1,734 research outputs found

    Algorithms for Autonomous Personal Navigation Systems

    Get PDF
    Personal positioning is a challenging topic in the area of navigation mainly because of the cost, size and power consumption constraints imposed on the hardware. Satellite based positioning techniques can meet the requirements for many applications, but cover well only outdoor environment. Problems like weak satellite signals make the positioning impossible indoors. Urban canyons are also difficult areas for GNSS based navigation because of large multipath errors and satellite signal outages. Many applications require seamless positioning in all environments. However, there is no overall solution for navigation in GNSS denied environment, which is reliable, accurate, cost effective and quickly installed. Recently developed systems for indoor positioning often require pre-installed infrastructure. Another approach is to use fully autonomous navigation systems based on self-contained sensors and street or indoor maps. This thesis is concerned with autonomous personal navigation devices, which do not rely on the reception of external information, like satellite or terrestrial signals. The three proposed algorithms can be integrated into personal navigation systems. The first algorithm computes positioning for a map aided navigation system designed for land vehicles traveling on road network. The novelty is in application of particle filtering to vehicle navigation using road network database. The second algorithm is aimed at map aided vehicle navigation indoors. The novelty is in the method for correction of position and heading. The third algorithm computes solution for pedestrian navigation system, which is based on body mounted inertial measurement unit and models of human gait

    Learning from Interventions using Hierarchical Policies for Safe Learning

    Full text link
    Learning from Demonstrations (LfD) via Behavior Cloning (BC) works well on multiple complex tasks. However, a limitation of the typical LfD approach is that it requires expert demonstrations for all scenarios, including those in which the algorithm is already well-trained. The recently proposed Learning from Interventions (LfI) overcomes this limitation by using an expert overseer. The expert overseer only intervenes when it suspects that an unsafe action is about to be taken. Although LfI significantly improves over LfD, the state-of-the-art LfI fails to account for delay caused by the expert's reaction time and only learns short-term behavior. We address these limitations by 1) interpolating the expert's interventions back in time, and 2) by splitting the policy into two hierarchical levels, one that generates sub-goals for the future and another that generates actions to reach those desired sub-goals. This sub-goal prediction forces the algorithm to learn long-term behavior while also being robust to the expert's reaction time. Our experiments show that LfI using sub-goals in a hierarchical policy framework trains faster and achieves better asymptotic performance than typical LfD.Comment: Accepted for publication at the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20

    Big Data in Bicycle Traffic: A user-oriented guide to the use of smartphone-generated bicycle traffic data

    Get PDF
    For cycling to be attractive, the infrastructure must be of high quality. Due to the high level of resources required to record it locally, the available data on the volume of cycling traffic has to date been patchy. At the moment, the most reliable and usable numbers seem to be derived from permanently installed automatic cycling traffic counters, already used by many local authorities. One disadvantage of these is that the number of data collection points is generally far too low to cover the entirety of a city or other municipality in a way that achieves truly meaningful results. The effect of side roads on cycling traffic is therefore only incompletely assessed. Furthermore, there is usually no data at all on other parameters, such as waiting times, route choices and cyclists’ speed. This gap might in future be filled by methods such as GPS route data, as is now possible by today’s widespread use of smartphones and the relevant tracking apps. The results of the project presented in this guide have been supported by the BMVI [Federal Ministry of Transport and Digital Infrastructure] within the framework of its 2020 National Cycling Plan. This research project seeks to investigate the usability of user data generated using a smartphone app for bicycle traffic planning by local authorities. In summary, it can be stated that, taking into account the factors described in this guide, GPS data are usable for bicycle traffic planning within certain limitations. (The GPS data evaluated in this case were provided by Strava Inc.) Nowadays it is already possible to assess where, when and how cyclists are moving around across the entire network. The data generated by the smartphone app could be most useful to local authorities as a supplement to existing permanent traffic counters. However, there are a few aspects that need to be considered when evaluating and interpreting the data, such as the rather fitness-oriented context of the routes surveyed in the examples examined. Moreover, some of the data is still provided as database or GIS files, although some online templates that are easier to use are being set up, and some can already be used in a basic initial form. This means that evaluation and interpretation still require specialist expertise as well as human resources. However, the need for these is expected to reduce in the future with the further development of web interfaces and supporting evaluation templates. For this to work, developers need to collaborate with local authorities to work out what parameters are needed as well as the most suitable formats. This research project carried out an approach to extrapolating cycling traffic volumes from random samples of GPS data over the whole network. This was also successfully verified in another municipality. Further research is still nevertheless required in the future, as well as adaptation to the needs of different localities. Evidence for the usability of GPS data in practice still needs to be acquired in the near future. The cities of Dresden, Leipzig and Mainz could be taken as examples for this, as they have all already taken their first steps in the use of GPS data in planning for and supporting cycling. These steps make sense in the light of the increasing digitisation of traffic and transport and the growing amount of data available as a result – despite the limitations on these data to date – so that administrative bodies can start early in building up the appropriate skills among their staff. The use of GPS data would yield benefits for bicycle traffic planning in the long run. In addition, the active involvement of cyclists opens up new possibilities in communication and citizen participation – even without requiring specialist knowledge. This guide delivers a practical introduction to the topic, giving a comprehensive overview of the opportunities, obstacles and potential offered by GPS data

