Sparse 3D Point-cloud Map Upsampling and Noise Removal as a vSLAM Post-processing Step: Experimental Evaluation

The monocular vision-based simultaneous localization and mapping (vSLAM) is one of the most challenging problem in mobile robotics and computer vision. In this work we study the post-processing techniques applied to sparse 3D point-cloud maps, obtained by feature-based vSLAM algorithms. Map post-processing is split into 2 major steps: 1) noise and outlier removal and 2) upsampling. We evaluate different combinations of known algorithms for outlier removing and upsampling on datasets of real indoor and outdoor environments and identify the most promising combination. We further use it to convert a point-cloud map, obtained by the real UAV performing indoor flight to 3D voxel grid (octo-map) potentially suitable for path planning.Comment: 10 pages, 4 figures, camera-ready version of paper for "The 3rd International Conference on Interactive Collaborative Robotics (ICR 2018)

Applying MAPP Algorithm for Cooperative Path Finding in Urban Environments

The paper considers the problem of planning a set of non-conflict trajectories for the coalition of intelligent agents (mobile robots). Two divergent approaches, e.g. centralized and decentralized, are surveyed and analyzed. Decentralized planner - MAPP is described and applied to the task of finding trajectories for dozens UAVs performing nap-of-the-earth flight in urban environments. Results of the experimental studies provide an opportunity to claim that MAPP is a highly efficient planner for solving considered types of tasks

Аналіз та можливість модифікації наявних алгоритмів пошуку оптимального шляху на квадратній сітці з використанням методів паралельного програмування

The task about unmanned aerial vehicle optimal path planning between two points on continuous terrain, which has obstacles, was set. Advantages and disadvantages of existing methods of continuous terrain discretization, application of which required for solution the task using computer, were considered. As the most optimal method of continuous terrain discretization the method of square grid was chosen. As selection criteria of the most optimal among existing path planning algorithms on square grid, the alteration angle was accepted. This criteria was suggested because of physical constraints of unmanned aerial vehicle during maneuvers on continuous terrain. According to the chosen criteria, review and analysis of the most widespread path planning algorithms on square grid, which are varieties of А* algorithm, was carrying out, including the short description of the principle of their work and data structures they use. As the most optimal path planning algorithm, which satisfies a given criteria, the LIAN algorithm was chosen. During testing the LIAN implementation in Delphi programming language were discovered disadvantages of this algorithm, and offered possible variants of their solution. Given that proposed and possible further modifications of LIAN will improve qualitative characteristics of founded path, and also will increase its execution time, LIAN algorithm was analyzed on the possibility of its modification using parallel programming methods. Was offer the scheme of work of parallel variant of LIAN algorithm, in which this algorithm will be divided into two parts: parallel part, which will perform integral subtask of the algorithm, and which can be implemented as an instance of one of the parallel threads, and synchronized part, which will be implemented as a main thread. In the context of reliability of software, which will be implement the parallel variant of LIAN algorithm, was determined, to which data structures, which use original LIAN algorithm, can access the synchronized part of new algorithm, and to which can access the parallel part. The specific variants of LIAN algorithm modifications, that use the parallel programming methods, which are planned in the future to implement and research on efficiency and reliability of execution, were offered.Сформирована постановка задачи поиска оптимального пути на непрерывной местности путем её дискретизации с помощью квадратной сетки. Проведен обзор и анализ самых распространенных алгоритмов планирования пути на квадратной сетке, которые представляют собой разновидности алгоритма А*. В качестве алгоритма поиска оптимального пути на квадратной сетке, который учитывает физические ограничения беспилотного летательного аппарата, был выбран алгоритм LIAN. Были обнаружены недостатки алгоритма LIAN и возможные пути их решения, а также предложены варианты его модификации с использованием методов параллельного программирования.Сформована постановка задачі пошуку оптимального шляху на неперервній місцевості шляхом її дискретизації за допомогою квадратної сітки. Проведений огляд та аналіз найпоширеніших алгоритмів планування шляху на квадратній сітці, які є різновидами алгоритму А*. У якості алгоритму пошуку оптимального шляху на квадратній сітці, що враховує фізичні обмеження безпілотного літального апарату, був обраний алгоритм LIAN. Були виявлені недоліки алгоритму LIAN та можливі шляхи їх вирішення, а також запропоновані варіанти його модифікації з використанням методів паралельного програмування

Path Planning for Robot and Pedestrian Simulations

The thesis is divided into two parts. The first part presents a new proposed method for solving the path planning problem to find an optimal collision-free path between the starting and the goal points in a static environment. Initially, the grid model of the robot's working environment is constructed. Next, each grid cell's potential value in the working environment is calculated based on the proposed potential function. This function guides the robot to move toward the desired goal location, it has the lowest value at the goal location, and the value increase as the robot moves further away. Next, a new method, called Boundary Node Method (BNM), is proposed to find the initial feasible path. In this method, the robot is simulated by a nine-node quadrilateral element, where the centroid node represents the robot's position. The robot moves in the working environment toward the goal point with eight-boundary nodes based on the boundary nodes' characteristics. In the BNM method, the initial feasible path is generated from the sequence of the waypoints that the robot has to traverse as it moves toward the goal point without colliding with obstacles. The BNM method can generate the path safely and efficiently. However, the path is not optimal in terms of the total path length. An additional method, called Path Enhancement Method (PEM), is proposed to construct an optimal or near-optimal collision-free path. The generated path obtained by BNM and PEM may contain sharp turns. Therefore, the cubic spline interpolation is used to create a continuous smooth path that connects the starting point to the goal point. The performance of the proposed method is compared with the other path planning methods in terms of path length and computational time. Moreover, the multi-goal path planning problem is investigated to find the shortest collision-free path connecting a given set of goal points in the robot working environment. Furthermore, to verify the performance of the proposed method, several experimental tests have been performed on the e-puck robot with different obstacle configurations and various positions of goal points. The experimental results showed that the proposed method could construct the shortest collision-free path and direct the real physical robot to the final destination point. At the end of the first part of the thesis, we investigate the multi-goal path planning problem for the multi-robot system such that several robots reach each goal. In the second part of this thesis, we proposed a new method for simulating pedestrian crowd movement in a virtual environment. The first part of this thesis concerning the generation of the shortest collision-free path is used. In this method, we assumed that the crowd consists of multiple groups with a different number and various types of pedestrians. In this scenario, each group's intention is different for visiting several goal points with varying sequences of the visit. The proposed method uses the multi-group microscopic model to generate a real-time trajectory for each pedestrian navigating in the pedestrianized area of the virtual environment. Additionally, an agent-based model is introduced to simulate pedestrian' behaviours. Based on the proposed method, every single pedestrian in each group can continuously adjust their attributes, such as position, velocity, etc. Moreover, pedestrians optimize their path independently toward the desired goal points while avoiding obstacles and other pedestrians in the scene. At the end of this part of the thesis, a statistical analysis is carried out to evaluate the performance of the proposed method for simulating the crowd movement in the virtual environment. The proposed method implemented for several simulation scenarios under a variety of conditions for a wide range of different parameters. The results showed that the proposed method is capable of describing pedestrian' behaviours in the virtual environment