FPS: Fast Path Planner Algorithm Based on Sparse Visibility Graph and Bidirectional Breadth-First Search

Hybrid Metric-Topological 3D Occupancy Grid Maps for Large-scale Mapping

Commonly used robot map models include occupancy grid maps, topological maps, and semantic maps. Among these, an occupancy grid map is mainly represented as a quadrilateral grid. This paper proposes an occupancy information grid for intelligent robots by exploiting the advantages of the occupancy grid map and spatial information grid. In terms of geometric structure, a regular hexagonal grid is used instead of a regular quadrilateral grid. In terms of attribute structure, the single obstacle attribute is replaced by the grid terrain characteristics, grid element attributes, and grid edge attributes. Thus, the occupancy information grid model is transformed into a new data structure describing the spatial environment, and it can be effectively applied to map construction and path planning of intelligent robots. For the map construction application of intelligent robots, this paper describes the basic process of laser sensor-based grid model construction. For the path planning application of intelligent robots, this paper extends the A* algorithm based on a regular hexagonal grid. Additionally, map construction and path planning applications for intelligent robots are experimentally verified. Several experimental results were obtained. First, the experimental results confirmed the theoretical conclusion that the minimum sampling density of the hexagonal structure was 13.4% lower than that of the quadrilateral structure. Second, the regular hexagonal grid is clearly more advantageous in describing environmental scenes, which can ameliorate the "undercompleteness" phenomenon. Third, there were large differences in the planning paths based on two types of grids, as shown by the fact that the distance of the planning paths obtained by the regular hexagonal grid was reduced by at least 10.8% and at most 15.6% compared with the regular quadrilateral grid.

<div class="section abstract"><div class="htmlview paragraph">In the domain of advanced driver assistance systems and autonomous vehicles, precise perception and interpretation of the vehicle's environment are not merely requirements they are the very foundation upon which every aspect of functionality and safety is constructed. One prevalent method of representing the environment is through the use of an occupancy grid map. This map segments the environment into distinct grid cells, each of which is evaluated to determine if it is occupied or free. This evaluation operates under the assumption that each grid cell is independent of the others. The underlying mathematical structure of this system is the binary Bayes filter (BBF). The BBF integrates sensor data from various sources and can incorporate measurements taken at different times. The occupancy grid map does not rely on the identification of individual objects, which allows it to depict obstacles of any shape. This flexibility is a key advantage of this approach. Traditional occupancy grid maps fall short when it comes to predicting dynamic environments due to their lack of a process model. A notable enhancement to this static model is the Bayesian Occupancy Filter (BOF), which, unlike its predecessor, estimates a velocity distribution for each grid cell's occupancy using a histogram filter. However, the BOF's computational demands are high. To address this, research propose representing the dynamic state of grid cells using particles. This method enables the computation of dynamic grid maps in real-time applications, even with larger grid cell sizes and higher resolution. Despite these advancements, dynamic occupancy grid maps remain a relatively new field of study, especially when compared to more established object-tracking approaches. Until now, the BOF has been treated as a distinct research area with minimal overlap with other tracking methodologies. This methodology aims to bridge that gap and foster a more integrated approach to dynamic environment estimation.</div><div class="htmlview paragraph">This study introduces a novel approach to dynamic grid mapping, conceptualized as an approximation of a Random Finite Set (RFS) filter. An RFS is a probabilistic representation of a finite, random collection of objects and their respective states. Finite Set Statistics (FISST) provide a framework for Bayesian filtering of these random finite sets and form the foundation for several multi-object tracking methodologies, such as the Probability Hypothesis Density (PHD) filter. By characterizing the grid as an RFS, we can apply sophisticated concepts from the well-established domain of RFS filtering to dynamic grid mapping. The research develops a filter known as the Probability Hypothesis Density/Multi-Instance Bernoulli (PHD/MIB) filter. This filter alternately represents and propagates the dynamic grid map as a PHD and as multiple instances of Bernoulli filters, thereby offering a more integrated and efficient approach to dynamic environment estimation.</div><div class="htmlview paragraph">Furthermore, this research introduces a Sequential Monte Carlo (SMC) implementation of the PHD/MIB filter, as well as an approximation within the Dempster-Shafer framework, termed the Dempster-Shafer PHD/MIB (DS-PHD/MIB) filter. This DS-PHD/MIB filter necessitates fewer particles than the original PHD/MIB filter, thereby enhancing computational efficiency. The study provides a comprehensive description of an efficient, massively parallel implementation of the DS-PHD/MIB filter. The algorithm's pseudo code is also outlined, offering a clear and concise understanding of its workings. This approach further strengthens the integrated and efficient methodology for dynamic environment estimation.</div><div class="htmlview paragraph">In conclusion, the research delineates the attributes of the DS-PHD/MIB filter and debates its pros and cons in comparison to object-based tracking methodologies, using practical examples for illustration. A quantitative assessment using real-world data demonstrates that the DS-PHD/MIB filter yields consistent state estimation outcomes. It effectively models both the stochastic multi-object transition process and the stochastic multi-object observation process. Moreover, the evaluation affirms the real-time capability of the parallelized implementation of the DS-PHD/MIB filter. It validates its utility for state estimation in dynamic vehicle environments, thereby underscoring its potential for practical applications in dynamic environment estimation. This comprehensive approach offers a promising avenue for future research and development in this field.</div></div>

