scholarly journals Improved A-Star Algorithm for Long-Distance Off-Road Path Planning Using Terrain Data Map

2021 ◽  
Vol 10 (11) ◽  
pp. 785
Author(s):  
Zhonghua Hong ◽  
Pengfei Sun ◽  
Xiaohua Tong ◽  
Haiyan Pan ◽  
Ruyan Zhou ◽  
...  
Keyword(s):  
Data Structure ◽  
Path Planning ◽  
Optimal Path ◽  
Long Distance ◽  
Processing Efficiency ◽  
Maximum Acceleration ◽  
Processing Times ◽  
Digital Elevation ◽  
Feasible Path ◽  
Elevation Model

To overcome the limitation of poor processing times for long-distance off-road path planning, an improved A-Star algorithm based on terrain data is proposed in this study. The improved A-Star algorithm for long-distance off-road path planning tasks was developed to identify a feasible path between the start and destination based on a terrain data map generated using a digital elevation model. This study optimised the algorithm in two aspects: data structure, retrieval strategy. First, a hybrid data structure of the minimum heap and 2D array greatly reduces the time complexity of the algorithm. Second, an optimised search strategy was designed that does not check whether the destination is reached in the initial stage of searching for the global optimal path, thus improving execution efficiency. To evaluate the efficiency of the proposed algorithm, three different off-road path planning tasks were examined for short-, medium-, and long-distance path planning tasks. Each group of tasks corresponded to three different off-road vehicles, and nine groups of experiments were conducted. The experimental results show that the processing efficiency of the proposed algorithm is significantly better than that of the conventional A-Star algorithm. Compared with the conventional A-Star algorithm, the path planning efficiency of the improved A-Star algorithm was accelerated by at least 4.6 times, and the maximum acceleration reached was 550 times for long-distance off-road path planning. The simulation results show that the efficiency of long-distance off-road path planning was greatly improved by using the improved algorithm.

scholarly journals Reconstructing Pre-Industrial Long Distance Roads in a Hilly Region in Germany, Based on Historical and Archaeological Data

2017 ◽  
Vol 1 (2) ◽  
pp. 642-660 ◽  
Author(s):  
Irmela Herzog
Keyword(s):  
Cross Sections ◽  
Lidar Data ◽  
Long Distance ◽  
Early 19Th Century ◽  
Digital Elevation ◽  
Hilly Region ◽  
Local Accuracy ◽  
Cost Paths ◽  
Elevation Model ◽  
The One

The aim of this contribution is on the one hand to map pre-industrial long distance roads located in a hilly region east of Cologne, Germany, as exactly as possible and on the other hand to assess the accuracy of least-cost approaches that are increasingly applied by archaeologists for prehistoric road reconstruction. Probably the earliest map covering the study area east of Cologne dates back to 1575. The map is distorted so that rectification is difficult. But it is possible to assess the local accuracy of the map and to transfer the approximate routes to a modern map manually. Most of the area covered by the 1575 map is also depicted on a set of more accurate maps created in the early 19th century and a somewhat later historical map set (ca. 1842 AD). The historical roads on these rectified historical maps close to the approximate roads were digitized and compared to the outcomes of least-cost analysis, specifically least-cost paths and accessibility maps. Based on these route reconstructions with limited accuracy, Lidar data is checked to identify remains of these roads. Several approaches for visualizing Lidar data are tested to identify appropriate methods for detecting sunken roads. Possible sunken roads detected on the Lidar images were validated by checking cross sections in the digital elevation model and in the field.

scholarly journals Assessing Optimal Digital Elevation Model Selection for Active River Area Delineation Across Broad Regions

2021 ◽  
Author(s):  
Shizhou Ma ◽  
Karen Beazley ◽  
Patrick Nussey ◽  
Chris Greene
Keyword(s):  
High Resolution ◽  
Digital Elevation Model ◽  
Smoothing Algorithm ◽  
Processing Efficiency ◽  
Spatial Approach ◽  
Digital Elevation ◽  
Elevation Data ◽  
Computer Based ◽  
Lidar Dem ◽  
Elevation Model

