Mission Planning for a large number of Unmanned Aerial Vehicles (UAVs) involves a set of locations to visit in di erent time intervals, and the actions that a vehicle must perform depending on its features and sensors. Analyzing how humans solve this problem is sometimes hard due to the complexity of the problem and the lack of data available. This paper presents a summary of a serious videogame-based framework created to assess the quality of the mission plans designed by players, comparing them against the optimal solutions obtained by a Multi-Objective Optimization algorithm.
The study of Unmanned Aerial Vehicles (UAVs) is constantly increasing
nowadays. These technologies o er many potential applications in numerous elds as
monitoring coastal frontiers, road tra c, disaster management, etc [
The fast evolution of UAV systems is leading to a shortage of quali ed
operators. Thus, it is necessary to re-design the current training process to meet that
demand, making UAV operations more accessible and available for a less limited
pool of individuals, which may include high-skilled videogame players [
This work presents a summary of a previous work [
Modern approaches formulate the Mission Planning problem as a Constraint
Satisfaction Problem (CSP) [
The rest of the paper is structured as follows: section 2 describes how a mission is de ned in the UAV domain. Section 3 describes the game developed to simplify the Multi-UAV Cooperative Mission Planning Problem (MCMPP) problem and collect players Mission Plans. Section 4 explains the experiments performed and the experimental results obtained. Finally, last section presents the nal analysis and conclusions of this work. 2
The MCMPP is de ned as a number n of tasks to accomplish for a team of m UAVs. There are di erent type of tasks, such as exploring a speci c area or searching for an object in a zone. These tasks can be carried out thanks to the sensors available on the UAVs performing the mission. Each task is performed in a speci c geographic zone and a speci c time interval.
In addition, the vehicles performing the mission has some features that must be considered to check if a mission plan is correct. These features include the initial position, the initial fuel, the available sensors and one or more ight pro les. A ight pro le speci es for a vehicle at a moment its speed, its fuel consumption rate and its altitude.
Figure 1 shows an assignment of a UAV u to two tasks i and j. In this assignment it is necessary to assure that u has enough fuel and the sensors needed to perform both tasks and then return to its initial position. To ensure this, it is necessary to compute the distance du2i from the initial position of u to the entry point of task i and then take the fuel consumption rate from the ight pro le in order to compute the fuel consumed traversing this path. In addition, the speed vu from the ight pro le is used to compute the path duration.
Then, having the duration i of task i and the speed vi given by the ight pro le of the sensor used to perform the task, we can deduce the distance traversed by the UAV during the task performance, and therefore, using the fuel consumption rate of sensor's ight pro le, deduce the fuel consumed too. Next, the previous steps are repeated with task j.
Finally, it is necessary to compute the fuel consumption and ight time for the return of the UAV from the last task performed to its initial position.
When considering MCMPP as an optimization problem, the variables to minimize are the total fuel consumption and the makespan of the mission, i.e. the time elapsed since the mission start time until the mission is nished.
In previous works [
The game created to accomplish the MCMPP problem has been designed
focusing on the accesibility that professional mission planners lack of. It is based
on the multi-UAV simulation environment Drone Watch And Rescue, that we
designed in order to extract and analyze data from the user interactions [
Figure 2 shows a screenshot of Mission Planning Scenario in the game. This screen can be divided into ve distinct parts: 1. Main Screen: Displays graphically the Mission Scenario. 2. Waypoints panel : Shows the ying path of the selected UAV. 3. Plan submission button : Submits and saves the player's Mission Plan. 4. UAV's panel : Displays basic information and sensors of the selected UAVs. 5. Task Panel : Displays basic information and sensors of the selected task. 6. Console Panel : Logs the result of the player's interactions during a gameplay
To achieve an intuitive and quick understanding of the di erent controls available in the game, almost all of them are activated by doing mouse clicks on the game's Main screen. Below is detailed the whole set of game controls: { Select UAV : Allows the player to see the UAV current path and information. { Select Task : Allows the player to see the task information. { Assign/Unassign UAV to Task { Submit Plan: Submits and saves the current Mission Plan.
The game has been developed using web development technologies from the eld of videogames. Their main advantages include the portability of the game between both desktop and mobile systems, and a high availability: using any web browser with HTML5 capabilities, a user can access the URL where the game is hosted and play it without installing any additional software.
However, it is important to note the limitations of this type of technologies.
The system requirements on a videogame are much higher than those of a
common web application, and current Javascript engines, despite being more and
more powerful, yet have notorious performance troubles when running
computeintensive jobs. Because of this, the game has been designed with a 2-level
architecture (server-client), based on the design patterns used in the development
of multi-user real time applications and videogames [
In this work, the main goal of the experimentation is to rank the quality of
the Mission Plans designed by players in the video game described in section
3 against those obtained automatically and optimally by a MOBB algorithm,
detailed in the complete work [
The Mission Scenario used in this experiment features 8 tasks to be assigned to 5 UAVs scattered throughout the map. A graphical representation of this Mission Scenario can be seen, as a game screenshot, in Figure 2.
In this scenario, we must compute the optimal mission plans in terms of the
variables Makespan and Fuel Consumption. For this aim, we used the MOBB
algorithm developed in [
To evaluate the quality of a player's Mission Plan, we get its Makespan and Fuel consumption values, normalize them into [0; 1], and then compute the Euclidean distance of such values to the also normalized Makespan-Fuel consumption POF calculated before. The player's plan quality will represent his score in the game, and will allow us to compare gameplays.
To carry out this experiment, a set of 15 players submitted a Mission Plan
playing the video game developed. None of them had knowledge in the eld of
MCMPP, and only received a brief tutorial about the game objective and the
game controls. Figure 3 shows the performance of each player's gameplay as a
point in the Makespan-Fuel space. The closer a point is to the POF, the better
rank the player will have. Table 1 shows the rst ranking positions numerically.
The complete ranking is shown in [
The results prove there is not a dominant planning style in terms of the optimization variables focused by the players. Most of the points are located at the center of the space, which means that the general trend that a novice player follows in this type of problems is balancing the values to optimize. It is also remarkable that the Mission Plans are generally quite close to the POF. 5
This paper has presented a summary of the contributions made by some published works in the eld of the Multi-UAV Cooperative Mission Planning Problem, specially focused on assessing user performance when designing plans. A video-game based framework is created to make this problem understandable for non-expert users, and to rank and compare player plans against the optimal ones computed by a Multi-Objective Optimization algorithm.
As future work, we intend to extend the video game to allow the creation of more complex plans, to introduce some gami cation elements (as tutorials and levels) that make it even more accessible, and to include elements that improve the analysis of the players, as identi cations to track the evolution of their gameplays, or time spent measurements to rank the player's speed.
This work is supported by the Spanish Ministry of Science and Education under Project Code TIN2014-56494-C4-4-P, Comunidad Autonoma de Madrid under project CIBERDINE S2013/ICE-3095, and Savier an Airbus Defense & Space project (FUAM-076914 and FUAM-076915). The authors would like to acknowledge the support obtained from Airbus Defence & Space, specially from Savier Open Innovation project members: Jose Insenser, Gemma Blasco, Cesar Castro and Juan Antonio Henr quez.