Online 3D path planning for Tri-copter drone using GWO-IBA algorithm

Robots at present are involved in many parts of life, especially mobile robots, which are two parts, ground robots and flying robots, and the best example of a flying robot is the drone. Path planning is a fundamental part of UAVs because the drone follows the path that leads it to goal with obstacle avoidance. Therefore, this paper proposes a hybrid algorithm (grey wolf optimization - intelligent bug algorithm GWO-IBA) to determine the best, shortest and without obstacles path. The hybrid algorithm was implemented and tested in the MATLAB program on the Tri-copter model, and it gave different paths in different environments. The paths obtained were characterized by being free of obstacles and the shortest paths available to reach the target

Improved GWO Algorithm for UAV Path Planning on Crop Pest Monitoring

Agricultural information monitoring is the monitoring of the agricultural production process, and its task is to monitor the growth process of major crops systematically. When assessing the pest situation of crops in this process, the traditional satellite monitoring method has the defects of poor real-time and high operating cost, whereas the pest monitoring through Unmanned Aerial Vehicles (UAVs) effectively solves the above problems, so this method is widely used. An important key issue involved in monitoring technology is path planning. In this paper, we proposed an Improved Grey Wolf Optimization algorithm, IGWO, to realize the flight path planning of UAV in crop pest monitoring. A map environment model is simulated, and information traversal is performed, then the search of feasible paths for UAV flight is carried out by the Grey Wolf Optimization algorithm (GWO). However, the algorithm search process has the defect of falling into local optimum which leading to path planning failure. To avoid such a situation, we introduced the probabilistic leap mechanism of the Simulated Annealing algorithm (SA). Besides, the convergence factor is modified with an exponential decay mode for improving the convergence rate of the algorithm. Compared with the GWO algorithm, IGWO has the 8.3%, 16.7%, 28.6% and 39.6% lower total cost of path distance on map models with precision of 15, 20, 25 and 30 respectively, and also has better path planning results in contrast to other swarm intelligence algorithms

Grey Wolf Optimizer-Based Approaches to Path Planning and Fuzzy Logic-based Tracking Control for Mobile Robots

This paper proposes two applications of Grey Wolf Optimizer (GWO) algorithms to a path planning (PaPl) problem and a Proportional-Integral (PI)-fuzzy controller tuning problem. Both optimization problems solved by GWO algorithms are explained in detail. An off-line GWO-based PaPl approach for Nonholonomic Wheeled Mobile Robots (NWMRs) in static environments is proposed. Once the PaPl problem is solved resulting in the reference trajectory of the robots, the paper also suggests a GWO-based approach to tune cost-effective PI-fuzzy controllers in tracking control problem for NWMRs. The experimental results are demonstrated through simple multiagent settings conducted on the nRobotic platform developed at the Politehnica University of Timisoara, Romania, and they prove both the effectiveness of the two GWO-based approaches and major performance improvement

A Novel Path Planning Optimization Algorithm for Semi-Autonomous UAV in Bird Repellent Systems Based in Particle Swarm Optimization

