Coverage Path Planning Methods Focusing on Energy Efficient and Cooperative Strategies for Unmanned Aerial Vehicles.

Vasileios Argyriou,Thomas Lagkas,Panagiotis Sarigiannidis,Georgios Fevgas

doi:10.3390/s22031235

Abstract

The coverage path planning (CPP) algorithms aim to cover the total area of interest with minimum overlapping. The goal of the CPP algorithms is to minimize the total covering path and execution time. Significant research has been done in robotics, particularly for multi-unmanned unmanned aerial vehicles (UAVs) cooperation and energy efficiency in CPP problems. This paper presents a review of the early-stage CPP methods in the robotics field. Furthermore, we discuss multi-UAV CPP strategies and focus on energy-saving CPP algorithms. Likewise, we aim to present a comparison between energy efficient CPP algorithms and directions for future research.

Highlights

Focusing on Energy Efficient andIn recent years, due to rapid technological development, unmanned aerial vehicles (UAVs) and sensors they can carry have been developed to the extent that they can cover a wide range of applications [1]that cannot be satisfied by other types of robots [2]
We explore the limitations of the coverage path planning (CPP) methods between unmanned ground vehicles (UGV) and UAVs, the latest multi-robot and multi-UAV CPP
This paper presented a survey of coverage path planning according to the decomposition methods, such as no decomposition, exact, and approximate decomposition methods

Summary

Introduction

Focusing on Energy Efficient andIn recent years, due to rapid technological development, UAVs and sensors they can carry have been developed to the extent that they can cover a wide range of applications [1]that cannot be satisfied by other types of robots [2]. Due to rapid technological development, UAVs and sensors they can carry have been developed to the extent that they can cover a wide range of applications [1]. CPP algorithms have been developed, considering the parameters required for more efficient data retrieval from remote sensing sensors [12,13]. Algorithms have been developed that use multi-UAV to cover the area, reducing the coverage time of the area of interest [14,15]. The way to cover an area with autonomous robots differs depending on the algorithm [2,16,17]. CPP algorithms use different methods (e.g., grids, graphs, and neural networks) with calculations performed online or offline for known or unknown areas [18]

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Conclusion