A Multi-Objective Optimization Problem Solving Method Based on Improved Golden Jackal Optimization Algorithm and Its Application.

The traditional golden jackal optimization algorithm (GJO) has slow convergence speed, insufficient accuracy, and weakened optimization ability in the process of finding the optimal solution. At the same time, it is easy to fall into local extremes and other limitations. In this paper, a novel golden jackal optimization algorithm (SCMGJO) combining sine-cosine and Cauchy mutation is proposed. On one hand, tent mapping reverse learning is introduced in population initialization, and sine and cosine strategies are introduced in the update of prey positions, which enhances the global exploration ability of the algorithm. On the other hand, the introduction of Cauchy mutation for perturbation and update of the optimal solution effectively improves the algorithm's ability to obtain the optimal solution. Through the optimization experiment of 23 benchmark test functions, the results show that the SCMGJO algorithm performs well in convergence speed and accuracy. In addition, the stretching/compression spring design problem, three-bar truss design problem, and unmanned aerial vehicle path planning problem are introduced for verification. The experimental results prove that the SCMGJO algorithm has superior performance compared with other intelligent optimization algorithms and verify its application ability in engineering applications.

Three-dimensional unmanned aerial vehicle path planning utilizing artificial gorilla troops optimizer incorporating combined mutation and quadratic interpolation operators.

In real terrain and dynamic obstacle scenarios, the complexity of the 3D UAV path planning problem greatly increases. Thus, to procure the optimal flight path for UAVs in such scenarios, an augmented Artificial Gorilla Troops Optimizer, denoted as OQMGTO, is proposed. The proposed OQMGTO algorithm introduces three strategies: combination mutation, quadratic interpolation, and random opposition-based learning, aiming to enhance the ability to timely escape from local optimal path areas and rapidly converge to the global optimal path. Given the flight distance, smoothness, terrain collision, and other five realistic factors of UAVs, specific constraint conditions are proposed to address complex scenarios, aiming to construct a path planning model. By optimizing this model, OQMGTO algorithm solves the path planning problem in complex scenarios. The extensive validation of OQMGTO algorithm on CEC2017 test suite enhances its credibility as a powerful optimization tool. Comparison experiments are conducted in simulated terrain scenarios, including six multi-obstacle terrain scenarios and three dynamic obstacle scenarios. The experimental findings validate OOMGTO algorithm can assist UAV in searching for excellent flight paths, featuring high safety and reliability characteristics, which confirms the superiority of OOMGTO algorithm for path planning in simulated terrain scenarios. Furthermore, in four flight missions carried out in real terrains, OQMGTO algorithm demonstrates superior search performance, planning smooth trajectories without mountain collision.

Bio-Inspired Optimization-Based Path Planning Algorithms in Unmanned Aerial Vehicles: A Survey.

Advancements in electronics and software have enabled the rapid development of unmanned aerial vehicles (UAVs) and UAV-assisted applications. Although the mobility of UAVs allows for flexible deployment of networks, it introduces challenges regarding throughput, delay, cost, and energy. Therefore, path planning is an important aspect of UAV communications. Bio-inspired algorithms rely on the inspiration and principles of the biological evolution of nature to achieve robust survival techniques. However, the issues have many nonlinear constraints, which pose a number of problems such as time restrictions and high dimensionality. Recent trends tend to employ bio-inspired optimization algorithms, which are a potential method for handling difficult optimization problems, to address the issues associated with standard optimization algorithms. Focusing on these points, we investigate various bio-inspired algorithms for UAV path planning over the past decade. To the best of our knowledge, no survey on existing bio-inspired algorithms for UAV path planning has been reported in the literature. In this study, we investigate the prevailing bio-inspired algorithms extensively from the perspective of key features, working principles, advantages, and limitations. Subsequently, path planning algorithms are compared with each other in terms of their major features, characteristics, and performance factors. Furthermore, the challenges and future research trends in UAV path planning are summarized and discussed.