ABSTRACTOccupancy grid mapping in the field of mobile robotics is an integrative and promising solution for the various prerequisite such as path planning, autonomous navigation, localization, SLAM (Simultaneously Localization And Mapping) etc. The reliability of the occupancy grid mapping depends upon diverse parameters such as sensor accuracy, size of a grid cell, intrinsic and extrinsic parameters, sensor registration, sensor modelling, scanning angle, ambient conditions, etc. This research reveals the uncertainty in the generation of the laser occupancy grid map with the implementation of conventional laser geometry technique when the detected obstacle/target is on the perimeter of the grid cell. During autonomous navigation, the obstacle and the mobile robot are in a dynamic state that consequence in the wrong perception of the environment by identifying the wrong grid cell as occupied in the occupancy grid map when the obstacle is on the perimeter of the grid cell. The examined error is reduced by a newly designed Perimeter-based SLAM (P-SLAM) technique based on the vector algebra, laser geometry, Inverse Sensor Model and coordinate system. The obtained results with the implementation of P-SLAM are validated w.r.t conventional approaches with qualitative and quantitative analysis by performing real-world experiments.

This manuscript looks into details into the hardware implementations of an active stereo vision path planning navigation for a mobile robotics system. This mobile robotics system has been designated as the (KSU-IMR), as it a test-bed for research within the area of mobile robotics system navigation through the use of an active stereo vision for planning a path within an unstructured environment at King Saud University in KSA. The system is making use of fast image processing tools, as in addition to fast algorithms for processing the dynamic images. The approach followed here is to employ the Epipolar Geometry to compute the 3-D details of the environment where the mobile is moving. The algorithm mixes stereo vision, occupancy grid mapping, and potential field path planning procedures to form a robu

ACA: Automatic search strategy for radioactive source

This paper proposes a method for a cleaning robot to generate a topological map from an occupancy grid-map. Virtual door is defined as the candidates of real door, and the virtual doors are detected as edges of the topological map by extracting corner features from the occupancy grid-map; using this method, an initial topological map is generated, which consists of nodes and edges. The final topological map is generated using a genetic algorithm to merge the nodes and reduce the edges. As a result, the generated topological map consists of nodes divided by virtual doors and edges located in real doors. The proposed methods provide a topological map for the user interaction and the cleaning robot, and the topological map can be used to plan more efficient motion including room-to-room path planning and covering each room.

This paper investigates the usefulness of reasoning about the uncertain presence of obstacles during path planning, which typically stems from the usage of probabilistic occupancy grid maps for representing the environment when mapping via a noisy sensor like a stereo camera. The traditional planning paradigm prescribes using a hard threshold on the occupancy probability to declare that a cell is an obstacle, and to plan a single path accordingly while treating unknown space as free. We compare this approach against a new uncertainty-aware planner, which plans two different path hypotheses and then merges their initial trajectory segments into a single one ending in a “next-best view” pose. After this informative view is taken, the planner commits to one of the hypotheses, or to a completely new one if a collision is imminent. Simulations were conducted comparing the proposed and traditional planner. Results show the existence of planning scenarios -like when the environment contains a dead-end, or when the goal is placed close to an obstacle- in which reasoning about uncertainty can significantly decrease the robot's traveled distance and increase the chances of reaching the goal. The new planner was also validated on a real Clearpath Jackal robot equipped with a ZED 2 stereo camera.

Path planning is an important area in the control of autonomous mobile robots. Recent work has focused on aspects reductions in processing time than the memory requirements. A dynamic environment uses a lot of memory and hence the processing time increases too. Our approach is to reduce the processing time by the use of a pictorial approach to reduce the number of data used. In this paper, we present a path planning approach that operates in three steps. First, a construction of the visibility tree is performed. The following treatments are not performed on the original image but on the result tree whose elements are specific points of the environment linked by the relationship of visibility. We construct thereafter a visibility graph which one seeks the shortest path. This approach has a great interest because of its fast execution speed. The path search is extended also for the case where obstacles can move. The moving obstacles may be other mobile robots whose trajectories and speeds are known initially. At the end, some applications are provided on solving similar problem such civil aviation in order to guide plane avoiding collisions.