Abstract The Active River Area (ARA) is a spatial approach for identifying the extent of functional riparian area. Given known limitations in terms of input elevation data quality and methodology, ARA studies to date have not achieved effective computer-based ARA-component delineation, limiting the efficacy of the ARA framework in terms of informing riparian conservation and management. To achieve framework refinement and determine the optimal input elevation data for future ARA studies, this study tested a novel Digital Elevation Model (DEM) smoothing algorithm and assessed ARA outputs derived from a range of DEMs for accuracy and efficiency. It was found that the tested DEM smoothing algorithm allows the ARA framework to take advantage of high-resolution LiDAR DEM and considerably improves the accuracy of high-resolution LiDAR DEM derived ARA results; smoothed LiDAR DEM in 5-meter spatial resolution best balanced ARA accuracy and data processing efficiency and is ultimately recommended for future ARA delineations across large regions.

Analysis of Constructing 3-Dimensional Virtual Scene Technique Based on DEM

2011 ◽  
Vol 271-273 ◽  
pp. 404-409
Author(s):  
Xiao Gen Li ◽  
Zhi Quan Huang ◽  
Tong Jiang ◽  
An Ming Wang
Keyword(s):  
High Resolution ◽  
Spatial Data ◽  
Geographical Information ◽  
Long Distance ◽  
Virtual Scene ◽  
3 Dimensional ◽  
Reservoir Volume ◽  
Factors Analysis ◽  
Digital Elevation ◽  
Elevation Model

High resolution DEM(Digital Elevation Model, DEM) is created based on original CAD terrain map (one reservoir as example). Then the article offers precision analysis、topographic factors analysis、visibility analysis、reservoir volume and submerging acreage computing. Then adopting GeoVRML technique is to implement the functions of WebGIS 、visualization and query of computing result、graph data visualization and reservoir region virtual scene roaming etc. The system implements deep administrative levels information diged and long-distance visualization expression. The result shows on the basic high resolution DEM to realize 3-Dimensional Visualization Analyse and calculation functions and compress the spatial data to release these data in the WebGIS(Web Geographical Information System, WebGIS) as well as.

scholarly journals Reactive Path Planning Approach for Docking Robots in Unknown Environment

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Peng Cui ◽  
Weisheng Yan ◽  
Yintao Wang
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Autonomous Robots ◽  
Planning Problem ◽  
Long Distance ◽  
Unknown Environment ◽  
Work Area ◽  
Planning Approach ◽  
Feasible Path

Autonomous robots need to be recharged and exchange information with the host through docking in the long-distance tasks. Therefore, feasible path is required in the docking process to guide the robot and adjust its pose. However, when there are unknown obstacles in the work area, it becomes difficult to determine the feasible path for docking. This paper presents a reactive path planning approach named Dubins-APF (DAPF) to solve the path planning problem for docking in unknown environment with obstacles. In this proposed approach the Dubins curves are combined with the designed obstacle avoidance potential field to plan the feasible path. Firstly, an initial path is planned and followed according to the configurations of the robot and the docking station. Then when the followed path is evaluated to be infeasible, the intermediate configuration is calculated as well as the replanned path based on the obstacle avoidance potential field. The robot will be navigated to the docking station with proper pose eventually via the DAPF approach. The proposed DAPF approach is efficient and does not require the prior knowledge about the environment. Simulation results are given to validate the effectiveness and feasibility of the proposed approach.

Multi-scale theta* algorithm for the path planning of unmanned surface vehicle

2021 ◽  
pp. 147509022110396
Author(s):  
Xu Han ◽  
Xianku Zhang
Keyword(s):  
Path Planning ◽  
Planning Process ◽  
Optimal Path ◽  
Long Distance ◽  
Unmanned Surface Vehicle ◽  
Safe Navigation ◽  
Multi Scale ◽  
Fold Reduction ◽  
Planning Algorithm ◽  
Path Planning Algorithm

Theta* algorithm is a searching-based path planning algorithm that gives an optimal path with more flexibility on route angle than A* method. The dynamics of USV is characterized by large inertia, so that larger turning angle is preferred. In view of the shortcomings of traditional Theta* algorithm, such as being hard to balance overall situation and details in long-distance planning, and lacking of waypoint replacement scheme when a waypoint is unreachable, it is inappropriate to make long-distance planning through Theta* algorithm directly. In order to ensure safe navigation for unmanned surface vehicle (USV), this paper proposes a multi-scale Theta* algorithm to solve these defects. Simulation result manifests the proposed scheme can provide a path clear of obstacles with several fold reduction in time consumption. The proposed planning method simplifies path planning process and contributes to the development of marine transportation.