Dynamic Smoothing, Filtering and Differentiation of Signals Defining the Path of the UAV.

On the example of a control system for an unmanned aerial vehicle, we consider the problems of filtering, smoothing and restoring derivatives of reference action signals. These signals determine the desired spatial path of the plant at the first approximation. As a rule, researchers have considered these problems separately and have used different methods to solve each of them. The paper aims to develop a unified approach that provides a comprehensive solution to mentioned problems. We propose a dynamic admissible path generator. It is constructed as a copy of the canonical control plant model with smooth and bounded sigmoid corrective actions. For the deterministic case, a synthesis procedure has been developed, which ensures that the output variables of the generator track a non-smooth reference signal. Moreover, it considers the constraints on the velocity and acceleration of the plant. As a result, the generator variables produce a naturally smoothed spatial curve and its derivatives, which are realizable reference actions for the plant. The construction of the generator does not require exact knowledge of the plant parameters. Its dynamic order is less than that of the standard differentiators. We confirm the effectiveness of the approach by the results of numerical simulation.

Time-Constrained Node Visit Planning for Collaborative UAV-WSN Distributed Applications.

Unmanned Aerial Vehicles (UAVs) are often studied as tools to perform data collection from Wireless Sensor Networks (WSNs). Path planning is a fundamental aspect of this endeavor. Works in the current literature assume that data are always ready to be retrieved when the UAV passes. This operational model is quite rigid and does not allow for the integration of the UAV as a computational object playing an active role in the network. In fact, the UAV could begin the computation on a first visit and retrieve the data later. Potentially, the UAV could orchestrate the distributed computation to improve its performance, change its parameters, and even upload new applications to the sensor network. In this paper, we analyze a scenario where a UAV plays an active role in the operation of multiple sensor networks by visiting different node clusters to initiate distributed computation and collect the final outcomes. The experimental results validate the effectiveness of the proposed method in optimizing total flight time, Average Age of Information, Average cluster computation end time, and Average data collection time compared to prevalent approaches to UAV path-planning that are adapted to the purpose.

UAV Path Planning for Reconnaissance and Look-Ahead Coverage Support for Mobile Ground Vehicles.

Path planning of unmanned aerial vehicles (UAVs) for reconnaissance and look-ahead coverage support for mobile ground vehicles (MGVs) is a challenging task due to many unknowns being imposed by the MGVs' variable velocity profiles, change in heading, and structural differences between the ground and air environments. Few path planning techniques have been reported in the literature for multirotor UAVs that autonomously follow and support MGVs in reconnaissance missions. These techniques formulate the path planning problem as a tracking problem utilizing gimbal sensors to overcome the coverage and reconnaissance complexities. Despite their lack of considering additional objectives such as reconnaissance coverage and dynamic environments, they retain several drawbacks, including high computational requirements, hardware dependency, and low performance when the MGV has varying velocities. In this study, a novel 3D path planning technique for multirotor UAVs is presented, the enhanced dynamic artificial potential field (ED-APF), where path planning is formulated as both a follow and cover problem with nongimbal sensors. The proposed technique adopts a vertical sinusoidal path for the UAV that adapts relative to the MGV's position and velocity, guided by the MGV's heading for reconnaissance and exploration of areas and routes ahead beyond the MGV sensors' range, thus extending the MGV's reconnaissance capabilities. The amplitude and frequency of the sinusoidal path are determined to maximize the required look-ahead visual coverage quality in terms of pixel density and quantity pertaining to the area covered. The ED-APF was tested and validated against the general artificial potential field techniques for various simulation scenarios using Robot Operating System (ROS) and Gazebo-supported PX4-SITL. It demonstrated superior performance and showed its suitability for reconnaissance and look-ahead support to MGVs in dynamic and obstacle-populated environments.

UAV Mission Planning with SAR Application.

