Maximum likelihood estimation-assisted ASVSF through state covariance-based 2D SLAM algorithm

The smooth variable structure filter (ASVSF) has been relatively considered as a new robust predictor-corrector method for estimating the state. In order to effectively utilize it, an SVSF requires the accurate system model, and exact prior knowledge includes both the process and measurement noise statistic. Unfortunately, the system model is always inaccurate because of some considerations avoided at the beginning. Moreover, the small addictive noises are partially known or even unknown. Of course, this limitation can degrade the performance of SVSF or also lead to divergence condition. For this reason, it is proposed through this paper an adaptive smooth variable structure filter (ASVSF) by conditioning the probability density function of a measurementto the unknown parameters at one iteration. This proposed method is assumed to accomplish the localization and direct point-based observation task of a wheeled mobile robot, TurtleBot2. Finally, by realistically simulating it and comparing to a conventional method, the proposed method has been showing a better accuracy and stability in term of root mean square error (RMSE) of the estimated map coordinate (EMC) and estimated path coordinate (EPC)

Navigation and Grasping with a Mobile Manipulator: from Simulation to Experimental Results

Cobot is the name for collaborative robots. This kind of robot is intended to work in close contact with the human being and to collaborate, by increasing the production rate and by reducing the human onerous tasks, in terms of repetitiveness and precision. At the state of the art, Cobots are often fixed on a support platform, static in their workstation. The aim of this thesis is, hence, to explore, test and validate navigation algorithms for a holonomic mobile robot and in a second moment, to study its behavior with a Cobot mounted on it, in a pick-move-place application. To this purpose, the first part of the thesis addresses the mobile navigation, while the second part the mobile manipulation. Concerning mobile robotics, in the first place, a theoretical background is given and the kinematic model of a holonomic robot is derived. Then, the problem of simultaneous localization and mapping (SLAM) is addressed, i.e. how the robot is able to build a map while localizing itself. Finally, a dedicated chapter will explain the algorithms responsible for exploration and navigation: planners, exploration of frontiers and Monte Carlo localization. Once the necessary theoretical background has been given, these algorithms will be tested both in simulation and in practice on a real robot. In the second part, some theoretical knowledge about manipulators is given and also the kinematic model of the Cobot is derived, together with the algorithm used for a collision free trajectory planning. To conclude, the results of the complete task are shown, first of all in simulation and then on the real robotic system

Real-time Simultaneous Localization And Mapping Of Mobile Robots

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2008Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2008Bu çalışmanın amacı çeşitli algılayıcılara sahip mobil robot ile kapalı, bilinmeyen ortamların haritasını çıkarmak ve aynı zamanda robotun kendini bulunduğu ortam içinde konumlandırmasıdır. Yapılan çalışmada robotun çevre ile olan etkileşimi kızılötesi ve ultrasonik algılayıcılar ile sağlanmaktadır. Ultrasonik algılayıcılar ucuz ve başarılı bir algılayıcı tipi olmasının yanında, yapısından kaynaklanan problemlerden dolayı çalışması zor olan algılayıcı tiplerinden biridir. Yapılan çalışmalar sırasında bu problemlerin en az seviyeye indirilmesi sağlanmıştır. Kızılötesi algılayıcılar ise yakın mesafeden yaptıkları doğru ölçümlerden dolayı çarpışma önleyici güvenlik sistemi amaçlı kullanılmıştır. Ortam haritasının çıkarılmasında ultrasonik mesafe ölçerler ve dijital pusula kullanılmıştır. Bununla birlikte robotun konumunun takip edilebilmesi için robotun üzerinde enkoderli motorlar kullanılmıştır. Robotun konumlandırılması ve harita çıkarma doğruluğu büyük ölçüde tasarımda kullanılan algılayıcı ve eyleyicilere bağlıdır. Algılayıcı ve eyleyicilerin seçiminde boyutları, doğrulukları ve mikroişlemci ile olan arayüzleri dikkate alınmıştır. Algılayıcılar tarafından ölçülen veriler mikroişlemci tarafından alınıp işlenmekte ve daha karmaşık hesaplama, bilgi depolama, konumlandırma, harita çıkarma işlemi yapacak olan bilgisayara kablosuz RF iletişimi ile aktarılmaktadır.The aim of this study is localization and mapping of the unknown indoor environments using mobile robot that have various sensors. The mobile robot provides interaction with the surroundings by using infrared and ultrasonic sensors. The ultrasonic sensors are cheap and successful but also they have some problem arise from the structure of them. These problems are reduced to the lower level during the study. Infrared sensors perform accurate measurements from the closer range therefore they are used for collision avoidance security purposes. Environment mapping is generated by using ultrasonic range finders and digital compass. In addition to this, to observe the localization of the robot, motors with encoders are used. Localization of the robot and accuracy of mapping are mostly related to used sensors and actuators of the robot design. The selection of the sensors and the actuators are considered according to their sizes, accuracies, interfaces to the microprocessor. Data measured by the sensors that is received and processed at the microprocessor. Then, data processed by the microprocessor is sent to the remote computer via RF communication for the complicated computation, data storage, localization and generating map.Yüksek LisansM.Sc