Bird damage to fruit crops causes significant monetary losses to farmers annually. The application of traditional bird repelling methods such as bird cannons and tree netting became inefficient in the long run, keeping high maintenance and reduced mobility. Due to their versatility, Unmanned Aerial Vehicles (UAVs) can be beneficial to solve this problem. However, due to their low battery capacity that equals low flight duration, it is necessary to evolve path planning optimization. A path planning optimization algorithm of UAVs based on Particle Swarm Optimization (PSO) is presented in this dissertation. This technique was used due to the need for an easy implementation optimization algorithm to start the initial tests. The PSO algorithm is simple and has few control parameters while maintaining a good performance. This path planning optimization algorithm aims to manage the drone's distance and flight time, applying optimization and randomness techniques to overcome the disadvantages of the traditional systems. The proposed algorithm's performance was tested in three study cases: two of them in simulation to test the variation of each parameter and one in the field to test the influence on battery management and height influence. All cases were tested in the three possible situations: same incidence rate, different rates, and different rates with no bird damage to fruit crops. The proposed algorithm presents promising results with an outstanding reduced average error in the total distance for the path planning obtained and low execution time. However, it is necessary to point out that the path planning optimization algorithm may have difficulty finding a suitable solution if there is a bad ratio between the total distance for path planning and points of interest. The field tests were also essential to understand the algorithm's behavior of the path planning algorithm in the UAV, showing that there is less energy discharged with fewer points of interest, but that do not correlates with the flight time. Also, there is no association between the maximum horizontal speed and the flight time, which means that the function to calculate the total distance for path planning needs to be adjusted.Anualmente, os danos causados pelas aves em pomares criam perdas monetárias significativas aos agricultores. A aplicação de métodos tradicionais de dispersão de aves, como canhões repelentes de aves e redes nas árvores, torna-se ineficiente a longo prazo, sendo ainda de alta manutenção e de mobilidade reduzida. Devido à sua versatilidade, os Veículos Aéreos Não Tripulados (VANT) podem ser benéficos para resolver este problema. No entanto, devido à baixa capacidade das suas baterias, que se traduz num baixo tempo de voo, é necessário otimizar o planeamento dos caminhos. Nesta dissertação, é apresentado um algoritmo de otimização para planeamento de caminhos para VANT baseado no Particle Swarm Optimization (PSO). Para se iniciarem os primeiros testes do algoritmo proposto, a técnica utilizada foi a supracitada devido à necessidade de um algoritmo de otimização fácil de implementar. O algoritmo PSO é simples e possuí poucos parâmetros de controlo, mantendo um bom desempenho. Este algoritmo de otimização de planeamento de caminhos propõe-se a gerir a distância e o tempo de voo do drone, aplicando técnicas de otimização e de aleatoriedade para superar a sua desvantagem relativamente aos sistemas tradicionais. O desempenho do algoritmo de planeamento de caminhos foi testado em três casos de estudo: dois deles em simulação para testar a variação de cada parâmetro e outro em campo para testar a capacidade da bateria. Todos os casos foram testados nas três situações possíveis: mesma taxa de incidência, taxas diferentes e taxas diferentes sem danos de aves. Os resultados apresentados pelo algoritmo proposto demonstram um erro médio muto reduzido na distância total para o planeamento de caminhos obtido e baixo tempo de execução. Porém, é necessário destacar que o algoritmo pode ter dificuldade em encontrar uma solução adequada se houver uma má relação entre a distância total para o planeamento de caminhos e os pontos de interesse. Os testes de campo também foram essenciais para entender o comportamento do algoritmo na prática, mostrando que há menos energia consumida com menos pontos de interesse, sendo que este parâmetro não se correlaciona com o tempo de voo. Além disso, não há associação entre a velocidade horizontal máxima e o tempo da missão, o que significa que a função de cálculo da distância total para o planeamento de caminhos requer ser ajustada

Communication and Control in Collaborative UAVs: Recent Advances and Future Trends

The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains. Nevertheless, the challenges of establishing high-speed communication links, flexible control strategies, and developing efficient collaborative decision-making algorithms for a swarm of UAVs limit their autonomy, robustness, and reliability. Thus, a growing focus has been witnessed on collaborative communication to allow a swarm of UAVs to coordinate and communicate autonomously for the cooperative completion of tasks in a short time with improved efficiency and reliability. This work presents a comprehensive review of collaborative communication in a multi-UAV system. We thoroughly discuss the characteristics of intelligent UAVs and their communication and control requirements for autonomous collaboration and coordination. Moreover, we review various UAV collaboration tasks, summarize the applications of UAV swarm networks for dense urban environments and present the use case scenarios to highlight the current developments of UAV-based applications in various domains. Finally, we identify several exciting future research direction that needs attention for advancing the research in collaborative UAVs

