Dynamic Optimization and Heuristics Based Online Coverage Path Planning in 3D Environment for UAVs

Path planning is one of the most important issues in the robotics field, being applied in many domains ranging from aerospace technology and military tasks to manufacturing and agriculture. Path planning is a branch of autonomous navigation. In autonomous navigation, dynamic decisions about the path have to be taken while the robot moves towards its goal. Among the navigation area, an important class of problems is Coverage Path Planning (CPP). The CPP technique is associated with determining a collision-free path that passes through all viewpoints in a specific area. This paper presents a method to perform CPP in 3D environment for Unmanned Aerial Vehicles (UAVs) applications, namely 3D dynamic for CPP applications (3DD-CPP). The proposed method can be deployed in an unknown environment through a combination of linear optimization and heuristics. A model to estimate cost matrices accounting for UAV power usage is proposed and evaluated for a few different flight speeds. As linear optimization methods can be computationally demanding to be used on-board a UAV, this work also proposes a distributed execution of the algorithm through fog-edge computing. Results showed that 3DD-CPP had a good performance in both local execution and fog-edge for different simulated scenarios. The proposed heuristic is capable of re-optimization, enabling execution in environments with local knowledge of the environments.

Automated summary

Path planning is crucial in the robotics field, with Coverage Path Planning (CPP) being an important class of problems that involves determining a collision-free path passing through all viewpoints in a specific area. This paper proposes a method for 3D dynamic Coverage Path Planning (3DD-CPP) for UAV applications, utilizing a combination of linear optimization and heuristics for good performance in unknown environments. Results show that the proposed heuristic allows for re-optimization and execution in environments with local knowledge, with distributed fog-edge computing to address computational demands.