Mission Planning for Emergency Rapid Mapping with Drones

Joint mission and route planning of unmanned air vehicles via a learning-based heuristic

The use of unmanned aerial vehicles (UAVs) has been increasingly permeating diverse areas. Swarms of heterogeneous UAVs have the potential to accomplish richer tasks due to the combination of varied resources. However, controlling a swarm of heterogeneous UAVs poses interesting challenges, such as the optimal allocation of resources, the geographical distribution of the components of the swarm, the timely alternation of team members and the complete mission plan of the UAV swarm. In this chapter we explore the use of SwarmOS, a middleware for edge computing, focused on the cooperative communication of heterogeneous devices. The SwarmOS middleware acts as a communication mediator and facilitator, creating an organic network of devices. SwarmOS is currently used for decentralized and distributed Internet of Things (IoT) applications in a variety of platforms and protocols, and in the present work, we extend its use to the mission plans of UAV swarms. To evaluate the use of SwarmOS for UAV swarms, we selected some relevant applications from the rural realm, farming and livestock. Along with the SwarmOS middleware we propose a specification of mission plans for those applications, using a rigorous and verifiable model inspired in interaction protocol and soft institutions, which were previously used in hazard prevention of UAV. The specification of the use cases will be discussed and analyzed to pave the way for future implementations of the proposed mission plans.

Swarms of Unmanned Aerial Vehicles (UAV) have been foreseen by multiple organizations to serve an important role in future air-based warfare and civilian operations. UAVs are less expensive than their piloted counterparts, provide greater flexibilities and remove the need for on-board pilot support. Efficient control of swarms opens a set of new challenges, such as automatic UAV coordination, efficient swarm monitoring and dynamic mission planning. In this paper, we investigate the problem of dynamic mission planning for a UAV swarm. An agent-based control framework is proposed, which employs a control agent for task assignment and multiple UAV agents for local task scheduling. A prototype simulation framework is implemented as a proof-of-concept. Experimentation with the framework suggests the effectiveness of swarm control using several mission planning mechanisms.

Multi-Depot Vehicle Routing Problem with Drones: Mathematical formulation, solution algorithm and experiments

Precise timing and precise location information are provided by Global Navigation Satellite Systems (GNSS) and play a crucial role in the positioning, navigation and data acquisition of most Unmanned Aerial Vehicles (UAV). GNSS functions include the following applications in UAVs: time-stamping and geo-referencing of collected data and images, synchronization of swarm flying and follow-me flights, determination of position and attitude in-flight, flight trajectory by following a pre-defined number of waypoints, mission planning, return home automatically without external control, avoidance of obstacles and geo-fencing. 
 Some of these critical operations have implications for the safety of the UAV, the surrounding environment and health and safety of people, for example UAVs threatening to bring down aircrafts at airports, which are no-fly zones for UAVs. The appropriate GNSS based function to avoid this is geo-fencing. Another example is obstacle avoidance to prevent collisions and damages both for the UAV and the obstacle, e.g. anything from a window pane, tree, human being, to a power line.
 In order to ensure health and safety it is thus important to ensure correct function of the navigation and the timing, under a wide variety of circumstances, and in different signal environments. There can be signal disturbances, such as obscurations by buildings or reflected GNSS signals, called multipath. The performance of timing and navigation based on GPS/GNSS can be tested and verified in a controlled and repeatable way in the laboratory with different types of test equipment. We will give an introduction to a wide range of potential threats to GNSS Positioning, navigation and timing and an overview of different test methods. In addition, we are presenting a method for time synchronization of drones to enable safe swarm and follow flights in UAVs.

Cellular-enabled unmanned aerial vehicles (UAVs) require almost continuous cellular network connectivity to fulfill their missions successfully. However, the area (e.g., rural) they fly over may have partial coverage, making the path planning of such UAV missions a challenging task. Recently a tolerable outage duration is taken into account for such UAVs, and the trajectory optimization under this outage duration is studied. However, these existing studies consider only a single UAV and focus on optimization of each UAV's own path separately even in multi-UAV scenarios. In this paper, we study the trajectory optimization problem for cellular-enabled UAVs by taking into account the collaboration among UAVs. That is, for a given set of UAVs, each with a mission to fly from a starting point to an ending point, we aim to optimize the total mission completion time for all UAVs such that none of them has a connection outage more than a threshold. We let UAVs collaborate and provide connectivity as relays to each other to solve their outage problem and shorten their trajectories. We first model and solve this problem using nonlinear programming after discretization of the problem. Since it takes longer to solve the problem with such an approach, we then provide a graph-based approximate solution that runs fast. Numerical results show that the proposed approximate solution provides close to optimal results and performs better than state-of-the-art solutions that consider each UAV separately without collaboration among UAVs.

