Grid-Based Coverage Path Planning With Minimum Energy Over Irregular-Shaped Areas With Uavs

2019 ◽  
Author(s):  
Taua M. Cabreira ◽  
Paulo R. Ferreira ◽  
Carmelo Di Franco ◽  
Giorgio C. Buttazzo
Keyword(s):  
Path Planning ◽  
Minimum Energy ◽  
Coverage Path Planning ◽  
Grid Based

Coverage Path Planning with Proximal Policy Optimization in a Grid-based Environment

2020 ◽  
Author(s):  
Aleksandr Ianenko ◽  
Alexander Artamonov ◽  
Georgii Sarapulov ◽  
Alexey Safaraleev ◽  
Sergey Bogomolov ◽  
...  
Keyword(s):  
Path Planning ◽  
Coverage Path Planning ◽  
Policy Optimization ◽  
Grid Based

scholarly journals PPS: Energy-Aware Grid-Based Coverage Path Planning for UAVs Using Area Partitioning in the Presence of NFZs

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3742
Author(s):  
Alia Ghaddar ◽  
Ahmad Merei ◽  
Enrico Natalizio
Keyword(s):  
Minimum Energy ◽  
Energy Aware ◽  
Coverage Path Planning ◽  
Minimum Energy Consumption ◽  
Aerial Vehicle ◽  
Monitoring And Surveillance ◽  
Area Monitoring ◽  
Grid Based ◽  
Quality Of Coverage

Area monitoring and surveillance are some of the main applications for Unmanned Aerial Vehicle (UAV) networks. The scientific problem that arises from this application concerns the way the area must be covered to fulfill the mission requirements. One of the main challenges is to determine the paths for the UAVs that optimize the usage of resources while minimizing the mission time. Different approaches rely on area partitioning strategies. Depending on the size and complexity of the area to monitor, it is possible to decompose it exactly or approximately. This paper proposes a partitioning method called Parallel Partitioning along a Side (PPS). In the proposed method, grid-mapping and grid-subdivision of the area, as well as area partitioning are performed to plan the UAVs path. An extra challenge, also tackled in this work, is the presence of non-flying zones (NFZs). These zones are areas that UAVs must not cover or pass over it. The proposal is extensively evaluated, in comparison with existing approaches, to show that it enables UAVs to plan paths with minimum energy consumption, number of turns and completion time while at the same time increases the quality of coverage.

scholarly journals Energy-aware Coverage Path Planning for Unmanned Aerial Vehicles

2020 ◽  
Author(s):  
Tauã Cabreira ◽  
Lisane Brisolara ◽  
Paulo Ferreira Jr.
Keyword(s):  
Energy Consumption ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Partial Information ◽  
State Of The Art ◽  
Energy Aware ◽  
Coverage Path Planning ◽  
Aerial Vehicles ◽  
Novel Approaches ◽  
Grid Based

Coverage Path Planning (CPP) problem is a motion planning subtopic in robotics, where it is necessary to build a path for a robot to explore every location in a given scenario. Unmanned Aerial Vehicles (UAV) have been employed in several applications related to the CPP problem. However, one of the significant limitations of UAVs is endurance, especially in multi-rotors. Minimizing energy consumption is pivotal to prolong and guarantee coverage. Thus, this work proposes energy-aware coverage path planning solutions for regular and irregular-shaped areas containing full and partial information. We consider aspects such as distance, time, turning maneuvers, and optimal speed in the UAV’s energy consumption. We propose an energy-aware spiral algorithm called E-Spiral to perform missions over regular-shaped areas. Next, we explore an energy-aware grid-based solution called EG-CPP for mapping missions over irregular-shaped areas containing no-fly zones. Finally, we present an energy-aware pheromone-based solution for patrolling missions called NC-Drone. The three novel approaches successfully address different coverage path planning scenarios, advancing the state-of-the-art in this area.

scholarly journals EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs

2020 ◽  
Vol 12 (2) ◽  
pp. 29 ◽  
Author(s):  
Alia Ghaddar ◽  
Ahmad Merei
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Second Phase ◽  
Energy Aware ◽  
Coverage Path Planning ◽  
Scene Information ◽  
Two Phases ◽  
3D Scene ◽  
Top View ◽  
Grid Based

The presence of obstacles like a tree, buildings, or birds along the path of a drone has the ability to endanger and harm the UAV’s flight mission. Avoiding obstacles is one of the critical challenging keys to successfully achieve a UAV’s mission. The path planning needs to be adapted to make intelligent and accurate avoidance online and in time. In this paper, we propose an energy-aware grid based solution for obstacle avoidance (EAOA). Our work is based on two phases: in the first one, a trajectory path is generated offline using the area top-view. The second phase depends on the path obtained in the first phase. A camera captures a frontal view of the scene that contains the obstacle, then the algorithm determines the new position where the drone has to move to, in order to bypass the obstacle. In this paper, the obstacles are static. The results show a gain in energy and completion time using 3D scene information compared to 2D scene information.

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.

Sign in / Sign up

Export Citation Format

Share Document