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

Abstract

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.

A Path Planning Method with Perception Optimization Based on Sky Scanning for UAVs

“…It is easy to write a program implementation in practical applications and we can obtain better planning results when combined with the planning algorithm. In the field of robot path planning [ 38 , 39 ], the grid method has been widely regarded by researchers as a typical environment modeling method. With the continuous complexity of environmental information and the continuous improvement of robot autonomy, the grid method is still being used.…”

Multi UAV Coverage Path Planning in Urban Environments

“…The first phase decomposes the search area into a graph, and the second phase uses the mixed-integer linear programming (MILP) to calculate an optimal path that minimizes the completion time. Melo et al [ 41 ] introduce a 3D dynamic algorithm for CPP capable of covering a set of 3D waypoints while allowing optimization of energy usage.…”

Path Planning Optimization for Driverless Vehicle in Parallel Parking Integrating Radial Basis Function Neural Network

“…The path planning problem is often converted to an optimization problem [10,11]. In a guaranteed collision-free trajectory planning method, a dynamic optimization problem is built, including vehicle kinematics, collision avoidance constraints, and physical restraints.…”