Automatic large-scale three dimensional modeling using cooperative multiple robots

Abstract3D modeling of real objects by a 3D laser scanner has become popular in many applications, such as reverse engineering of petrochemical plants, civil engineering and construction, and digital preservation of cultural properties. Despite the development of lightweight and high-speed laser scanners, the complicated measurement procedure and long measurement time are still heavy burdens for widespread use of laser scanning. To solve these problems, a robotic 3D scanning system using multiple robots has been proposed. This system, named CPS-SLAM, consists of a parent robot with a 3D laser scanner and child robots with target markers. A large-scale 3D model is acquired by an on-board 3D laser scanner on the parent robot from several positions determined precisely by a localization technique, named the Cooperative Positioning System (CPS), that uses multiple robots. Therefore, this system can build a 3D model without complicated post-processing procedures such as ICP. In addition, this system is an open-loop SLAM system and a very precise 3D model can be obtained without closed loops. This paper proposes an automatic planning technique for a laser measurement by using CPS-SLAM. Planning a proper scanning strategy depending on a target structure makes it possible to perform laser scanning efficiently and accurately even for a large-scale and complex environment. The proposed technique plans an efficient scanning strategy automatically by taking account of several criteria, such as visibility between robots, error accumulation, and efficient traveling. We conducted computer simulations and outdoor experiments to verify the performance of the proposed technique

Contemporary Robotics

This book book is a collection of 18 chapters written by internationally recognized experts and well-known professionals of the field. Chapters contribute to diverse facets of contemporary robotics and autonomous systems. The volume is organized in four thematic parts according to the main subjects, regarding the recent advances in the contemporary robotics. The first thematic topics of the book are devoted to the theoretical issues. This includes development of algorithms for automatic trajectory generation using redudancy resolution scheme, intelligent algorithms for robotic grasping, modelling approach for reactive mode handling of flexible manufacturing and design of an advanced controller for robot manipulators. The second part of the book deals with different aspects of robot calibration and sensing. This includes a geometric and treshold calibration of a multiple robotic line-vision system, robot-based inline 2D/3D quality monitoring using picture-giving and laser triangulation, and a study on prospective polymer composite materials for flexible tactile sensors. The third part addresses issues of mobile robots and multi-agent systems, including SLAM of mobile robots based on fusion of odometry and visual data, configuration of a localization system by a team of mobile robots, development of generic real-time motion controller for differential mobile robots, control of fuel cells of mobile robots, modelling of omni-directional wheeled-based robots, building of hunter- hybrid tracking environment, as well as design of a cooperative control in distributed population-based multi-agent approach. The fourth part presents recent approaches and results in humanoid and bioinspirative robotics. It deals with design of adaptive control of anthropomorphic biped gait, building of dynamic-based simulation for humanoid robot walking, building controller for perceptual motor control dynamics of humans and biomimetic approach to control mechatronic structure using smart materials

Characterisation of a nuclear cave environment utilising an autonomous swarm of heterogeneous robots

As nuclear facilities come to the end of their operational lifetime, safe decommissioning becomes a more prevalent issue. In many such facilities there exist ‘nuclear caves’. These caves constitute areas that may have been entered infrequently, or even not at all, since the construction of the facility. Due to this, the topography and nature of the contents of these nuclear caves may be unknown in a number of critical aspects, such as the location of dangerous substances or significant physical blockages to movement around the cave. In order to aid safe decommissioning, autonomous robotic systems capable of characterising nuclear cave environments are desired. The research put forward in this thesis seeks to answer the question: is it possible to utilise a heterogeneous swarm of autonomous robots for the remote characterisation of a nuclear cave environment? This is achieved through examination of the three key components comprising a heterogeneous swarm: sensing, locomotion and control. It will be shown that a heterogeneous swarm is not only capable of performing this task, it is preferable to a homogeneous swarm. This is due to the increased sensory and locomotive capabilities, coupled with more efficient explorational prowess when compared to a homogeneous swarm