Drone deep reinforcement learning: A review

Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. These applications belong to the civilian and the military fields. To name a few; infrastructure inspection, traffic patrolling, remote sensing, mapping, surveillance, rescuing humans and animals, environment monitoring, and Intelligence, Surveillance, Target Acquisition, and Reconnaissance (ISTAR) operations. However, the use of UAVs in these applications needs a substantial level of autonomy. In other words, UAVs should have the ability to accomplish planned missions in unexpected situations without requiring human intervention. To ensure this level of autonomy, many artificial intelligence algorithms were designed. These algorithms targeted the guidance, navigation, and control (GNC) of UAVs. In this paper, we described the state of the art of one subset of these algorithms: the deep reinforcement learning (DRL) techniques. We made a detailed description of them, and we deduced the current limitations in this area. We noted that most of these DRL methods were designed to ensure stable and smooth UAV navigation by training computer-simulated environments. We realized that further research efforts are needed to address the challenges that restrain their deployment in real-life scenarios

3D Real-Time Energy Efficient Path Planning for a Fleet of Fixed-Wing UAVs

UAV path planning requires finding an optimal (or sub-optimal) collision free path in a cluttered environment, while taking into account geometric, physical and temporal constraints, eventually allowing UAVs to perform their tasks despite several uncertainty sources. This paper reviews the current state-of-the-art in path planning, and subsequently introduces a novel node-based algorithm based on the called EEA*. EEA* is based on the A* Search algorithm and aims at mitigating some of its key limitations. The proposed EEA* deals with 3D environments, it provides robustness quickly converging to the solution, it is energy efficient and it is realtime implementable and executable. Along with the proposed EEA*, a local path planner is developed to cope with unknown dynamic threats in the environment. Applicability and effectiveness is first demonstrated via simulated experiments using a fixed-wing UAV that operates in different mountain-like 3D environments in the presence of several unknown dynamic obstacles. Then, the algorithm is applied in a multi-agent setting with three UAVs that are commanded to follow their respective paths in a safe way. The energy efficiency of the EEA* algorithm has also been tested and compared with the conventional A* algorithm

Modified mayfly algorithm for UAV path planning

The unmanned aerial vehicle (UAV) path planning problem is primarily concerned with avoiding collision with obstacles while determining the best flight path to the target position. This paper first establishes a cost function to transform the UAV route planning issue into an optimization issue that meets the UAV's feasible path requirements and path safety constraints. Then, this paper introduces a modified Mayfly Algorithm (modMA), which employs an exponent decreasing inertia weight (EDIW) strategy, adaptive Cauchy mutation, and an enhanced crossover operator to effectively search the UAV configuration space and discover the path with the lowest overall cost. Finally, the proposed modMA is evaluated on 26 benchmark functions as well as the UAV route planning problem, and the results demonstrate that it outperforms the other compared algorithms.Web of Science65art. no. 13

Bold:Bio-inspired optimized leader election for multiple drones

Over the past few years, unmanned aerial vehicles (UAV) or drones have been used for many applications. In certain applications like surveillance and emergency rescue operations, multiple drones work as a network to achieve the target in which any one of the drones will act as the master or coordinator to communicate, monitor, and control other drones. Hence, drones are energy-constrained; there is a need for effective coordination among them in terms of decision making and communication between drones and base stations during these critical situations. This paper focuses on providing an efficient approach for the election of the cluster head dynamically, which heads the other drones in the network. The main objective of the paper is to provide an effective solution to elect the cluster head among multi drones at different periods based on the various physical constraints of drones. The elected cluster head acts as the decision-maker and assigns tasks to other drones. In a case where the cluster head fails, then the next eligible drone is re-elected as the leader. Hence, an optimally distributed solution proposed is called Bio-Inspired Optimized Leader Election for Multiple Drones (BOLD), which is based on two AI-based optimization techniques. The simulation results of BOLD compared with the existing Particle Swarm Optimization-Cluster head election (PSO-C) in terms of network lifetime and energy consumption, and from the results, it has been proven that the lifetime of drones with the BOLD algorithm is 15% higher than the drones with PSO-C algorithm