scholarly journals Path Planning Followed by Kinodynamic Smoothing for Multirotor Aerial Vehicles (MAVs)

2021 ◽  
Vol 17 (4) ◽  
pp. 491-505
Author(s):  
G. Kulathunga ◽  
◽  
D. Devitt ◽  
R. Fedorenko ◽  
A. Klimchik ◽  
...  
Keyword(s):  
Path Planning ◽  
Motion Planning ◽  
Free Path ◽  
Optimal Path ◽  
Optimal Solution ◽  
Search Space ◽  
Planning Algorithm ◽  
Feasible Path ◽  
Straight Lines ◽  
Path Planning Algorithm

Any obstacle-free path planning algorithm, in general, gives a sequence of waypoints that connect start and goal positions by a sequence of straight lines, which does not ensure the smoothness and the dynamic feasibility to maneuver the MAV. Kinodynamic-based motion planning is one of the ways to impose dynamic feasibility in planning. However, kinodynamic motion planning is not an optimal solution due to high computational demands for real-time applications. Thus, we explore path planning followed by kinodynamic smoothing while ensuring the dynamic feasibility of MAV. The main difference in the proposed technique is not to use kinodynamic planning when finding a feasible path, but rather to apply kinodynamic smoothing along the obtained feasible path. We have chosen a geometric-based path planning algorithm “RRT*” as the path finding algorithm. In the proposed technique, we modified the original RRT* introducing an adaptive search space and a steering function that helps to increase the consistency of the planner. Moreover, we propose a multiple RRT* that generates a set of desired paths. The optimal path from the generated paths is selected based on a cost function. Afterwards, we apply kinodynamic smoothing that will result in a dynamically feasible as well as obstacle-free path. Thereafter, a b-spline-based trajectory is generated to maneuver the vehicle autonomously in unknown environments. Finally, we have tested the proposed technique in various simulated environments. According to the experiment results, we were able to speed up the path planning task by 1.3 times when using the proposed multiple RRT* over the original RRT*.

Constrained coverage path planning: evolutionary and classical approaches

Robotica ◽  
2018 ◽  
Vol 36 (6) ◽  
pp. 904-924 ◽  
Author(s):  
S. M. Ahmadi ◽  
H. Kebriaei ◽  
H. Moradi
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Artificial Potential Field ◽  
Coverage Path Planning ◽  
Planning Algorithm ◽  
Aerial Vehicle ◽  
Adaptive Operator Selection ◽  
Coverage Function ◽  
Feasible Path ◽  
Path Planning Algorithm

SUMMARYThe constrained coverage path planning addressed in this paper refers to finding an optimal path traversed by a unmanned aerial vehicle (UAV) to maximize its coverage on a designated area, considering the time limit and the feasibility of the path. The UAV starts from its current position to assess the condition of a new entry to the area. Nevertheless, the UAV needs to comply with the coverage task, simultaneously and therefore, it is likely that the optimal policy would not be the shortest path in such a condition, since a wider area can be covered through a longer path. From the other side, along with a longer path, the UAV may not reach to the target in due time. In addition, the speed of UAV is assumed to be constant and as a result, a feasible path needs to be smooth enough to support this assumption. The problem is modeled as an Epsilon-constraint optimization in which a coverage function has to be maximized, considering the constraints on the length and the smoothness of the path. For this purpose, a new genetic path planning algorithm with adaptive operator selection is proposed to solve such a complicated constrained optimization problem. The proposed approach has been compared to some classical approaches like, a modified version of the Artificial Potential Field and a modified version of Dijkstra's algorithm (a graph-based approach). All the methods are implemented and tested in different scenarios and their performances are evaluated via the simulation results.

scholarly journals A Fuzzy Gain-Based Dynamic Ant Colony Optimization for Path Planning in Dynamic Environments

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 280
Author(s):  
Viswanathan Sangeetha ◽  
Raghunathan Krishankumar ◽  
Kattur Soundarapandian Ravichandran ◽  
Fausto Cavallaro ◽  
Samarjit Kar ◽  
...  
Keyword(s):  
Path Planning ◽  
Ant Colony Optimization ◽  
Performance Metrics ◽  
Optimal Path ◽  
Unmanned Vehicles ◽  
Ant Colony ◽  
Computational Overhead ◽  
Dynamic Scenario ◽  
Occupancy Grids ◽  
Feasible Path

