A Solution Approach for UAV Fleet Mission Planning in Changing Weather Conditions

Abstract

With a rising demand for utilizing unmanned aerial vehicles (UAVs) to deliver materials in outdoor environments, particular attention must be given to all the different aspects influencing the deployment of UAVs for such purposes. These aspects include the characteristics of the UAV fleet (e.g. size of fleet, UAV specifications and capabilities), the energy consumption (highly affected by weather conditions and payload) and the characteristics of the network and customer locations. All these aspects must be taken into account when aiming to achieve deliveries to customers in a safe and timely manner. However, at present, there is a lack of decision support tools and methods for mission planners that consider all these influencing aspects together. To bridge this gap, this paper presents a decomposed solution approach, which provides decision support for UAVs’ fleet mission planning. The proposed approach assists flight mission planners in aerospace companies to select and evaluate different mission scenarios, for which flight-mission plans are obtained for a given fleet of UAVs, while guaranteeing delivery according to customer requirements in a given time horizon. Mission plans are analyzed from multiple perspectives including different weather conditions (wind speed and direction), payload capacities of UAVs, energy capacities of UAVs, fleet sizes, the number of customers visited by a UAV on a mission and delivery performance. The proposed decision support-driven declarative model supports the selection of the UAV mission planning scenarios subject to variations on all these configurations of the UAV system and variations in the weather conditions. The computer simulation based experimental results, provides evidence of the applicability and relevance of the proposed method. This ultimately contributes as a prototype of a decision support system of UAVs fleet-mission planning, able to determine whether is it possible to find a flight-mission plan for a given fleet of UAVs guaranteeing customer satisfaction under the given conditions. The mission plans are created in such a manner that they are suitable to be sent to Air Traffic Control for flight approval.

UAV Mission Planning Resistant to Weather Uncertainty

“…The approach has been tested on several examples, analyzing how customer satisfaction is influenced by different values of the mission parameters, such as the fleet size, travel distance, wind direction, and wind speed. Computational experiments show the results that allow assessing alternative strategies of UAV mission planning.The increasing degree of autonomy and automation has made a demand for faster and safer systems to handle complex UAV operations.In using UAV technology to address the societal challenges through the making seamless flow of transport operations, practical constraints such as weather conditions and energy consumption make the problem highly complex and intractable [3]. Even though UAV technologies provide the more flexible transfer of goods between locations, these generate new challenges in the organization and maintenance of the planned routes and schedules [5,6].…”

“…UAV fleet mission planning problems are by their nature an extension of the well-known Vehicle Routing Problem (VRP). However, these problems have the added complexity of combining routing and scheduling, three-dimensional operations, and non-linear fuel consumption [3,4,7]. The classical VRP is well-studied and the methods and approaches found within this domain are still very much applicable for the advancement of new technology in the area of UAV operations.…”

“…The classical VRP is well-studied and the methods and approaches found within this domain are still very much applicable for the advancement of new technology in the area of UAV operations. However, mission planning for UAV fleets must consider a number of constraints and operating conditions rarely seen in the traditional VRP and operating conditions and limitations not found in other transportation means [3]. Some central examples of this are the constraints on UAV range that depend on weather conditions, airspace regulations and restrictions, as well as congestion in terms of collision avoidance and safety distance, and the UAV characteristics such as airspeed, maximum payload, energy capacity, physical dimensions, etc.…”

“…Some central examples of this are the constraints on UAV range that depend on weather conditions, airspace regulations and restrictions, as well as congestion in terms of collision avoidance and safety distance, and the UAV characteristics such as airspeed, maximum payload, energy capacity, physical dimensions, etc. [3,4,7,8]. In UAV mission planning, it is necessary to address weather conditions [9,10] and changes to these weather conditions can potentially strongly influence the solution strategy for the UAV mission planning, especially wind direction and speed as they directly impact energy consumption and flight characteristics [3,11].…”

“…In UAV mission planning, it is necessary to address weather conditions [9,10] and changes to these weather conditions can potentially strongly influence the solution strategy for the UAV mission planning, especially wind direction and speed as they directly impact energy consumption and flight characteristics [3,11]. Thus, UAV mission planning strategies must estimate energy consumption to identify the set of all reachable destinations [3,4]. As weather is by its nature uncertain, delivery services should accommodate this uncertainty through planning UAV fleet missions as a sequence of repeatedly executed UAV routes and schedules [4].…”