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A 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.

The 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.

The 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.

The 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.

The purpose of this paper is to present a new approach for finding a minimum-length trajectory for an autonomous unmanned air vehicle or a long-range missile from a release point with specified release conditions to a destination with specified approach conditions. The trajectory has to avoid obstacles and no-fly zones and must take into account the kinematic constraints of the air vehicle.

A discrete routing model is proposed that represents the airspace by a sophisticated network. The problem is then solved by applying standard shortest-path algorithms.

In contrast to the most widely used grids, the generated networks allow arbitrary flight directions and turn angles, as well as maneuvers of different strengths, thus fully exploiting the flight capabilities of the aircraft. Moreover, the networks are resolution-independent and provide high flexibility by the option to adapt density.

As an application, a concept for in-flight replanning of flight paths to changing destinations is proposed. All computationally intensive tasks are performed in a pre-flight planning prior to the launch of the mission. The in-flight planning is based entirely on precalculated data, which are stored in the onboard computer of the air vehicle. In particular, no path finding algorithms with high or unpredictable running time and uncertain outcome have to be applied during flight.

The paper presents a new network-based algorithm for flight path optimization that overcomes weaknesses of grid-based approaches and allows high-quality solutions. The method can be applied for quick in-flight replanning of flight paths.

The purpose of this research is to propose a novel method to plan paths of unmanned aerial vehicle (UAV) formations. This is to make use of the aerodynamic advantage of formation flight to reduce energy consumption of UAVs.

The method proposed in this research make use of the fact that, under certain conditions, the regions where if a UAV rendezvous or separates with another UAV would save energy by formation flying can be analytically calculated. The intersections of these regions are used to decide which UAV are to fly in the same formation. This combination of which UAV are to fly together and what order they join/part from the formation is called the topology of the problem.

The proposed method was proved to be effective in identifying efficient topologies, with the majority of the topologies selected falling below 5 percent error rate in terms of energy.

The originality of this research lies in the fact that the proposed method is completely visualised – all the necessary information to organise formations is visualised in the envelopes. Still, the proposed method was proved to be effective in selecting efficient topologies.

Airdrop operation has become an important transportation mode due to its mobility and rapidity and mission planning is one of the critical steps in the preparation of an airdrop operation. The purpose of this paper is to propose an efficient mission planning method for airdrop operation using multiple vehicles.

A hierarchical mission planning method is proposed. According to the objectives of the action, the mission planning is divided into three planning levels to form the hierarchical structure and the constraints are distributed among them. By doing so, the proposed approach converts the original mission planning problem to a constrained optimization problem, which is solvable using existing mathematical methods.

On the basis of analysis, the mathematic models of three planning levels are established. Each level has its own optimization objective, taking part of constraints into account. The integrated mission scheme had been obtained step by step.

This paper systematically tackles the complicated multiple vehicles airdrop mission planning problem, and it provides a platform for optimizing the outcomes. The mathematical models established in this paper could apply in a variety of more complex mission scenarios.

This paper fulfils an urgent need to study how the advantages of airdrop operation can be maximized through planning airdrop mission schemes carefully.

Artificial intelligence (AI)-based systems can save the lives of many people by assessing the safety of flight paths. Unfortunately, the world witnessed a horrible event in January 2020 with the case of flight 752 of Ukrainian International Airlines from Tehran to Kiev and it has prompted us to ask how AI can prevent such events by warning to flight path planners. This paper aims to propose a framework for assessing the safety of flight paths from a shooting of an airplane by air defense systems installed on the path. Unlike the existing studies, this study takes a new look at pre-flight risk assessment by using textual information in social and news networks. To this end, the authors use existing information retrieval techniques to identify high flight risk areas by examining the news articles, comments, posts, tweets, etc., in social media and then estimate the probability of targeting a passenger aircraft by the air defense systems probably installed on high-risk areas with the help of a statistical model. This estimation can then be used by fight planners to avoid such events.

