Explore the vast range of titles available.

Unmanned aerial vehicles (UAV), or drones, have gained a lot of popularity over the last decade. The use of autonomous drones appears to be a viable and low-cost solution to problems in many applications. Path planning capabilities are essential for autonomous control systems. An autonomous drone must be able to rapidly compute feasible and energy-efficient paths to avoid collisions. In this study, we review two key aspects of path planning: environmental representation and path generation techniques. Common path planning techniques are analyzed, and their key limitations are highlighted. Finally, we review thirty-five highly cited publications to identify current trends in drone path planning research. We then use these results to identify factors that need to be addressed in future studies in order to develop a practical path planner for autonomous drones.

The engagement of a group of autonomous air vehicles against several targets is a major challenge in mission planning. This paper addresses the problem of cooperative flight path planning where the air vehicles should arrive at the destinations simultaneously or sequentially with specified time delays, while minimizing the total mission time. This involves finding an optimal assignment of air vehicles to targets and generating trajectories in compliance with the kinematic constraints of the vehicles. The trajectories have to avoid nofly-areas, threats and other obstacles, and must prevent the air vehicles from colliding with each other. The presented algorithm for simultaneous arrival first calculates shortest flight paths between all pairs of air vehicles and targets using a network-based routing model. An optimal assignment and a critical path is found by solving a linear bottleneck assignment problem with costs corresponding to the lengths of the shortest paths. The other flight paths are prolongated to the length of the critical path by automatic insertion of waypoints. This is achieved by concatenating subpaths stored in different shortest-path-trees. Due to the special structure of the network, all concatenated flight paths are flyable and feasible. Sequential arrival at a target is realized by sorting the flight paths according to their lengths and prolongating them whenever necessary to accomplish the desired time delays. The capability of the approach is demonstrated by simulation results.

Unmanned aerial vehicles (UAVs) have emerged as a highly efficient means of monitoring landslide-prone regions, given the growing concern for urban safety and the increasing occurrence of landslides. Designing optimal UAV flight routes is crucial for effective landslide monitoring. However, in real-world scenarios, the testing and validating of flight path planning algorithms incur high cost and safety concerns, making overall flight operations challenging. Therefore, this paper proposes the use of the Unreal Engine simulation framework to design UAV flight path planning specifically for landslide monitoring. It aims to validate the authenticity of the simulated flight paths and the correctness of the algorithms. Under the proposed simulation framework, we then test a novel flight path planning algorithm. The simulation results demonstrate that the model reconstruction obtained using the novel flight path algorithm exhibits more detailed textures, with a 3D model simulation accuracy ranging from 10 to 14 cm. Among them, the RMSE value of the novel flight route algorithm falls within the range of 10 to 11 cm, exhibiting a 2 to 3 cm improvement in accuracy compared to the traditional flight path algorithm. Additionally, it effectively reduces the flight duration by 9.3% under the same flight path compared to conventional methods. The results confirm that the simulation framework developed in this paper meets the requirements for landslide damage monitoring and validates the feasibility and correctness of the UAV flight path planning algorithm.

In the Unmanned Aerial Vehicle (UAV) system, finding a flight planning path with low cost and fast search speed is an important problem. However, in the complex three-dimensional (3D) flight environment, the planning effect of many algorithms is not ideal. In order to improve its performance, this paper proposes a UAV path planning algorithm based on improved Harris Hawks Optimization (HHO). A 3D mission space model and a flight path cost function are first established to transform the path planning problem into a multidimensional function optimization problem. HHO is then improved for path planning, where the Cauchy mutation strategy and adaptive weight are introduced in the exploration process in order to increase the population diversity, expand the search space and improve the search ability. In addition, in order to reduce the possibility of falling into local extremum, the Sine-cosine Algorithm (SCA) is used and its oscillation characteristics are considered to gradually converge to the optimal solution. The simulation results show that the proposed algorithm has high optimization accuracy, convergence speed and robustness, and it can generate a more optimized path planning result for UAVs.

In modern warfare, the use of UAVs for reconnaissance, search and rescue missions is very common, and it is essential to plan the flight path of UAVs. However, in the face of complex battlefield environment, the existing flight path planning algorithms have the problems of long time consumption and unstable path. Therefore, this paper studies the UAV flight path planning optimization in complex battlefield environment. First, we construct the battlefield environment model. Then, by analyzing the UAV flight constraints existing in battlefield environment, the objective function is obtained. And the problem of UAV flight path planning optimization is transformed into a nonlinear combinatorial optimization problem. On this basis, an Adaptive Adjustment Flight Path Planning algorithm (AA-FPP) is proposed. The AA-FPP algorithm adaptively adjusts the absorption coefficient of fireflies by using chaotic strategy. It adjusts the control position updating formula by using time-varying inertia weight to enhance its global searching ability. Then, random factors based on Boltzmann selection strategy are introduced to perturb the iterative solutions in AA-FPP. It expands the search space of the path and enhances the convergence efficiency. Finally, simulation results show that the AA-FPP algorithm can successfully plan a flight path that reduces static/dynamic threat intensity. And it has greater advantages in path stability and planning time consumption.

