scholarly journals R-DFS: A Coverage Path Planning Approach Based on Region Optimal Decomposition

2021 ◽  
Vol 13 (8) ◽  
pp. 1525
Author(s):  
Gang Tang ◽  
Congqiang Tang ◽  
Hao Zhou ◽  
Christophe Claramunt ◽  
Shaoyang Men
Keyword(s):  
Path Planning ◽  
Google Earth ◽  
Simulation Experiments ◽  
Minimum Width ◽  
Coverage Path Planning ◽  
Optimal Paths ◽  
Planning Approach ◽  
Polygonal Area ◽  
Aerial Vehicle ◽  
Working Distance

Most Coverage Path Planning (CPP) strategies based on the minimum width of a concave polygonal area are very likely to generate non-optimal paths with many turns. This paper introduces a CPP method based on a Region Optimal Decomposition (ROD) that overcomes this limitation when applied to the path planning of an Unmanned Aerial Vehicle (UAV) in a port environment. The principle of the approach is to first apply a ROD to a Google Earth image of a port and combining the resulting sub-regions by an improved Depth-First-Search (DFS) algorithm. Finally, a genetic algorithm determines the traversal order of all sub-regions. The simulation experiments show that the combination of ROD and improved DFS algorithm can reduce the number of turns by 4.34%, increase the coverage rate by more than 10%, and shorten the non-working distance by about 29.91%. Overall, the whole approach provides a sound solution for the CPP and operations of UAVs in port environments.

scholarly journals Modified A-Star Algorithm for Efficient Coverage Path Planning in Tetris Inspired Self-Reconfigurable Robot with Integrated Laser Sensor

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2585 ◽  
Author(s):  
Anh Le ◽  
Veerajagadheswar Prabakaran ◽  
Vinu Sivanantham ◽  
Rajesh Mohan
Keyword(s):  
Path Planning ◽  
Laser Sensor ◽  
Coverage Area ◽  
Operation System ◽  
Coverage Path Planning ◽  
Cleaning Robot ◽  
Simulated Environment ◽  
Planning Approach ◽  
Reconfigurable Robot ◽  
Set Up

Advancing an efficient coverage path planning in robots set up for application such as cleaning, painting and mining are becoming more crucial. Such drive in the coverage path planning field proposes numerous techniques over the past few decades. However, the proposed approaches were only applied and tested with a fixed morphological robot in which the coverage performance was significantly degraded in a complex environment. To this end, an A-star based zigzag global planner for a novel self-reconfigurable Tetris inspired cleaning robot (hTetro) presented in this paper. Unlike the traditional A-star algorithm, the presented approach can generate waypoints in order to cover the narrow spaces while assuming appropriate morphology of the hTtero robot with the objective of maximizing the coverage area. We validated the efficiency of the proposed planning approach in the Robot Operation System (ROS) Based simulated environment and tested with the hTetro robot in real-time under the controlled scenarios. Our experiments demonstrate the efficiency of the proposed coverage path planning approach resulting in superior area coverage performance in all considered experimental scenarios.

Path Planning Approach for a Quadrotor Unmanned Aerial Vehicle

2021 ◽  
pp. 426-439
Author(s):  
César A. Cárdenas R. ◽  
V. Landero ◽  
Ramón E. R. González ◽  
Paola Ariza-Colpas ◽  
Emiro De-la-Hoz-Franco ◽  
...  
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Planning Approach ◽  
Aerial Vehicle

Complete coverage path planning for aerial vehicle flocks deployed in outdoor environments

2019 ◽  
Vol 75 ◽  
pp. 189-201 ◽  
Author(s):  
Dario Calogero Guastella ◽  
Luciano Cantelli ◽  
Giuseppe Giammello ◽  
Carmelo Donato Melita ◽  
Gianluca Spatino ◽  
...  
Keyword(s):  
Path Planning ◽  
Complete Coverage ◽  
Coverage Path Planning ◽  
Outdoor Environments ◽  
Aerial Vehicle

scholarly journals LEO: Liquid Exploration Online

2020 ◽  
Vol 2 (1) ◽  
pp. 58-80
Author(s):  
Frank Hoeller
Keyword(s):  
Path Planning ◽  
State Of The Art ◽  
Simulation Experiments ◽  
Coverage Path Planning ◽  
Current State ◽  
Extensive Evaluation ◽  
Novel Approach ◽  
Local Navigation ◽  
Local Decision ◽  
Real Robot

