A Framework for Coverage Path Planning Optimization Based on Point Cloud for Structural Inspection

Research on route planning for solar UAV based on the intelligent optimization algorithm

The solar unmanned aerial vehicle (UAV) route planning needs to comprehensively consider the conversion efficiency of solar cells under the influence of solar ground reflection radiation and sky scattering radiation. On the one hand, it is necessary to consider the cost of radar threat, mileage energy consumption, mountain impact and other costs. On the other hand, it is also necessary to consider the influence of high threat, mountain shadow occlusion cost, and cloud shading cost on solar photovoltaic conversion efficiency. The above problem was solved through using the ant colony intelligent optimization algorithm. By constructing ant colony paths rationally, models of mountain impact cost, high threat, mountain shadow shelter cost, and cloud shading cost were established. The constraints such as the maximum action distance, solar irradiation angle and effective action distance of various costs were introduced into the cost model and exploration factor calculation, and the comprehensive optimization problem of solar UAV route was solved. Finally, the simulation results show that the algorithm path structure is reasonable; the target node can be found independently; the convergence speed can meet the requirements of route planning; the generated route cost is small; the algorithm is reasonable and effective.

Path planning optimization in unmanned aerial vehicles using meta-heuristic algorithms: a systematic review

Unmanned aerial vehicles (UAVs) have recently been increasingly popular in various areas, fields, and applications. Military, disaster management, rescue operations, public services, agriculture, and various other areas are examples. As a result, UAV path planning is concerned with determining the optimal path from the source to the destination while avoiding collisions with lowering the cost of time, energy, and other resources. This review aims to assort academic studies on the path planning optimization in UAV using meta-heuristic algorithms, summarize the results of each optimization algorithm, and extend the understanding of the current state of the path planning in UAV in the meta-heuristic optimization field. For this purpose, we implemented a broad, automated search using Boolean and snowballing searching methods to find academic works on path planning in UAVs. Studies and papers have been distinguished, and the following information was obtained and aggregated from each article: authors, publication's year, the journal name or the conference name, proposed algorithms, the aim of the study, the outcome, and the quality of each study. According to the findings, the meta-heuristic algorithm is a standard optimization method for tackling single and multi-objective problems. Besides, the findings show that meta-heuristic algorithms have a great compact on the path planning optimization in UAVs, and there is good progress in this field. However, the problem still exists mainly in complex and dynamic environments, on battlefields, in rescue missions, mobile obstacles, and with multiple UAVs.

An Edge-Fog Architecture for Distributed 3D Reconstruction and Remote Monitoring of a Power Plant Site in the Context of 5G

It is well known that power plants worldwide present access to difficult and hazardous environments, which may cause harm to on-site employees. The remote and autonomous operations in such places are currently increasing with the aid of technology improvements in communications and processing hardware. Virtual and augmented reality provide applications for crew training and remote monitoring, which also rely on 3D environment reconstruction techniques with near real-time requirements for environment inspection. Nowadays, most techniques rely on offline data processing, heavy computation algorithms, or mobile robots, which can be dangerous in confined environments. Other solutions rely on robots, edge computing, and post-processing algorithms, constraining scalability, and near real-time requirements. This work uses an edge-fog computing architecture for data and processing offload applied to a 3D reconstruction problem, where the robots are at the edge and computer nodes at the fog. The sequential processes are parallelized and layered, leading to a highly scalable approach. The architecture is analyzed against a traditional edge computing approach. Both are implemented in our scanning robots mounted in a real power plant. The 5G network application is presented along with a brief discussion on how this technology can benefit and allow the overall distributed processing. Unlike other works, we present real data for more than one proposed robot working in parallel on site, exploring hardware processing capabilities and the local Wi-Fi network characteristics. We also conclude with the required scenario for the remote monitoring to take place with a private 5G network.

Coverage path planning for spraying drones

The pandemic by COVID-19 is causing a devastating effect on the health of the global population. Currently, there are several efforts to prevent the spread of the virus. Among those efforts, cleaning and disinfecting public areas have become important tasks and they should be automated in future smart cities. To contribute in this direction, this paper proposes a coverage path planning method for a spraying drone, an unmanned aerial vehicle that has mounted a sprayer/sprinkler system, that can disinfect areas. State-of-the-art planners consider a camera instead of a sprinkler, in consequence, the expected coverage will differ in running time because the liquid dispersion is different from a camera's projection model. In addition, current planners assume that the vehicles can fly outside the target region; this assumption can not be satisfied in our problem, because disinfections are performed at low altitudes. Our method presents i) a new sprayer/sprinkler model that fits a more realistic coverage volume to the drop dispersion and ii) a planning method that efficiently restricts the flight to the region of interest avoiding potential collisions in bounded scenes. The algorithm has been tested in several simulation scenes, showing that it is effective and covers more areas with respect to two approaches in the literature. Note that the proposal is not limited to disinfection applications, but can be applied to other ones, such as painting or precision agriculture.

Unmanned Aerial Vehicles Motion Control with Fuzzy Tuning of Cascaded-PID Gains

One of the main challenges of maneuvering an Unmanned Aerial Vehicle (UAV) to keep a stabilized flight is dealing with its fast and highly coupled nonlinear dynamics. There are several solutions in the literature, but most of them require fine-tuning of the parameters. In order to avoid the exhaustive tuning procedures, this work employs a Fuzzy Logic strategy for online tuning of the PID gains of the UAV motion controller. A Cascaded-PID scheme is proposed, in which velocity commands are calculated and sent to the flight control unit from a given target desired position (waypoint). Therefore, the flight control unit is responsible for the lower control loop. The main advantage of the proposed method is that it can be applied to any UAV without the need of its formal mathematical model. Robot Operating System (ROS) is used to integrate the proposed system and the flight control unit. The solution was evaluated through flight tests and simulations, which were conducted using Unreal Engine 4 with the Microsoft AirSim plugin. In the simulations, the proposed method is compared with the traditional Ziegler-Nichols tuning method, another Fuzzy Logic approach, and the ArduPilot built-in PID controller. The simulation results show that the proposed method, compared to the ArduPilot controller, drives the UAV to reach the desired setpoint faster. When compared to Ziegler-Nichols and another different Fuzzy Logic approach, the proposed method demonstrates to provide a faster accommodation and yield smaller errors amplitudes.

Photogrammetric Process to Monitor Stress Fields Inside Structural Systems

This research employs displacement fields photogrammetrically captured on the surface of a solid or structure to estimate real-time stress distributions it undergoes during a given loading period. The displacement fields are determined based on a series of images taken from the solid surface while it experiences deformation. Image displacements are used to estimate the deformations in the plane of the beam surface, and Poisson’s Method is subsequently applied to reconstruct these surfaces, at a given time, by extracting triangular meshes from the corresponding points clouds. With the aid of the measured displacement fields, the Boundary Element Method (BEM) is considered to evaluate stress values throughout the solid. Herein, the unknown boundary forces must be additionally calculated. As the photogrammetrically reconstructed deformed surfaces may be defined by several million points, the boundary displacement values of boundary-element models having a convenient number of nodes are determined based on an optimized displacement surface that best fits the real measured data. The results showed the effectiveness and potential application of the proposed methodology in several tasks to determine real-time stress distributions in structures.

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.