To design a framework for estimating the probability of a fatal shooting of an airplane by air defense systems installed on its flight path, the authors have used the idea of information retrieval in conjunction with statistical methods. The authors have extracted some significant variables in the shooting of flights and proposed an AI-based framework to estimate the probability of a fatal shooting of an airplane during its flight and sketched a case study for using machine learning approaches to assist with flight path planning. As a case study, the authors covered flight 752 to explain the usefulness of the proposed framework in this context.

Unlike the existing methods, this study investigates flight path safety assessment from the social media and crowdsourcing perspective. In this study, the authors proposed an AI-based framework to avoid aviation hazards by estimating the probability of a shooting of an airplane by air defense systems installed on its flight path. Moreover, this study was designed to show how estimating the safety of flight paths by using AI-based methods can help flight planners to avoid such events and gain further insights into the use of AI-based systems in pre-flight risk assessment.

The idea behind the proposed method is original and as the authors’ best knowledge, there is no similar framework using social media for flight path safety assessment.

This paper aims to propose an environmentally adaptive trajectory planning system considering the dynamic characteristics of unmanned aerial vehicles (UAVs) and the distance between obstacles and the UAV. The system generates a smooth and safe flight trajectory online.

First, the hybrid A* search method considering the dynamic characteristics of the quadrotor is used to find the collision-free initial trajectory. Then, environmentally adaptive velocity cost is designed for environment-adaptive trajectory optimization using environmental gradient data. The proposed method adaptively adjusts the autonomous flight speed of the UAV. Finally, the initial trajectory is applied to the multi-layered optimization framework to make it smooth and dynamically viable.

The feasibility of the designed system is validated by online flight experiments, which are in unknown, complex situations.

The proposed trajectory planning system is integrated into a vision-based quadrotor platform. It is easily implementable onboard and computationally efficient.

A hybrid A* path searching method is proposed to generate feasible motion primitives by dispersing the input space uniformly. The proposed method considers the control input of the UAV and the search time as the heuristic cost. Therefore, the proposed method can provide an initial path with the minimum flying time and energy loss that benefits trajectory optimization. The environmentally adaptive velocity cost is proposed to adaptively adjust the flight speed of the UAV using the distance between obstacles and the UAV. Furthermore, a multi-layered environmentally adaptive trajectory optimization framework is proposed to generate a smooth and safe trajectory.

This paper aims to propose an online local trajectory planner for safe and fast trajectory generation that combines the jerk-limited trajectory (JLT) generation algorithm and the particle swarm optimization (PSO) algorithm. A trajectory switching algorithm is proposed to improve the trajectory tracking performance. The proposed system generates smooth and safe flight trajectories online for quadrotors.

First, the PSO algorithm method can obtain the optimal set of target points near the path points obtained by the global path searching. The JLT generation algorithm generates multiple trajectories from the current position to the target points that conform to the kinetic constraints. Then, the generated multiple trajectories are evaluated to pick the obstacle-free trajectory with the least cost. A trajectory switching strategy is proposed to switch the unmanned aerial vehicle (UAV) to a new trajectory before the UAV reaches the last hovering state of the current trajectory, so that the UAV can fly smoothly and quickly.

The feasibility of the designed system is validated through online flight experiments in indoor environments with obstacles.

The proposed trajectory planning system is integrated into a quadrotor platform. It is easily implementable onboard and computationally efficient.

The proposed local planner for trajectory generation and evaluation combines PSO and JLT generation algorithms. The proposed method can provide a collision-free and continuous trajectory, significantly reducing the required computing resources. The PSO algorithm locally searches for feasible target points near the global waypoint obtained by the global path search. The JLT generation algorithm generates trajectories from the current state toward each point contained by the target point set. The proposed trajectory switching strategy can avoid unnecessary hovering states in flight and ensure a continuous and safe flight trajectory. It is especially suitable for micro quadrotors with a small payload and limited onboard computing power.

