Navigation without localisation: reliable teach and repeat based on the convergence theorem

We present a novel concept for teach-and-repeat visual navigation. The proposed concept is based on a mathematical model, which indicates that in teach-and-repeat navigation scenarios, mobile robots do not need to perform explicit localisation. Rather than that, a mobile robot which repeats a previously taught path can simply `replay' the learned velocities, while using its camera information only to correct its heading relative to the intended path. To support our claim, we establish a position error model of a robot, which traverses a taught path by only correcting its heading. Then, we outline a mathematical proof which shows that this position error does not diverge over time. Based on the insights from the model, we present a simple monocular teach-and-repeat navigation method. The method is computationally efficient, it does not require camera calibration, and it can learn and autonomously traverse arbitrarily-shaped paths. In a series of experiments, we demonstrate that the method can reliably guide mobile robots in realistic indoor and outdoor conditions, and can cope with imperfect odometry, landmark deficiency, illumination variations and naturally-occurring environment changes. Furthermore, we provide the navigation system and the datasets gathered at http://www.github.com/gestom/stroll_bearnav.Comment: The paper will be presented at IROS 2018 in Madri

Tahap penguasaan, sikap dan minat pelajar Kolej Kemahiran Tinggi MARA terhadap mata pelajaran Bahasa Inggeris

Kajian ini dilakukan untuk mengenal pasti tahap penguasaan, sikap dan minat pelajar Kolej Kemahiran Tinggi Mara Sri Gading terhadap Bahasa Inggeris. Kajian yang dijalankan ini berbentuk deskriptif atau lebih dikenali sebagai kaedah tinjauan. Seramai 325 orang pelajar Diploma in Construction Technology dari Kolej Kemahiran Tinggi Mara di daerah Batu Pahat telah dipilih sebagai sampel dalam kajian ini. Data yang diperoleh melalui instrument soal selidik telah dianalisis untuk mendapatkan pengukuran min, sisihan piawai, dan Pekali Korelasi Pearson untuk melihat hubungan hasil dapatan data. Manakala, frekuensi dan peratusan digunakan bagi mengukur penguasaan pelajar. Hasil dapatan kajian menunjukkan bahawa tahap penguasaan Bahasa Inggeris pelajar adalah berada pada tahap sederhana manakala faktor utama yang mempengaruhi penguasaan Bahasa Inggeris tersebut adalah minat diikuti oleh sikap. Hasil dapatan menggunakan pekali Korelasi Pearson juga menunjukkan bahawa terdapat hubungan yang signifikan antara sikap dengan penguasaan Bahasa Inggeris dan antara minat dengan penguasaan Bahasa Inggeris. Kajian menunjukkan bahawa semakin positif sikap dan minat pelajar terhadap pengajaran dan pembelajaran Bahasa Inggeris semakin tinggi pencapaian mereka. Hasil daripada kajian ini diharapkan dapat membantu pelajar dalam meningkatkan penguasaan Bahasa Inggeris dengan memupuk sikap positif dalam diri serta meningkatkan minat mereka terhadap Bahasa Inggeris dengan lebih baik. Oleh itu, diharap kajian ini dapat memberi panduan kepada pihak-pihak yang terlibat dalam membuat kajian yang akan datang

Middleware platform for distributed applications incorporating robots, sensors and the cloud

Cyber-physical systems in the factory of the future will consist of cloud-hosted software governing an agile production process executed by autonomous mobile robots and controlled by analyzing the data from a vast number of sensors. CPSs thus operate on a distributed production floor infrastructure and the set-up continuously changes with each new manufacturing task. In this paper, we present our OSGibased middleware that abstracts the deployment of servicebased CPS software components on the underlying distributed platform comprising robots, actuators, sensors and the cloud. Moreover, our middleware provides specific support to develop components based on artificial neural networks, a technique that recently became very popular for sensor data analytics and robot actuation. We demonstrate a system where a robot takes actions based on the input from sensors in its vicinity

Autonomous control of underground mining vehicles using reactive navigation

Describes how many of the navigation techniques developed by the robotics research community over the last decade may be applied to a class of underground mining vehicles (LHDs and haul trucks). We review the current state-of-the-art in this area and conclude that there are essentially two basic methods of navigation applicable. We describe an implementation of a reactive navigation system on a 30 tonne LHD which has achieved full-speed operation at a production mine

Simulation of a mobile robot navigation system

Mobile robots are used in various application areas including manufacturing, mining, military operations, search and rescue missions and so on. As such there is a need to model robot mobility that tracks robot system modules such as navigation system and visi on based object recognition. For the navigation system it is important to locate the position of the robot in surr ounding environment. Then it has to plan a path towards desired destination. The navigation system of a robot has to identify all potential obstacles in order to find a suitable path. The objective of this research is to develop a simulation system to identify difficulties facing mobile robot navigation in industrial environments, and then tackle these problems effectively. The simulation makes use of information provided by various sensors including vision, range, and force sensors. With the help of battery operated mobile robots it is possible to move objects around in any industry/manufacturing plant and thus minimize environmental impact due to carbon emissions and pollution. The use of such robots in industry also makes it safe to deal with hazardous materials. In industry, a mobile robot deals with many tools and equipment and therefore it has to detect and recognize these objects and then track them. In this paper, the object detection and recognition is based on vision sensors and then image processing techniques. Techniques cove red include Speeded Up Ro bust Features (SURF), template matching, and colour segmentation. If the robot detects the target in its view, it will track the target and then grasp it. However, if the object is not in the current view, the robot continues its search to find it. To make the mobile robot move in its environment, a number of basic path planning strategies have been used. In the navigation system, the robot navigates to the nearest wall (or similar obstacle) and then moves along that obstacle. If an obstacle is detected by the robot using the built-in ultrasonic range sensor, the robot will navigate around that obstacle and then continue moving along it. While the robot is self-navigating in its environment, it continues to look for the target. The robot used in this work robot is scalable for industrial applications in mining, search and rescue missions, and so on. This robot is environmentally friendly and does not produce carbon emissions. In this paper the simulation of path planning algorithm for an autonomous robot is presented. Results of modelling the robot in a real-world industrial environment for testing the robot’s navigation are also discussed