This paper puts forward a joint optimization algorithm of task assignment and flight path planning for a heterogeneous unmanned aerial vehicle (UAV) cluster in a multi-mission scenario (MMS). The basis of the proposed algorithm is to establish constraint and threat models of a heterogeneous UAV cluster to simultaneously minimize range and maximize value gain and survival probability in an MMS under the constraints of task payload, range, and task requirement. On one hand, the objective function for the heterogeneous UAV cluster within an MMS is derived and it is adopted as a metric for assessing the performance of the joint optimization in task assignment and flight path planning. On the other hand, since the formulated joint optimization problem is a multi-objective, non-linear, and non-convex optimization model due to its multiple decision variables and constraints, the roulette wheel selection (RWS) principle and the elite strategy (ES) are introduced in an ant colony optimization (ACO) to solve the complex optimization model. The simulation results indicate that the proposed algorithm is superior and more efficient compared to other approaches.

PurposeA major challenge for mission planning of aircraft is to generate flight paths in highly dynamic environments. This paper presents a new approach for online flight path planning with flight time constraints for fixed-wing UAVs. The flight paths must take into account the kinematic restrictions of the vehicle and be collision-free with terrain, obstacles and no-fly areas. Moreover, the flight paths are subject to time constraints such as predetermined time of arrival at the target or arrival within a specified time interval.Design/methodology/approachThe proposed flight path planning algorithm is an evolution of the well-known RRT* algorithm. It uses three-dimensional Dubins paths to reflect the flight capabilities of the air vehicle. Requirements for the flight time are realized by skillfully concatenating two rapidly exploring random trees rooted in the start and target point, respectively.FindingsThe approach allows to consider static obstacles, obstacles which might pop up unexpectedly, as well as moving obstacles. Targets might be static or moving with constantly changing course. Even a change of the target during flight, a change of the target approach direction or a change of the requested time of arrival is included.Originality/valueThe capability of the flight path algorithm is demonstrated by simulation results. Response times of fractions of a second qualify the algorithm for real-time applications in highly dynamic scenarios.

This paper addresses the flight path planning problem for multiple missiles engaging stationary targets in high-risk areas. Targets protected by air defence are preferably engaged by a fleet or swarm of missiles, not individual missiles. The concept of a swarm attack is that a large number of approaching missiles overwhelm air defence. The deployment of missiles is often part of a broader mission including further participants. Flight path planning is then an integral element of mission planning, requiring strict timing coordination of all members involved. The flight times of the missiles are dictated by the master planning. We present algorithms for offline planning and online re-planning of flight paths for a fleet of missiles with flight time constraints. The algorithms are based on an advanced bidirectional RRT* algorithm that generates risk-minimizing flight paths with predefined flight times. Online planning generates the flight paths of the fleet sequentially, maintaining a safety distance between the missiles to prevent mutual collision. Offline planning uses a global optimization approach to determine an optimal selection of flight paths from a large set of potential paths. The selection is performed by a branch and bound algorithm that determines optimal cliques in the path compatibility graph. The optimization is embedded in an iterative algorithm that allows to successively improve the mission success.

Manual bridge inspections are labour-intensive, hazardous, and costly. While unmanned aerial system (UAS) are promising to facilitate the process, current flight planning tools do not address the unique challenges of complex bridge geometries or GNSS-denied underdeck environments. We present ORBIT, an open-source toolkit for generating optimized waypoint routes specifically designed bridge inspection missions using only minimal prior data. ORBIT generates coordinated waypoint routes for overview and underdeck inspections, maintaining spatial overlap between datasets to facilitate accurate image alignment. This approach also allows the UAS to closely follow bridge side faces at constant offsets, optimizing data acquisition for damage detection tasks. The planning workflow supports integration of commonly available cross-sectional plans or satellite imagery, incorporates flexible safety zones, and exports missions in standard KML and KMZ formats for direct use even with off-the-shelf commercial drones. Field deployments on multiple concrete canal bridges demonstrate that the generated routes provide complete inspection coverage. Underdeck missions were successfully executed using a DJI Mavic 3 Enterprise, relying solely on its onboard IMU when GNSS was unavailable and achieving reliable operation for bridge spans up to 20 meters. By making ORBIT openly available, this work aims to enable safer, more precise, and scalable UAS-based bridge inspection, and to support future research in the field.https://github.com/ErToBar2/ORBIT

Uncrewed aircraft systems (UAS), also known as drones, have become increasingly popular in various applications due to their ability to access remote or challenging locations. A crucial aspect of UAS operations is flight path planning, which determines the trajectory the aircraft takes to achieve its mission objectives. However, the diverse use cases of UAS demand different planning approaches tailored to their specific requirements. This paper presents an overview over current research in UAS flight path planning and proposes a categorization of use cases based on their distinct goals and considerations. The terminology differentiates the goals of tasks between navigation-centric and data acquisition and also considers their context, domain, perspective, and sensors. We analyze existing approaches with a focus on UAS flight path planning for data acquisition using cameras, highlighting their strengths and limitations. This structured overview facilitates the understanding of the diverse landscape of UAS flight path planning and paves the way for the development of more targeted and effective solutions for various applications and their use cases. The analysis shows, that the term ”UAS flight path planning” is currently used in a variety of distinct use cases with diverging requirements, so a unified terminology should be established for clear communication.