Using multiple unmanned aerial vehicles (UAVs) to perform some tasks cooperatively has received growing attention in recent years. Task assignment is a difficult problem in mission planning. Multiple tasks assignment problem for cooperating homogeneous UAVs is considered as a traditional combinatorial optimization problem. This paper addresses the problem of assigning multiple tasks to cooperative homogeneous UAVs, minimizing the total cost and balancing the cost of each UAV. We propose a centralized task assignment scheme which is based on minimum spanning tree. This scheme involves two phases. In the first phase, we use the Kruskal algorithm and the breadth first search algorithm to assign all tasks to UAVs and get a proper initial task assignment solution. The second phase involves the Pareto optimization improvement in the solution generated from the first phase. For a single UAV, we use the dynamic programming algorithm to calculate the total cost of completing all assigned tasks. The performance of the proposed scheme is compared to that of heuristic simulated annealing algorithm. The simulation results show that the proposed scheme can solve the homogeneous multi-UAV cooperative task assignment problem effectively.

Unmanned aerial vehicles (UAVs) have become increasingly popular in the civil field, and building inspection is one of the most promising applications. In a rural area, the UAVs are assigned to inspect the surface of buildings, and an unmanned ground vehicle (UGV) is introduced to carry the UAVs to reach the rural area and also serve as a charging station. In this paper, the mission planning problem for UAVs and UGV systems is focused on, and the goal is to realize an efficient inspection of buildings in a specific rural area. Firstly, the mission planning problem (MPP) involving UGVs and UAVs is described, and an optimization model is established with the objective of minimizing the total UAV operation time, fully considering the impact of UAV operation time and its cruising capability. Subsequently, the locations of parking points are determined based on the information about task points. Finally, a hybrid ant colony optimization-genetic algorithm (ACO-GA) is designed to solve the problem. The update mechanism of ACO is incorporated into the selection operation of GA. At the same time, the GA is improved and the defects that make GA easy to fall into local optimal and ACO have insufficient searching ability are solved. Simulation results demonstrate that the ACO-GA algorithm can obtain reasonable solutions for MPP, and the search capability of the algorithm is enhanced, presenting significant advantages over the original GA and ACO.

Autonomous Unmanned Aerial Vehicles (UAV) for planetary exploration missions require increased onboard mission-planning and decision-making capabilities to access full operational potential in remote environments (e.g., Antarctica, Mars or Titan). However, the uncertainty introduced by the environment and the limitation of available sensors has presented challenges for planning such missions. Partially Observable Markov Decision Processes (POMDPs) are commonly used to enable decision-making and mission-planning processes that account for environmental, perceptional (extrinsic) and actuation (intrinsics) uncertainty. Here, we propose the UAV4PE framework, a testing framework for autonomous UAV missions using POMDP formulations. This framework integrates modular components for simulation, emulation, UAV guidance, navigation and mission planning. State-of-the-art tools such as python, C++, ROS, PX4 and JuliaPOMDP are employed by the framework, and we used python data-science libraries for the analysis of the experimental results. The source code and the experiment data are included in the UAV4PE framework. The POMDP formulation proposed here was able to plan and command a UAV-based planetary exploration mission in simulation, emulation and real-world experiments. The experiments evaluated key indicators such as the mission success rate, the surface area explored and the number of commands (actions) executed. We also discuss future work aimed at improving the UAV4PE framework, and the autonomous UAV mission planning formulation for planetary exploration.

In this paper, a mission planning system is presented that generates mission plans for a group of unmanned aerial vehicles (UAVs) to provide continuous surveillance over an urban area. Given the information of terrain and buildings in the target area, a two-stage approach is employed to solve the problem. In the first stage, a set of camera locations called the vantage set is generated that provides complete coverage of the target area. In the second stage, one or several UAVs are determined to collectively share the vantage set and their individual paths are generated to carry out the continuous surveillance duty. In both stages, evolutionary algorithms (genetic algorithm for vantage set generation and ant colony system for UAV/path planning) are used to search for the optimal solution. During the search, constraints such as the flying capabilities of UAVs and collision avoidance are imposed to guarantee the feasibility of the final result.