Investigation on the mobile robot navigation in an unknown environment

Mobile robots could be used to search, find, and relocate objects in many types of manufacturing operations and environments. In this scenario, the target objects might reside with equal probability at any location in the environment and, therefore, the robot must navigate and search the whole area autonomously, and be equipped with specific sensors to detect objects. Novel challenges exist in developing a control system, which helps a mobile robot achieve such tasks, including constructing enhanced systems for navigation, and vision-based object recognition. The latter is important for undertaking the exploration task that requires an optimal object recognition technique. In this thesis, these challenges, for an indoor environment, were divided into three sub-problems. In the first, the navigation task involved discovering an appropriate exploration path for the entire environment, with minimal sensing requirements. The Bug algorithm strategies were adapted for modelling the environment and implementing the exploration path. The second was a visual-search process, which consisted of employing appropriate image-processing techniques, and choosing a suitable viewpoint field for the camera. This study placed more emphasis on colour segmentation, template matching and Speeded-Up Robust Features (SURF) for object detection. The third problem was the relocating process, which involved using a robot’s gripper to grasp the detected, desired object and then move it to the assigned, final location. This also included approaching both the target and the delivery site, using a visual tracking technique. All codes were developed using C++ and C programming, and some libraries that included OpenCV and OpenSURF were utilized for image processing. Each control system function was tested both separately, and then in combination as a whole control program. The system performance was evaluated using two types of mobile robots: legged and wheeled. In this study, it was necessary to develop a wheeled search robot with a high performance processor. The experimental results demonstrated that the methodology used for the search robots was highly efficient provided the processor was adequate. It was concluded that it is possible to implement a navigation system within a minimum number of sensors if they are located and used effectively on the robot’s body. The main challenge within a visual-search process is that the environmental conditions are difficult to control, because the search robot executes its tasks in dynamic environments. The additional challenges of scaling these small robots up to useful industrial capabilities were also explored

Self-Organized Multi-Camera Network for a Fast and Easy Deployment of Ubiquitous Robots in Unknown Environments

To bring cutting edge robotics from research centres to social environments, the robotics community must start providing affordable solutions: the costs must be reduced and the quality and usefulness of the robot services must be enhanced. Unfortunately, nowadays the deployment of robots and the adaptation of their services to new environments are tasks that usually require several days of expert work. With this in view, we present a multi-agent system made up of intelligent cameras and autonomous robots, which is easy and fast to deploy in different environments. The cameras will enhance the robot perceptions and allow them to react to situations that require their services. Additionally, the cameras will support the movement of the robots. This will enable our robots to navigate even when there are not maps available. The deployment of our system does not require expertise and can be done in a short period of time, since neither software nor hardware tuning is needed. Every system task is automatic, distributed and based on self-organization processes. Our system is scalable, robust, and flexible to the environment. We carried out several real world experiments, which show the good performance of our proposalThis work was supported by the research projects TIN2009-07737, INCITE08PXIB262202PR, and TIN2012-32262, the grant BES-2010-040813 FPI-MICINN, and by the grant “Consolidation of Competitive Research Groups, Xunta de Galicia ref. 2010/6”S

An Object Detection and Identification System for a Mobile Robot Control

The one of the features of mobile robot control is to detect and to identify objects in workspace. Especially, autonomous systems must detect obstacles and then revise actual trajectories according to new conditions. Hence, many solutions and approaches can be found in literature. Different sensors and cameras are used to solve problem by many researchers. Different type sensors usage can affect not only system performance but also operational cost. In this study, single camera based obstacle detection and identification algorithm was developed to control omni-drive mobile robot systems. Objects and obstacles, which are in robot view, are detected and identified their coordinates by using developed algorithms dynamically. Developed algorithm was tested on Festo Robotino mobile robot. Proposed approach offers not only cost efficiency but also short process time

Secure Encoded Instruction Graphs for End-to-End Data Validation in Autonomous Robots

As autonomous robots become increasingly ubiquitous, more attention is being paid to the security of robotic operation. Autonomous robots can be seen as cyber-physical systems that transverse the virtual realm and operate in the human dimension. As a consequence, securing the operation of autonomous robots goes beyond securing data, from sensor input to mission instructions, towards securing the interaction with their environment. There is a lack of research towards methods that would allow a robot to ensure that both its sensors and actuators are operating correctly without external feedback. This paper introduces a robotic mission encoding method that serves as an end-to-end validation framework for autonomous robots. In particular, we put our framework into practice with a proof of concept describing a novel map encoding method that allows robots to navigate an objective environment with almost-zero a priori knowledge of it, and to validate operational instructions. We also demonstrate the applicability of our framework through experiments with real robots for two different map encoding methods. The encoded maps inherit all the advantages of traditional landmark-based navigation, with the addition of cryptographic hashes that enable end-to-end information validation. This end-to-end validation can be applied to virtually any aspect of robotic operation where there is a predefined set of operations or instructions given to the robot