Multi-region Coverage Path Planning for Heterogeneous Unmanned Aerial Vehicles Systems

Author(s):  
Jinchao Chen ◽  
Chenglie Du ◽  
Xu Lu ◽  
Keke Chen
Drones ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 4 ◽  
Author(s):  
Tauã Cabreira ◽  
Lisane Brisolara ◽  
Paulo R. Ferreira Jr.

Coverage path planning consists of finding the route which covers every point of a certain area of interest. In recent times, Unmanned Aerial Vehicles (UAVs) have been employed in several application domains involving terrain coverage, such as surveillance, smart farming, photogrammetry, disaster management, civil security, and wildfire tracking, among others. This paper aims to explore and analyze the existing studies in the literature related to the different approaches employed in coverage path planning problems, especially those using UAVs. We address simple geometric flight patterns and more complex grid-based solutions considering full and partial information about the area of interest. The surveyed coverage approaches are classified according to a classical taxonomy, such as no decomposition, exact cellular decomposition, and approximate cellular decomposition. This review also contemplates different shapes of the area of interest, such as rectangular, concave and convex polygons. The performance metrics usually applied to evaluate the success of the coverage missions are also presented.


2020 ◽  
Author(s):  
Tauã Cabreira ◽  
Lisane Brisolara ◽  
Paulo Ferreira Jr.

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.


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