    A dialogue based mobile virtual assistant for tourists: The SpaceBook Project

    Get PDF
    Ubiquitous mobile computing offers innovative approaches in the delivery of information that can facilitate free roaming of the city, informing and guiding the tourist as the city unfolds before them. However making frequent visual reference to mobile devices can be distracting, the user having to interact via a small screen thus disrupting the explorative experience. This research reports on an EU funded project, SpaceBook, that explored the utility of a hands-free, eyes-free virtual tour guide, that could answer questions through a spoken dialogue user interface and notify the user of interesting features in view while guiding the tourist to various destinations. Visibility modelling was carried out in real-time based on a LiDAR sourced digital surface model, fused with a variety of map and crowd sourced datasets (e.g. Ordnance Survey, OpenStreetMap, Flickr, Foursquare) to establish the most interesting landmarks visible from the user's location at any given moment. A number of variations of the SpaceBook system were trialled in Edinburgh (Scotland). The research highlighted the pleasure derived from this novel form of interaction and revealed the complexity of prioritising route guidance instruction alongside identification, description and embellishment of landmark information – there being a delicate balance between the level of information ‘pushed’ to the user, and the user's requests for further information. Among a number of challenges, were issues regarding the fidelity of spatial data and positioning information required for pedestrian based systems – the pedestrian having much greater freedom of movement than vehicles

    GNSS trajectory anomaly detection using similarity comparison methods for pedestrian navigation

    Get PDF
    The urban setting is a challenging environment for GNSS receivers. Multipath and other anomalies typically increase the positioning error of the receiver. Moreover, the error estimate of the position is often unreliable. In this study, we detect GNSS trajectory anomalies by using similarity comparison methods between a pedestrian dead reckoning trajectory, recorded using a foot-mounted inertial measurement unit, and the corresponding GNSS trajectory. During a normal walk, the foot-mounted inertial dead reckoning setup is trustworthy up to a few tens of meters. Thus, the differing GNSS trajectory can be detected using form similarity comparison methods. Of the eight tested methods, the Hausdorff distance (HD) and the accumulated distance difference (ADD) give slightly more consistent detection results compared to the rest

    A Computational Method based on Radio Frequency Technologies for the Analysis of Accessibility of Disabled People in Sustainable Cities

    Get PDF
    The sustainability strategy in urban spaces arises from reflecting on how to achieve a more habitable city and is materialized in a series of sustainable transformations aimed at humanizing different environments so that they can be used and enjoyed by everyone without exception and regardless of their ability. Modern communication technologies allow new opportunities to analyze efficiency in the use of urban spaces from several points of view: adequacy of facilities, usability, and social integration capabilities. The research presented in this paper proposes a method to perform an analysis of movement accessibility in sustainable cities based on radio frequency technologies and the ubiquitous computing possibilities of the new Internet of Things paradigm. The proposal can be deployed in both indoor and outdoor environments to check specific locations of a city. Finally, a case study in a controlled context has been simulated to validate the proposal as a pre-deployment step in urban environments

