Eksperimentalna usporedba metoda izgradnje mrežastih karata prostora korištenjem ultrazvučnih senzora

For successful usage of mobile robots in human working areas several navigation problems have to be solved. One of the navigational problems is the creation and update of the model or map of a mobile robot working environment. This article describes most used types of the occupancy grid maps based sonar range readings. These maps are: (i) Bayesian map, (ii) Dempster-Shafer map, (iii) Fuzzy map, (iv) Borenstein map, (v) MURIEL map, and (vi) TBF map. Besides the maps description, a memory consumption and computation time comparison is done. Simulation validation is done using the AMORsim mobile robot simulator for Matlab and experimental validation is done using a Pioneer 3DX mobile robot. Obtained results are presented and compared regarding resulting map quality.Za uspješnu primjenu mobilnih robota u radnim prostorima s ljudima potrebno je riješiti različite probleme navigacije. Jedan od problema navigacije jest kreiranje modela i uključivanje novih informacija o radnoj okolini mobilnog robota u model radne okoline ili kartu. Članak opisuje često korištene tipove mrežastih karata prostora zasnovanih na očitanjima ultrazvučnih osjetila udaljenosti. Obrađeni modeli prostora su: (i) Bayesova karta, (ii) Dempster-Shaferova karta, (iii) neizrazita karta, (iv) Borensteinova karta, (v) MURIEL karta i (vi) TBF karta. Osim opisa, u članku je dana i usporedba implementiranih algoritama prema memorijskim i računskim zahtjevima. Simulacijska provjera napravljena je korištenjem AMORsim simulatora mobilnog robota za programski paket Matlab, a eksperimentalna provjera napravljena je korištenjem Pioneer 3DX mobilnog robota. Također su prikazani dobiveni rezultati uz usporedbu njihove kakvoće

Conditional GANs for Sonar Image Filtering with Applications to Underwater Occupancy Mapping

Underwater robots typically rely on acoustic sensors like sonar to perceive their surroundings. However, these sensors are often inundated with multiple sources and types of noise, which makes using raw data for any meaningful inference with features, objects, or boundary returns very difficult. While several conventional methods of dealing with noise exist, their success rates are unsatisfactory. This paper presents a novel application of conditional Generative Adversarial Networks (cGANs) to train a model to produce noise-free sonar images, outperforming several conventional filtering methods. Estimating free space is crucial for autonomous robots performing active exploration and mapping. Thus, we apply our approach to the task of underwater occupancy mapping and show superior free and occupied space inference when compared to conventional methods.Comment: 7 pages, 13 figures. This paper is published at 2023 IEEE International Conference on Robotics and Automation (ICRA

Topomap: Topological Mapping and Navigation Based on Visual SLAM Maps

Visual robot navigation within large-scale, semi-structured environments deals with various challenges such as computation intensive path planning algorithms or insufficient knowledge about traversable spaces. Moreover, many state-of-the-art navigation approaches only operate locally instead of gaining a more conceptual understanding of the planning objective. This limits the complexity of tasks a robot can accomplish and makes it harder to deal with uncertainties that are present in the context of real-time robotics applications. In this work, we present Topomap, a framework which simplifies the navigation task by providing a map to the robot which is tailored for path planning use. This novel approach transforms a sparse feature-based map from a visual Simultaneous Localization And Mapping (SLAM) system into a three-dimensional topological map. This is done in two steps. First, we extract occupancy information directly from the noisy sparse point cloud. Then, we create a set of convex free-space clusters, which are the vertices of the topological map. We show that this representation improves the efficiency of global planning, and we provide a complete derivation of our algorithm. Planning experiments on real world datasets demonstrate that we achieve similar performance as RRT* with significantly lower computation times and storage requirements. Finally, we test our algorithm on a mobile robotic platform to prove its advantages.Comment: 8 page

Concurrent Cognitive Mapping and Localization Using Expectation Maximization

Robot mapping remains one of the most challenging problems in robot programming. Most successful methods use some form of occupancy grid for representing a mapped region. An occupancy grid is a two dimensional array in which the array cells represents (x,y) coordinates of a cartesian map. This approach becomes problematic in mapping large environments as the map quickly becomes too large for processing and storage. Rather than storing the map as an occupancy grid, our robot (equipped with ultrasonic sonars) views the world as a series of connected spaces. These spaces are initially mapped as an occupancy grid in a room-by-room fashion using a modified version of the Histogram In Motion Mapping (HIMM) algorithm extended in this thesis. As the robot leaves a space, denoted by passing through a doorway, it converts the grid to a polygonal representation using a novel edge detection technique. Then, it stores the polygonal representation as rooms and hallways in a set of Absolute Space Representations (ASRs) representing the space connections. Using this representation makes navigation and localization easier for the robot to process. The system also performs localization on the simplified cognitive version of the map using an iterative method of estimating the maximum likelihood of the robot\u27s correct position. This is accomplished using the Expectation Maximization algorithm. Treating vector directions from the polygonal map as a Gaussian distribution, the Expectation Maximization algorithm is applied, for the first time, to find the most probable correct pose while using a cognitive mapping approach

Field Testing of a Stochastic Planner for ASV Navigation Using Satellite Images

We introduce a multi-sensor navigation system for autonomous surface vessels (ASV) intended for water-quality monitoring in freshwater lakes. Our mission planner uses satellite imagery as a prior map, formulating offline a mission-level policy for global navigation of the ASV and enabling autonomous online execution via local perception and local planning modules. A significant challenge is posed by the inconsistencies in traversability estimation between satellite images and real lakes, due to environmental effects such as wind, aquatic vegetation, shallow waters, and fluctuating water levels. Hence, we specifically modelled these traversability uncertainties as stochastic edges in a graph and optimized for a mission-level policy that minimizes the expected total travel distance. To execute the policy, we propose a modern local planner architecture that processes sensor inputs and plans paths to execute the high-level policy under uncertain traversability conditions. Our system was tested on three km-scale missions on a Northern Ontario lake, demonstrating that our GPS-, vision-, and sonar-enabled ASV system can effectively execute the mission-level policy and disambiguate the traversability of stochastic edges. Finally, we provide insights gained from practical field experience and offer several future directions to enhance the overall reliability of ASV navigation systems.Comment: 33 pages, 20 figures. Project website https://pcctp.github.io. arXiv admin note: text overlap with arXiv:2209.1186

Obstacle Avoidance and Path Planning Using a Sparse Array of Sonars

This paper proposes an exploration method for robots equipped with a set of sonar sensors that does not allow for complete coverage of the robot's close surroundings. In such cases, there is a high risk of collision with possible undetected obstacles. The proposed method, adapted for use in urban outdoors environments, minimizes such risks while guiding the robot towards a predefined target location. During the process, a compact and accurate representation of the environment can be obtained