The paper presents the concept of mission planning for a short-range tactical class Unmanned Aerial Vehicle (UAV) that recognizes targets using the sensors it has been equipped with. Tasks carried out by such systems are mainly associated with aerial reconnaissance employing Electro Optical (EO)/Near Infra-Red (NIR) heads, Synthetic Aperture Radar (SAR), and Electronic Intelligence (ELINT) systems. UAVs of this class are most often used in NATO armies to support artillery actions, etc. The key task, carried out during their activities, is to plan a reconnaissance mission in which the flight route will be determined that optimally uses the sensors' capabilities. The paper describes the scenario of determining the mission plan and, in particular, the UAV flight routes to which the recognition targets are assigned. The problem was decomposed into several subproblems: assigning reconnaissance tasks to UAVs with choosing the reconnaissance sensors and designating an initial UAV flight plan. The last step is planning a detailed flight route taking into account the time constraints imposed on recognition and the characteristics of the reconnaissance sensors. The final step is to generate the real UAV flight trajectory based on its technical parameters. The algorithm for determining exact flight routes for the indicated reconnaissance purposes was also discussed, taking into account the presence of enemy troops and available air corridors. The task scheduling algorithm-Vehicle Route Planning with Time Window (VRPTW)-using time windows is formulated in the form of the Mixed Integer Linear Problem (MILP). The MILP formulation was used to solve the UAV flight route planning task. The algorithm can be used both when planning individual UAV missions and UAV groups cooperating together. The approach presented is a practical way of establishing mission plans implemented in real unmanned systems.

On-Board Real-Time Trajectory Planning for Fixed Wing Unmanned Aerial Vehicles in Extreme Environments.

A team from the University of Bristol have developed a method of operating fixed wing Unmanned Aerial Vehicles (UAVs) at long-range and high-altitude over Volcán de Fuego in Guatemala for the purposes of volcanic monitoring and ash-sampling. Conventionally, the mission plans must be carefully designed prior to flight, to cope with altitude gains in excess of 3000 m, reaching 9 km from the ground control station and 4500 m above mean sea level. This means the climb route cannot be modified mid-flight. At these scales, atmospheric conditions change over the course of a flight and so a real-time trajectory planner (RTTP) is desirable, calculating a route on-board the aircraft. This paper presents an RTTP based around a genetic algorithm optimisation running on a Raspberry Pi 3 B+, the first of its kind to be flown on-board a UAV. Four flights are presented, each having calculated a new and valid trajectory on-board, from the ground control station to the summit region of Volcan de Fuego. The RTTP flights are shown to have approximately equivalent efficiency characteristics to conventionally planned missions. This technology is promising for the future of long-range UAV operations and further development is likely to see significant energy and efficiency savings.

Multi-UAV Path Planning for Autonomous Missions in Mixed GNSS Coverage Scenarios.

This paper presents an algorithm for multi-UAV path planning in scenarios with heterogeneous Global Navigation Satellite Systems (GNSS) coverage. In these environments, cooperative strategies can be effectively exploited when flying in GNSS-challenging conditions, e.g., natural/urban canyons, while the different UAVs can fly as independent systems in the absence of navigation issues (i.e., open sky conditions). These different flight environments are taken into account at path planning level, obtaining a distributed multi-UAV system that autonomously reconfigures itself based on mission needs. Path planning, formulated as a vehicle routing problem, aims at defining smooth and flyable polynomial trajectories, whose time of flight is estimated to guarantee coexistence of different UAVs at the same challenging area. The algorithm is tested in a simulation environment directly derived from a real-world 3D scenario, for variable number of UAVs and waypoints. Its solution and computational cost are compared with optimal planning methods. Results show that the computational burden is almost unaffected by the number of UAVs, and it is compatible with near real time implementation even for a relatively large number of waypoints. The provided solution takes full advantage from the available flight resources, reducing mission time for a given set of waypoints and for increasing UAV number.