WiFi Localization and Navigation for Autonomous Indoor Mobile Robots

Building upon previous work that demonstrates the effectiveness of WiFi localization information per se, in this paper we contribute a mobile robot that autonomously navigates in indoor environments using WiFi sensory data. We model the world as a WiFi signature map with geometric constraints and introduce a continuous perceptual model of the environment generated from the discrete graph-based WiFi signal strength sampling. We contribute our WiFi localization algorithm which continuously uses the perceptual model to update the robot location in conjunction with its odometry data. We then briefly introduce a navigation approach that robustly uses the WiFi location estimates. We present the results of our exhaustive tests of the WiFi localization independently and in conjunction with the navigation of our custom-built mobile robot in extensive long autonomous runs

Low-effort place recognition with WiFi fingerprints using deep learning

Using WiFi signals for indoor localization is the main localization modality of the existing personal indoor localization systems operating on mobile devices. WiFi fingerprinting is also used for mobile robots, as WiFi signals are usually available indoors and can provide rough initial position estimate or can be used together with other positioning systems. Currently, the best solutions rely on filtering, manual data analysis, and time-consuming parameter tuning to achieve reliable and accurate localization. In this work, we propose to use deep neural networks to significantly lower the work-force burden of the localization system design, while still achieving satisfactory results. Assuming the state-of-the-art hierarchical approach, we employ the DNN system for building/floor classification. We show that stacked autoencoders allow to efficiently reduce the feature space in order to achieve robust and precise classification. The proposed architecture is verified on the publicly available UJIIndoorLoc dataset and the results are compared with other solutions

Elevator‘s External Button Recognition and Detection for Vision-based System

Recently, autonomous transporter offers the assistance and delivery for user but they are only focusing on single floor environment. To widen up fields of robotic, they teach robot to use an elevator because elevator provides an essential means of faster movement across level. However, most of the mobile service robot failed to detect elevator’s position due to the complex background and reflection on the elevator door and button panel itself. This paper presents a new strategy for recognition method to detect elevator by detecting their external button efficiently. Sobel is use as edge detection operator to find the estimated absolute gradient magnitude at each point in an input grayscale image. Then, but we enhanced the technique by combining it with wiener filter to reduce the amount of noise present in a signal by comparing the signal with an estimation of the desired noiseless signal. This filter helps to eliminate the reflection image on elevator’s button panel before it can be converted to black and white image (binarization). The process followed by some morphological and structuring elements process. Tests have been done and the results shown that elevator’s external button can be recognized and detected by those entire framework

A Simulation-Based Study of Maze-Solving-Robot Navigation for Educational Purposes

The point of education in the early stage of studying robotics is understanding its basic principles joyfully. Therefore, this paper creates a simulation program of indoor navigations using an open-source code in Python to make navigation and control algorithms easier and more attractive to understand and develop. We propose the maze-solving-robot simulation as a teaching medium in class to help students imagine and connect the robot theory to its actual movement. The simulation code is built for free to learn, improve, and extend in robotics courses or assignments. A maze-solving robot study case is then done as an example of implementing navigation algorithms. Five algorithms are compared, such as Random Mouse, Wall Follower, Pledge, Tremaux, and Dead-End Filling. Each algorithm is simulated a hundred times in every type of the proposed mazes, namely mazes with dead ends, loops only, and both dead ends and loops. The observed indicators of the algorithms are the success rate of the robots reaching the finish lines and the number of steps taken. The simulation results show that each algorithm has different characteristics that should be considered before being chosen. The recommendation of when-to-use the algorithms is discussed in this paper as an example of the output simulation analysis for studying robotics

Low Speed Vehicle Localization using WiFi FingerPrinting

International audienceRecently, the problem of fully autonomous navigation of vehicle has gained major interest from research institutes and private companies. In general, these researches rely on GPS in fusion with other sensors to track vehicle in outdoor environment. However, as indoor environment such as car park is also an important scenario for vehicle navigation, the lack of GPS poses a serious problem. This study presents an approach to use WiFi Fingerprinting as a replacement for GPS information in order to allow seamlessly transition of localization architecture from outdoor to indoor environment. Often, movement speed of vehicle in indoor environment is low (10-12km/h) in comparison to outdoor scene but still surpasses human walking speed (3-5km/h, which is usually maximum movement speed for effective WiFi localization). This paper proposes an ensemble classification method together with a motion model in order to deal with the above issue. Experiments show that proposed method is capable of imitating GPS behavior on vehicle tracking