Theory, Design, and Implementation of Landmark Promotion Cooperative Simultaneous Localization and Mapping

Simultaneous Localization and Mapping (SLAM) is a challenging problem in practice, the use of multiple robots and inexpensive sensors poses even more demands on the designer. Cooperative SLAM poses specific challenges in the areas of computational efficiency, software/network performance, and robustness to errors. New methods in image processing, recursive filtering, and SLAM have been developed to implement practical algorithms for cooperative SLAM on a set of inexpensive robots. The Consolidated Unscented Mixed Recursive Filter (CUMRF) is designed to handle non-linear systems with non-Gaussian noise. This is accomplished using the Unscented Transform combined with Gaussian Mixture Models. The Robust Kalman Filter is an extension of the Kalman Filter algorithm that improves the ability to remove erroneous observations using Principal Component Analysis (PCA) and the X84 outlier rejection rule. Forgetful SLAM is a local SLAM technique that runs in nearly constant time relative to the number of visible landmarks and improves poor performing sensors through sensor fusion and outlier rejection. Forgetful SLAM correlates all measured observations, but stops the state from growing over time. Hierarchical Active Ripple SLAM (HAR-SLAM) is a new SLAM architecture that breaks the traditional state space of SLAM into a chain of smaller state spaces, allowing multiple robots, multiple sensors, and multiple updates to occur in linear time with linear storage with respect to the number of robots, landmarks, and robots poses. This dissertation presents explicit methods for closing-the-loop, joining multiple robots, and active updates. Landmark Promotion SLAM is a hierarchy of new SLAM methods, using the Robust Kalman Filter, Forgetful SLAM, and HAR-SLAM. Practical aspects of SLAM are a focus of this dissertation. LK-SURF is a new image processing technique that combines Lucas-Kanade feature tracking with Speeded-Up Robust Features to perform spatial and temporal tracking. Typical stereo correspondence techniques fail at providing descriptors for features, or fail at temporal tracking. Several calibration and modeling techniques are also covered, including calibrating stereo cameras, aligning stereo cameras to an inertial system, and making neural net system models. These methods are important to improve the quality of the data and images acquired for the SLAM process

On the Enhancement of the Localization of Autonomous Mobile Platforms

The focus of many industrial and research entities on achieving full robotic autonomy increased in the past few years. In order to achieve full robotic autonomy, a fundamental problem is the localization, which is the ability of a mobile platform to determine its position and orientation in the environment. In this thesis, several problems related to the localization of autonomous platforms are addressed, namely, visual odometry accuracy and robustness; uncertainty estimation in odometries; and accurate multi-sensor fusion-based localization. Beside localization, the control of mobile manipulators is also tackled in this thesis. First, a generic image processing pipeline is proposed which, when integrated with a feature-based Visual Odometry (VO), can enhance robustness, accuracy and reduce the accumulation of errors (drift) in the pose estimation. Afterwards, since odometries (e.g. wheel odometry, LiDAR odometry, or VO) suffer from drift errors due to integration, and because such errors need to be quantified in order to achieve accurate localization through multi-sensor fusion schemes (e.g. extended or unscented kalman filters). A covariance estimation algorithm is proposed, which estimates the uncertainty of odometry measurements using another sensor which does not rely on integration. Furthermore, optimization-based multi-sensor fusion techniques are known to achieve better localization results compared to filtering techniques, but with higher computational cost. Consequently, an efficient and generic multi-sensor fusion scheme, based on Moving Horizon Estimation (MHE), is developed. The proposed multi-sensor fusion scheme: is capable of operating with any number of sensors; and considers different sensors measurements rates, missing measurements, and outliers. Moreover, the proposed multi-sensor scheme is based on a multi-threading architecture, in order to reduce its computational cost, making it more feasible for practical applications. Finally, the main purpose of achieving accurate localization is navigation. Hence, the last part of this thesis focuses on developing a stabilization controller of a 10-DOF mobile manipulator based on Model Predictive Control (MPC). All of the aforementioned works are validated using numerical simulations; real data from: EU Long-term Dataset, KITTI Dataset, TUM Dataset; and/or experimental sequences using an omni-directional mobile robot. The results show the efficacy and importance of each part of the proposed work

Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development