The purpose of this research is to explore how to visually represent human decision-making processes during the performance of indoor building inspection flight operations using drones.

Data from expert pilots were collected using a virtual reality drone flight simulator. The expert pilot data were studied to inform the development of an interactive 2D representation of drone flight spatial and temporal data – InDrone. Within the InDrone platform, expert pilot data were visually encoded to characterize key pilot behaviors in terms of pilots' approaches to view and difficulties encountered while detecting the inspection markers. The InDrone platform was evaluated using a user-center experimental methodology focusing on two metrics: (1) how novice pilots understood the flight approaches and difficulties contained within InDrone and (2) the perceived usability of the InDrone platform.

The results of the study indicated that novice pilots recognized inspection markers and difficult-to-inspect building areas in 63% (STD = 48%) and 75% (STD = 35%) of the time on average, respectively. Overall, the usability of InDrone presented high scores as demonstrated by the novice pilots during the flight pattern recognition tasks with a mean score of 77% (STD = 15%).

This research contributes to the definition of visual affordances that support the communication of human decision-making during drone indoor building inspection flight operations. The developed InDrone platform highlights the necessity of defining visual affordances to explore drone flight spatial and temporal data for indoor building inspections.

The purpose of this paper is to present a novel approach for trajectory tracking of UAVS. Research on unmanned aircraft is constantly improving the autonomous flight capabilities of these vehicles to provide performance needed to use them in even more complex tasks. The UAV path planner (PP) plans the best path to perform the mission. This is a waypoint sequence that is uploaded on the flight management system providing reference to the aircraft guidance, navigation and control system (GNCS). The UAV GNCS converts the waypoint sequence in guidance references for the flight control system (FCS) that, in turn, generates the command sequence needed to track the optimum path.

A new guidance system (GS) is presented in this paper, based on the graph search algorithm kinematic A* (KA*). The GS is linked to a nonlinear model predictive control (NMPC) system that tracks the reference path, solving online (i.e. at each sampling time) a finite horizon (state horizon) open loop optimal control problem with genetic algorithm (GA). The GA finds the command sequence that minimizes the tracking error with respect to the reference path, driving the aircraft toward the desired trajectory. The same approach is also used to demonstrate the ability of the guidance laws to avoid the collision with static and dynamic obstacles.

The tracking system proposed reflects the merits of NMPC, successfully accomplishing the task. As a matter of fact, good tracking performance is evidenced, and effective control actions provide smooth and safe paths, both in nominal and off-nominal conditions.

The GNCS presented in this paper reflects merits of the algorithms implemented in the GS and FCS. As a matter of fact, these two units work efficiently together providing fast and effective control to avoid obstacles in flight and go back to the desired path. KA* was developed from graph search algorithms. Maintaining their simplicity, but improving their search logics, it represents an interesting solution for online replanning. The results show that the GS uploads the collision avoidance path continuously during flight, and it obtains straightforward the reference variables for the FCS, thanks to the KA* model.

Obstacle and wind field are common environmental factors for mini unmanned helicopter (MUH) flight. This paper aims to develop a trajectory planning approach guiding MUH to avoid static and dynamic obstacles and to fly in steady uniform or boundary-layer wind field.

An optimal control model including a nonlinear flight dynamics model and a cubic obstacle model is established for MUH trajectory planning. Radau pseudospectral method is used to generate the optimal trajectory.

The approach can plan reasonable obstacle-avoiding trajectories in obstacle and windy environments. The simulation results show that high-speed wind fields increase the flight time and fluctuation of control inputs. If boundary-layer wind field exists, the trajectory deforms significantly and gets closer to the ground to escape from the strong wind.

The key innovations in this paper include a cubic obstacle model which is straightforward and practical for trajectory planning and MUH trajectory planning in steady uniform wind field and boundary-layer wind field. This study provides an efficient solution to the trajectory planning for MUH in obstacle and windy environments.