In this paper, the cooperative multi-task online mission planning for multiple Unmanned Aerial Vehicles (UAVs) is studied. Firstly, the dynamics of unmanned aerial vehicles and the mission planning problem are studied. Secondly, a hierarchical mechanism is proposed to deal with the complex multi-UAV multi-task mission planning problem. In the first stage, the flight paths of UAVs are generated by the Dubins curve and B-spline mixed method, which are defined as “CBC)” curves, where “C” stands for circular arc and “B” stands for B-spline segment. In the second stage, the task assignment problem is solved as multi-base multi-traveling salesman problem, in which the “CBC” flight paths are used to estimate the trajectory costs. In the third stage, the flight trajectories of UAVs are generated by using Gaussian pseudospectral method (GPM). Thirdly, to improve the computational efficiency, the continuous and differential initial trajectories are generated based on the “CBC” flight paths. Finally, numerical simulations are presented to demonstrate the proposed approach, the designed initial solution search algorithm is compared with existing methods. These results indicate that the proposed hierarchical mission planning method can produce satisfactory mission planning results efficiently.

Satellite imaging has always been a challenging task and at the current time being there are very few alternatives for capturing vertical images of Earth's vast landscape. Aerial photography by using multiple unmanned aerial vehicles (UAV) might be one of the solutions. This research aims to study, improve and demonstrate autonomy for UAVs by using specialized software and apparatus. The mission can be utilized to allow the UAVs to fly sequentially in the same pathway without colliding and creating the highest amount of coverage, which it is important for some remote sensing applications. Certain benefits can be obtained from using UAV rather than satellite imaging such as lower cost of overall equipment than a space satellite and higher resolution pictures due to flexibility in altitude operations. The results of implement the autonomous UAV has compare between path of actual flight and the setting way-points and it express exceptional results by presenting less of Root Mean Square Error.

In recent years, Unmanned Aerial Vehicle (UAV) technology has become more and more mature, and the demand for UAV s in all walks of life is also increasing. UAV formations or swarms have become an emerging organizational form. As one of the cores of multi-UAV cooperative missions, mission planning has attracted more and more researchers' attention. The multi-UAV dynamic mission assignment method based on the clustering algorithm proposed in this paper can realize the reasonable planning of the multi-UAV reconnaissance mission. First of all, this paper establishes a problem model of the reconnaissance mission. Then based on the trajectory prediction of targets, the improved k-means clustering algorithm is proposed to classify the mission targets. For the targets assigned to each UAV, we propose the dynamic Dijkstra algorithm to optimize the targets reconnaissance sequence. Finally, we designed a simulation experiment and verified the effectiveness of the algorithm proposed in this paper.

Trajectory prediction is critical for ensuring the safety, reliability, and scalability of Unmanned Aerial Vehicle (UAV) in urban environments. Despite advances in deep learning, existing methods often struggle with dynamic UAV conditions, such as rapid directional changes and limited forecasting horizons, while lacking comprehensive real-time validation and generalization capabilities. This study addresses these challenges by proposing a gated recurrent unit (GRU)-based deep learning framework optimized through Look_Back and Forward_Length labeling to capture complex temporal patterns. The model demonstrated state-of-the-art performance, surpassing existing unmanned aerial vehicles (UAV) and aircraft trajectory prediction approaches, including FlightBERT++, in terms of both accuracy and robustness. It achieved reliable long-range predictions up to 4 s, and its real-time feasibility was validated due to its efficient resource utilization. The model’s generalization capability was confirmed through evaluations on two independent UAV datasets, where it consistently predicted unseen trajectories with high accuracy. These findings highlight the model’s ability to handle rapid maneuvers, extend prediction horizons, and generalize across platforms. This work establishes a robust trajectory prediction framework with practical applications in collision avoidance, mission planning, and anti-drone systems, paving the way for safer and more scalable UAV operations.

Influenced by complex terrain conditions of combat environments and constrained by the level of communication technology, communication among unmanned aerial vehicles (UAV) is greatly restricted. In light of this situation, mission planning for UAV swarms under limited communication has become a difficult problem. This paper introduces motifs as the basic unit of configuration and proposes a motif-based mission planning method considering dynamic reconfiguration. This method uses multidimensional dynamic list scheduling algorithm to generate a mission planning scheme based on the motif-based swarm configuration solution. Then it incorporates order preserved operators with NSGA-III algorithm to find Pareto front solutions of all possible mission planning schemes. The feasibility of this mission planning method is validated through a case study.