Under harsh geographical conditions where manned flight is not possible, the ability of the unmanned aerial vehicle (UAV) to successfully carry out the payload hold–release mission by avoiding obstacles depends on the optimal path planning and tracking performance of the UAV. The ability of the UAV to plan and track the path with minimum energy and time consumption is possible by using the flight parameters. This study performs the optimum path planning and tracking using Harris hawk optimization (HHO)–grey wolf optimization (GWO), a hybrid metaheuristic optimization algorithm, to enable the UAV to actualize the payload hold–release mission avoiding obstacles. In the study, the hybrid HHO–GWO algorithm, which stands out with its avoidance of local minima and speed convergence, is used to successfully obtain the feasible and effective path. In addition, the effect of the mass change uncertainty of the UAV on optimal path planning and tracking performance is determined. The effectiveness of the proposed approach is tested by comparing it with the metaheuristic swarm optimization algorithms such as particle swarm optimization (PSO) and GWO. The experimental results obtained indicate that the proposed algorithm generates a fast and safe optimal path without becoming stuck with local minima, and the quadcopter tracks the generated path with minimum energy and time consumption.

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Metaheuristic Optimization-Based Path Planning and Tracking of Quadcopter for Payload Hold-Release Mission

Unmanned Aerial Vehicles (UAV) path planning can be considered as a complicated function optimization problem with constraint condition. Population based algorithm, especially the artificial bee colony (ABC) algorithm, is known as an effective tool to solve this problem. ABC algorithm is a relatively predominant optimization technique with an advantage of having fewer control parameters over other population algorithms. Considering the ergodicity and the stochastic of the chaotic map, we propose a modified strategy of initialization for the standard ABC, which utilizing the logistic map and opposition based learning to generate the initial population as well as the scout bee position. In addition, the employed bee search equation is modified by adding weight coefficients for the purpose of increasing the convergence speed. Then we test the modified artificial bee colony algorithm in four function optimization problems and path planning problems. The results demonstrate a superior performance of our algorithm in solving UAV path planning in two dimensions compare with the standard ABC algorithm.

Path planning for Unmanned Air Vehicles using an improved artificial bee colony algorithm

Three-dimensional path planning for autonomous robots is a prevalent problem in mobile robotics. This paper presents three novel versions of a hybrid method designed to assist in planning such paths for these robots. In this paper, an improvement on Rapidly exploring Random Tree (RRT) algorithm, namely Adapted-RRT, is presented that uses three well-known metaheuristic algorithms, namely Grey Wolf Optimization (GWO), Incremental Grey Wolf Optimization (I-GWO), and Expanded Grey Wolf Optimization (Ex-GWO)). RRT variants, using these algorithms, are named Adapted-RRTGWO, Adapted-RRTI-GWO, and Adapted-RRTEx-GWO. The most significant shortcoming of the methods in the original sampling-based algorithm is their inability in finding the optimal paths. On the other hand, the metaheuristic-based algorithms are disadvantaged as they demand a predetermined knowledge of intermediate stations. This study is novel in that it uses the advantages of sampling and metaheuristic methods while eliminating their shortcomings. In these methods, two important operations (length and direction of each movement) are defined that play an important role in selecting the next stations and generating an optimal path. They try to find solutions close to the optima without collision, while providing comparatively efficient execution time and space complexities. The proposed methods have been simulated employing four different maps for three unmanned aerial vehicles, with diverse sets of starting and ending points. The results have been compared among a total of 11 algorithms. The comparison of results shows that the proposed path planning methods generally outperform various algorithms, namely BPIB-RRT*, tGSRT, GWO, I-GWO, Ex-GWO, PSO, Improved BA, and WOA. The simulation results are analysed in terms of optimal path costs, execution time, and convergence rate.

Adapted-RRT: novel hybrid method to solve three-dimensional path planning problem using sampling and metaheuristic-based algorithms

Unmanned aerial vehicles (UAVs) are considered a promising example of an automatic emergency task in a dynamic marine environment. However, the maritime communication performance between UAVs and offshore platforms has become a severe challenge. Due to the complex marine environment, the task allocation and route planning efficiency of multiple UAVs in an intelligent ocean are not satisfactory. To address these challenges, this paper proposes an intelligent marine task allocation and route planning scheme for multiple UAVs based on improved particle swarm optimization combined with a genetic algorithm (GA-PSO). Based on the simulation of an intelligent marine control system, the traditional particle swarm optimization (PSO) algorithm is improved by introducing partial matching crossover and secondary transposition mutation. The improved GA-PSO is used to solve the random task allocation problem of multiple UAVs and the two-dimensional route planning of a single UAV. The simulation results show that compared with the traditional scheme, the proposed scheme can significantly improve the task allocation efficiency, and the navigation path planned by the proposed scheme is also optimal.

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Task allocation and route planning of multiple UAVs in a marine environment based on an improved particle swarm optimization algorithm

The previous three industrializations had a major impact on the agricultural sector. There was a transformation from indigenous farming to mechanized farming. With the advent of Industry 4.0, the fourth generation of the Agriculture revolution has taken place with the introduction of precision farming, and the backbone of this revolution is mainly the Internet of Things. Edge Intelligence in combination with Agriculture Big-Data Analysis and modern evolving communication technologies like 5G Narrow Band (NB) IoT is going to play a significant role in the progress of Agriculture 4.0 through real-time applications as a simpler waveform like NB-IoT consumes less power, have high reliability, have wider deployment capability and have lower latency. Energy-efficient and Real-time application of technologies in the agricultural sector is the need of the hour for a smart green industrial revolution through Agriculture 4.0. The induction of Multilingual Voice User Interface control enabled drones in combination with dynamic sensors in 5G era, will prove to be a game-changer in the agricultural sector. Edge Intelligence enabled drones will introduce the concept of ubiquitous computing in the agricultural domain, will be quite beneficial for the farmers as it will provide real-time alerting, monitoring systems in addition to real-time precision farming.

AgriEdge: Edge Intelligent 5G Narrow Band Internet of Drone Things for Agriculture 4.0

Unmanned aerial vehicles (UAVs) are a quintessential example of automation in the field of avionics. UAVs provide a platform for performing a wide variety of tasks, but in each case the concept of path planning plays an integral role. It helps to generate a pathway free of obstacles, having minimum length leading to lesser fuel consumption, lesser traversal time and helps in steering the aircraft and its corresponding antenna power signature safely around the hostile antenna to avoid detection. To optimize path planning to incorporate all the above-mentioned constraints, this paper presents two new hybrid algorithms particle swarm optimization (PSO) with harmony search algorithm and PSO with genetic algorithm. The hybrid algorithms perform both an exploratory and exploitative search, unlike the existing algorithms which are biased, towards either an exploitative search or an exploratory search. Furthermore, the hybrid algorithms are compared to the existing optimization algorithms and in all cases the hybrid algorithms give a minimum of 7% better result against PSO with up to a 40% better result against Invasive Weed optimization algorithm for a fixed computational time, suggesting better real-time applications.

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B. Abhishek

Papers

Hybrid PSO-HSA and PSO-GA algorithm for 3D path planning in autonomous UAVs