Over the last decade, Unmanned Aerial Vehicles (UAVs), also known as drones, have been broadly utilized in various agricultural fields, such as crop management, crop monitoring, seed sowing, and pesticide spraying. Nonetheless, autonomy is still a crucial limitation faced by the Internet of Things (IoT) UAV systems, especially when used as sprayer UAVs, where data needs to be captured and preprocessed for robust real-time obstacle detection and collision avoidance. Moreover, because of the objective and operational difference between general UAVs and sprayer UAVs, not every obstacle detection and collision avoidance method will be sufficient for sprayer UAVs. In this regard, this article seeks to review the most relevant developments on all correlated branches of the obstacle avoidance scenarios for agricultural sprayer UAVs, including a UAV sprayer’s structural details. Furthermore, the most relevant open challenges for current UAV sprayer solutions are enumerated, thus paving the way for future researchers to define a roadmap for devising new-generation, affordable autonomous sprayer UAV solutions. Agricultural UAV sprayers require data-intensive algorithms for the processing of the images acquired, and expertise in the field of autonomous flight is usually needed. The present study concludes that UAV sprayers are still facing obstacle detection challenges due to their dynamic operating and loading conditions.

https://www.mdpi.com/2073-4395/11/6/1069/pdf

A State-of-the-Art Analysis of Obstacle Avoidance Methods from the Perspective of an Agricultural Sprayer UAV’s Operation Scenario

In practical applications, an appropriate path planning algorithm needs to be selected according to the actual situation. Using the Cartographer algorithm based on graph optimization to build the map, and using AMCL positioning at the same time, the problem of low accuracy caused by its own defects has been successfully solved. A* algorithm original path trajectory inflection point, node redundancy phenomenon is obvious, longer running time, and even difficult to plan the optimal path An improved A* algorithm is proposed, which uses adding weight coefficient w, adaptively adjusting the step size algorithm and cubic Bezier curve make up for the lack of more turning points, larger turning angles and longer running time in the algorithm search. The hybrid path planning algorithm combining the improved A* algorithm and the dynamic window method solves the problem that the A* algorithm cannot avoid dynamic obstacles in complex environments, and prevents the mobile robot from falling into local optimum. Experiments show that the improved algorithm can effectively solve the problems of more turning points, larger turning angles and longer running time encountered by the mobile robot in the search.

A Novel Hybrid Path Planning Method for Mobile Robots

Future smart factories need to use intelligent transport devices like automated guided vehicles (AGVs) for connecting intelligent production and logistics. To address the lack of edge side functions in the current AGV systems, this paper proposes an agent-based system architecture for AGVs in cloud-edge computing environments. The system is divided into three main components: the cloud center, edge nodes, and AGV agents. The cloud center is largely responsible for AGV transport route planning, while the edge nodes are in charge of AGV transport control and equipment management. The driving function and executing commands are handled by AGV agents. AGV agents can communicate and collaborate with each other to address emergent issues. The proposed approach has been validated through simulations.

An Agent-based System Architecture for Automated Guided Vehicles in Cloud-Edge Computing Environments

In practical applications, an appropriate path planning algorithm needs to be selected according to the actual situation. Using the Cartographer algorithm based on graph optimization to build the map, and using AMCL positioning at the same time, the problem of low accuracy caused by its own defects has been successfully solved. A* algorithm original path trajectory inflection point, node redundancy phenomenon is obvious, longer running time, and even difficult to plan the optimal path An improved A* algorithm is proposed, which uses adding weight coefficient w, adaptively adjusting the step size algorithm and cubic Bezier curve make up for the lack of more turning points, larger turning angles and longer running time in the algorithm search. The hybrid path planning algorithm combining the improved A* algorithm and the dynamic window method solves the problem that the A* algorithm cannot avoid dynamic obstacles in complex environments, and prevents the mobile robot from falling into local optimum. Experiments show that the improved algorithm can effectively solve the problems of more turning points, larger turning angles and longer running time encountered by the mobile robot in the search. 

Solid-state welding (SSW) for the production of magnesium alloys is one of the most promising methods. There are various methods used for solid-state welding of magnesium alloys. These are low-density structural alloys that improve energy efficiency and environmentally friendly nature, as well as being drivers of a circular economy model for sustainable architecture and an inventive ecology. While the use of magnesium alloys is expected to increase, it is critical to understand how welded connections behave mechanically and structurally. We have categorically covered all the different types of solid-state welding for magnesium alloys with their pros and cons and different applications. We wrapped up our discussion by emphasizing the importance of solid-state magnesium alloy welding in the future. 

Solid-State Welding of Magnesium Alloys

Automated Guided Vehicles are used to help the process of carrying materials from one place to another. AGVs are widely used in the materials handling process in different industries hence, the reasoning behind their extremely wide usage in industries such as Automotive, manufacturing, Medical, etc. This research deals with the obstacle avoidance of Automated Guided Vehicles (AGV). This paper propose an algorithm that helps to build obstacle-free trajectories for AGV that leave the original track and again get back to the original track by avoiding the obstacle. The new path also have to act according to the dynamic & kinematic constraints of the AGV. The proposed method will be validated using simulations.

J. Lakshmipathi

Papers

Intelligent Obstacle Avoiding AGV Using Vector Field Histogram and Supervisory Control

Friction Stir Welding Tool Condition Prediction Using Vibrational Analysis Through Machine Learning – A Review

Product defect categorization using machine vision through machine learning