations in order to improve its environment model. To evaluate the ability of a robot to build and maintain its environment models, we need to be able to compare lifelong exploration strategies under repeatable experimental conditions. An evaluation methodology based on pre-recorded sensory datasets would not be suitable for this purpose, as this would not allow the robot to choose the location or time of its observations. Evaluating lifelong exploration requires the deterministic reproduction of environment changes, while preserving the robots ability to decide upon its own actions during the experiment. This thesis therefore contributes a new benchmarking methodology for lifelong exploration, which replicates the events occurring in real environments through physical simulations that reflect the environment changes gathered by ambient sensors over long periods of time. The established experimental benchmarks are based on long-term sensory datasets recorded in a smart home, with dynamics produced by a single person over a period of one year, and an office environment, with dynamics produced by a team of workers. Building upon the aforementioned benchmarking methodology, the thesis investigates possible strategies for lifelong exploration. An experimental comparison of lifelong exploration strategies that combine various decision-making paradigms and spatio-temporal representations is presented. Moreover, a new approach to lifelong explorations is proposed that applies information-theoretic exploration techniques to environment representations that model the uncertainty of world states as probabilistic functions of time. The proposed method explicitly models the world dynamics and can predict the environment changes. The predictive ability is used to reason about the most informative locations to explore for a given time. A 16 week long experiment shows that the combination of dynamic environment representations with information-gain exploration principles allows to create and maintain up-to-date models of continuously changing environments, enabling efficient and self-improving long-term operation of mobile service robots. The final part of the thesis considers the problem of acquiring and maintaining dense 3D models of dynamic environments during long-term operation, building on the work presented in the earlier chapters. The term "4D mapping" is used to indicate 3D mapping by mobile robots over extended periods of time. A new approach to lifelong 4D mapping and exploration is presented, which was deployed on a real robotic platform during long term operation in real-world human-populated environments. The approach integrates sensory data captured by the robot at different times and locations into a global, metric I 4D spatio-temporal model and then uses the model to decide where and when to perform the next round of observations. Finally, the deployment of the 4D exploration method in a real-world office scenario is described and evaluated. The one week long experiments show that the method enables reliable 4D mapping and persistent self-localisation of autonomous mobile robots, continually improving the robots maps to reflect the ever-changing world

    Heterogeneous computing systems for vision-based multi-robot tracking

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    Irwansyah A. Heterogeneous computing systems for vision-based multi-robot tracking. Bielefeld: UniversitÀt Bielefeld; 2017
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