<p>This work investigates the validity of an occupancy grid mapping inspired by human cognition and the way humans visually perceive the environment. This query is motivated by the fact that, to date, no autonomous driving system reaches the performance of an ordinary human driver. The mechanisms behind human perception could provide cues on how to improve common techniques employed in autonomous navigation - specifically the use of occupancy grids to represent the environment. We experiment with a neural network that maps an image of the scene onto an occupancy grid representation, and we show how the model benefits from two key (and yet simple) changes: 1) a different format of occupancy grid that resembles the way the brain projects the environment into a warped representation in the cortical visual area; 2) a mechanism similar to human visual attention that filters out non-relevant information from the scene. These effective expedients can potentially be applied to any autonomous driving task requiring an abstract representation of the scenario like the occupancy grids. </p>

This article is concerned with 2D indoor environment mapping produced by a sensor data fusion algorithm. A measurement subsystem assembled on a mobile robot uses its sensors to gather data from its surroundings. In this article, an algorithm is proposed and evaluated to fuse data acquired by the following low-cost sensors: a visual sensor (a wireless webcam with a laser pointer) and three range finder sensors (two infrared units and a sonar transducer). The main steps of the proposed solution are: (a) at each accurate robot pose an occupancy grid (OG) probabilistic map is generated for each type of sensor, (b) the three OG maps are merged using competitive fusion and the RANSAC algorithm is employed to extract line segments, (c) the line segments are further processed using competitive and/or complementary fusion resulting in a feature-based map for each robot pose. The previous steps are repeated until all robots pose are computed. The feature-based maps for all robot poses are then merged using competitive fusion, and a final precise OG map of the environment is generated. The proposed algorithm was evaluated using data gathered by the SLAMVITA robot sensors in two small test environments. The experimental results have shown that the proposed algorithm was able to build precise maps of the test environments (error less than 1.5 %).

Occupancy grid mapping is an important approach for intelligent vehicle environment perception. In this paper, an occupancy grid mapping approach in Dezert-Smarandache theory (DSmT) framework for the purpose of dynamic environment perception is proposed. To avoid the transformation of the local map from polar to Catersian coordinate, a different inverse sensor model in Cartesian coordinate for laser scanner was proposed. Two different combination rules in DSmT framework, Dempster’s rule of combination and PCR2, are implemented independently for global map update and mobile object detection. The performance of the two combination rules were compared by ways of simulation and experiment. According to the comparisons we find that both of the combination rules are capable of detecting mobile objects. And the former effectively filtered out the noise and make the detection robust, but the latter didn’t, suggesting that the former is more suitable for occupancy grid mapping. Static and mobile objects are extracted from the occupancy grid map using digital image processing technology. Full Text: PDF DOI: http://dx.doi.org/10.11591/ijra.v2i4.923

In this article, we describe an algorithm for acquiring occupancy grid maps with mobile robots. The standard occupancy grid mapping developed by Elfes and Moravec in the mid-1980s decomposes the high-dimensional mapping problem into many one-dimensional estimation problems, which are then tackled independently. Because of the independencies between neighboring grid cells, this often generates maps that are inconsistent with the sensor data. To overcome this, we propose a cluster that is a set of cells. The cells in the clusters are tackled dependently with another occupancy grid mapping with an expectation maximization (EM) algorithm. The occupancy grid mapping with an EM algorithm yields more consistent maps, especially in the cluster. As we use the mapping algorithm adaptively with clusters according to the sensor measurements, our mapping algorithm is faster and more accurate than previous mapping algorithms.

Occupancy grid state mapping is a key process in robotics and autonomous driving systems. It divides the environment into grid cells that contain information states. In this paper, we propose a modified SMC-BOF method to map and predict occupancy grids. The original SMC-BOF has been widely used in the occupancy grid mapping because it has lower computational costs than the BOF method. However, there are some issues related to conflicting information in dynamic situations. The original SMC-BOF cannot completely control an elongated vehicle that has conflicting information caused by the height difference between backward of vehicle and ground. To overcome this problem, we add confidence weights onto a part of the grid mapping process of the original SMC-BOF using the Linear Opinion Pool. We evaluate our method by LIDAR and stereo vision data in the KITTI dataset.

Communication is one of the most critical areas in robot operations, especially in environments unsuitable for humans, such as confined spaces. Maintaining a constant connection between robot and base ensures that these operations go as expected, reducing the risk of accidents, damage, and the need to rescue the robot. Thus, this paper proposes a path planner that ensures that the robot travels only through locations with the best radio signal. For this, we construct a graph using the information from the fusion of a radio signal map and an occupancy grid map. We then use bidirectional Dijkstra's algorithm to compare two paths computed using different metrics: one associated with a shorter distance and another aiming for radio signal quality improvement. Finally, we present the results of tests performed in a laboratory, showing the feasibility of the path planner based on the radio signal quality.