Path planning can be perceived as a combination of searching and executing the optimal path between the start and destination locations. Deliberative planning capabilities are essential for the motion of autonomous unmanned vehicles in real-world scenarios. There is a challenge in handling the uncertainty concerning the obstacles in a dynamic scenario, thus requiring an intelligent, robust algorithm, with the minimum computational overhead. In this work, a fuzzy gain-based dynamic ant colony optimization (FGDACO) for dynamic path planning is proposed to effectively plan collision-free and smooth paths, with feasible path length and the minimum time. The ant colony system’s pheromone update mechanism was enhanced with a sigmoid gain function for effective exploitation during path planning. Collision avoidance was achieved through the proposed fuzzy logic control. The results were validated using occupancy grids of variable size, and the results were compared against existing methods concerning performance metrics, namely, time and length. The consistency of the algorithm was also analyzed, and the results were statistically verified.

Path Planning for Rolling Locomotion of Polyhedral Tensegrity Robots Based on Dijkstra Algorithm

2019 ◽  
Vol 60 (4) ◽  
pp. 273-286 ◽  
Author(s):  
Yipeng Lu ◽  
Xian Xu ◽  
Yaozhi Luo
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Experimental Result ◽  
Dijkstra Algorithm ◽  
Long Distance ◽  
Numerical Result ◽  
Planning Approach ◽  
The Cost ◽  
Rolling Locomotion ◽  
Terrain Characteristics

Tensegrity-based locomotive robots have attracted more and more interests from multidisciplinary engineering community. To realize long distance locomotion for tensegrity robots in a given land, path planning is usually needed. This paper proposes a path planning approach for rolling locomotion of polyhedral tensegrity robots. Given the start vertex, target vertex and the directed graph G which indicates the possible paths, the optimal path with lowest cost can be found by Dijkstra algorithm. Numerical and experimental examples are carried out with a six-bar tensegrity robot prototype. Both motion distance and terrain characteristics are considered within the cost. The proposed approach is generally verified by the examples. A comparison between the numerical result and the experimental result is also presented.

Path Planning Aware of Robot’s Center of Mass for Steep Slope Vineyards

Robotica ◽  
2019 ◽  
Vol 38 (4) ◽  
pp. 684-698 ◽  
Author(s):  
Luís Santos ◽  
Filipe Santos ◽  
Jorge Mendes ◽  
Pedro Costa ◽  
José Lima ◽  
...  
Keyword(s):  
Path Planning ◽  
Digital Elevation Model ◽  
Autonomous Navigation ◽  
Intelligent Systems ◽  
Center Of Mass ◽  
Real Data ◽  
Steep Slope ◽  
Grid Map ◽  
Digital Elevation ◽  
Elevation Model

SummarySteep slope vineyards are a complex scenario for the development of ground robots. Planning a safe robot trajectory is one of the biggest challenges in this scenario, characterized by irregular surfaces and strong slopes (more than 35°). Moving the robot through a pile of stones, spots with high slope or/and with wrong robot yaw may result in an abrupt fall of the robot, damaging the equipment and centenary vines, and sometimes imposing injuries to humans. This paper presents a novel approach for path planning aware of center of mass of the robot for application in sloppy terrains. Agricultural robotic path planning (AgRobPP) is a framework that considers the A* algorithm by expanding inner functions to deal with three main inputs: multi-layer occupation grid map, altitude map and robot’s center of mass. This multi-layer grid map is updated by obstacles taking into account the terrain slope and maximum robot posture. AgRobPP is also extended with algorithms for local trajectory replanning during the execution of a trajectory that is blocked by the presence of an obstacle, always assuring the safety of the re-planned path. AgRobPP has a novel PointCloud translator algorithm called PointCloud to grid map and digital elevation model (PC2GD), which extracts the occupation grid map and digital elevation model from a PointCloud. This can be used in AgRobPP core algorithms and farm management intelligent systems as well. AgRobPP algorithms demonstrate a great performance with the real data acquired from AgRob V16, a robotic platform developed for autonomous navigation in steep slope vineyards.

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