    Mobile Robot Navigation for Person Following in Indoor Environments

    Get PDF
    Service robotics is a rapidly growing area of interest in robotics research. Service robots inhabit human-populated environments and carry out specific tasks. The goal of this dissertation is to develop a service robot capable of following a human leader around populated indoor environments. A classification system for person followers is proposed such that it clearly defines the expected interaction between the leader and the robotic follower. In populated environments, the robot needs to be able to detect and identify its leader and track the leader through occlusions, a common characteristic of populated spaces. An appearance-based person descriptor, which augments the Kinect skeletal tracker, is developed and its performance in detecting and overcoming short and long-term leader occlusions is demonstrated. While following its leader, the robot has to ensure that it does not collide with stationary and moving obstacles, including other humans, in the environment. This requirement necessitates the use of a systematic navigation algorithm. A modified version of navigation function path planning, called the predictive fields path planner, is developed. This path planner models the motion of obstacles, uses a simplified representation of practical workspaces, and generates bounded, stable control inputs which guide the robot to its desired position without collisions with obstacles. The predictive fields path planner is experimentally verified on a non-person follower system and then integrated into the robot navigation module of the person follower system. To navigate the robot, it is necessary to localize it within its environment. A mapping approach based on depth data from the Kinect RGB-D sensor is used in generating a local map of the environment. The map is generated by combining inter-frame rotation and translation estimates based on scan generation and dead reckoning respectively. Thus, a complete mobile robot navigation system for person following in indoor environments is presented

    DEVELOPMENT OF AN AUTONOMOUS NAVIGATION SYSTEM FOR THE SHUTTLE CAR IN UNDERGROUND ROOM & PILLAR COAL MINES

    Get PDF
    In recent years, autonomous solutions in the multi-disciplinary field of the mining engineering have been an extremely popular applied research topic. The growing demand for mineral supplies combined with the steady decline in the available surface reserves has driven the mining industry to mine deeper underground deposits. These deposits are difficult to access, and the environment may be hazardous to mine personnel (e.g., increased heat, difficult ventilation conditions, etc.). Moreover, current mining methods expose the miners to numerous occupational hazards such as working in the proximity of heavy mining equipment, possible roof falls, as well as noise and dust. As a result, the mining industry, in its efforts to modernize and advance its methods and techniques, is one of the many industries that has turned to autonomous systems. Vehicle automation in such complex working environments can play a critical role in improving worker safety and mine productivity. One of the most time-consuming tasks of the mining cycle is the transportation of the extracted ore from the face to the main haulage facility or to surface processing facilities. Although conveyor belts have long been the autonomous transportation means of choice, there are still many cases where a discrete transportation system is needed to transport materials from the face to the main haulage system. The current dissertation presents the development of a navigation system for an autonomous shuttle car (ASC) in underground room and pillar coal mines. By introducing autonomous shuttle cars, the operator can be relocated from the dusty, noisy, and potentially dangerous environment of the underground mine to the safer location of a control room. This dissertation focuses on the development and testing of an autonomous navigation system for an underground room and pillar coal mine. A simplified relative localization system which determines the location of the vehicle relatively to salient features derived from on-board 2D LiDAR scans was developed for a semi-autonomous laboratory-scale shuttle car prototype. This simplified relative localization system is heavily dependent on and at the same time leverages the room and pillar geometry. Instead of keeping track of a global position of the vehicle relatively to a fixed coordinates frame, the proposed custom localization technique requires information regarding only the immediate surroundings. The followed approach enables the prototype to navigate around the pillars in real-time using a deterministic Finite-State Machine which models the behavior of the vehicle in the room and pillar mine with only a few states. Also, a user centered GUI has been developed that allows for a human user to control and monitor the autonomous vehicle by implementing the proposed navigation system. Experimental tests have been conducted in a mock mine in order to evaluate the performance of the developed system. A number of different scenarios simulating common missions that a shuttle car needs to undertake in a room and pillar mine. The results show a minimum success ratio of 70%
    corecore