This article introduces a novel approach to the online complete- coverage path planning (CCPP) problem that is specically tailored to the needs of skid-steer tracked robots. In contrast to most of the current state-of-the-art algorithms for this task, the proposed algorithm reduces the number of turning maneuvers, which are responsible for a large part of the robot's energy consumption. Nevertheless, the approach still keeps the total distance traveled at a competitive level. The algorithm operates on a grid-based environment representation and uses a 3x3 prioritization matrix for local navigation decisions. This matrix prioritizes cardinal di- rections leading to a preference for straight motions. In case no progress can be achieved based on a local decision, global path planning is used to choose a path to the closest known unvisited cell, thereby guaranteeing completeness of the approach. In an extensive evaluation using simulation experiments, we show that the new algorithm indeed generates competi- tively short paths with largely reduced turning costs, compared to other state-of-the-art CCPP algorithms. We also illustrate its performance on a real robot.

A Balanced Shadow-Following Coverage Path Planning Approach under Energy Constraints

2021 ◽  
Author(s):  
Huan Wang ◽  
Dianxi Shi ◽  
Yunlong Wu ◽  
Lin Li ◽  
Ning Li ◽  
...  
Keyword(s):  
Path Planning ◽  
Coverage Path Planning ◽  
Planning Approach ◽  
Energy Constraints

Comparison of 3D Versus 4D Path Planning for Unmanned Aerial Vehicles

2016 ◽  
Vol 66 (6) ◽  
pp. 651 ◽  
Author(s):  
Halil Cicibas ◽  
Kadir Alpaslan Demir ◽  
Nafiz Arica
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Dynamic Environments ◽  
Research Model ◽  
Time Dimension ◽  
Complex Dynamic ◽  
Aerial Vehicles ◽  
Planning Approach ◽  
Spatial Dimensions ◽  
Aerial Vehicle

<p>This research compares 3D versus 4D (three spatial dimensions and the time dimension) multi-objective and multi-criteria path-planning for unmanned aerial vehicles in complex dynamic environments. In this study, we empirically analyse the performances of 3D and 4D path planning approaches. Using the empirical data, we show that the 4D approach is superior over the 3D approach especially in complex dynamic environments. The research model consisting of flight objectives and criteria is developed based on interviews with an experienced military UAV pilot and mission planner to establish realism and relevancy in unmanned aerial vehicle flight planning. Furthermore, this study incorporates one of the most comprehensive set of criteria identified during our literature search. The simulation results clearly show that the 4D path planning approach is able to provide solutions in complex dynamic environments in which the 3D approach could not find a solution.</p>

scholarly journals An Arable Field for Benchmarking of Metaheuristic Algorithms for Capacitated Coverage Path Planning Problems

Agronomy ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 1454 ◽  
Author(s):  
Erfan Khosravani Moghadam ◽  
Mahdi Vahdanjoo ◽  
Allan Leck Jensen ◽  
Mohammad Sharifi ◽  
Claus Aage Grøn Sørensen
Keyword(s):  
Path Planning ◽  
Optimal Solution ◽  
Solution Space ◽  
Metaheuristic Algorithms ◽  
Long Distance ◽  
Coverage Path Planning ◽  
Metaheuristic Methods ◽  
Efficient Route ◽  
The Absolute ◽  
Working Distance

This study specifies an agricultural field (Latitude = 56°30′0.8″ N, Longitude = 9°35′27.88″ E) and provides the absolute optimal route for covering that field. The calculated absolute optimal solution for this field can be used as the basis for benchmarking of metaheuristic algorithms used for finding the most efficient route in the field. The problem of finding the most efficient route that covers a field can be formulated as a Traveling Salesman Problem (TSP), which is an NP-hard problem. This means that the optimal solution is infeasible to calculate, except for very small fields. Therefore, a range of metaheuristic methods has been developed that provide a near-optimal solution to a TSP in a “reasonable” time. The main challenge with metaheuristic methods is that the quality of the solutions can normally not be compared to the absolute optimal solution since this “ground truth” value is unknown. Even though the selected benchmarking field requires only eight tracks, the solution space consists of more than 1.3 billion solutions. In this study, the absolute optimal solution for the capacitated coverage path planning problem was determined by calculating the non-working distance of the entire solution space and determining the solution with the shortest non-working distance. This was done for four scenarios consisting of low/high bin capacity and short/long distance between field and storage depot. For each scenario, the absolute optimal solution and its associated cost value (minimum non-working distance) were compared to the solutions of two metaheuristic algorithms; Simulated Annealing Algorithm (SAA) and Ant Colony Optimization (ACO). The benchmarking showed that neither algorithm could find the optimal solution for all scenarios, but they found near-optimal solutions, with only up to 6 pct increasing non-working distance. SAA performed better than ACO, concerning quality, stability, and execution time.

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