UAV Mission Planning Resistant to Weather Uncertainty

Abstract

Fleet mission planning for Unmanned Aerial Vehicles (UAVs) is the process of creating flight plans for a specific set of objectives and typically over a time period. Due to the increasing focus on the usage of large UAVs, a key challenge is to conduct mission planning addressing changing weather conditions, collision avoidance, and energy constraints specific to these types of UAVs. This paper presents a declarative approach for solving the complex 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.

Hierarchical Mission Planning with a GA-Optimizer for Unmanned High Altitude Pseudo-Satellites

“…Therefore, its evolutionary encoding has a different interpretation and is customized for HAPSs instead of satellites or other types of UAVs. Besides, similar to other works that take into account the uncertainty associated to weather conditions [44][45][46] or to other elements of the mission (e.g., the target location and movement in search and rescue missions [47,48] or the probability of target detection and destruction in hostile environments [49,50]), in this work the uncertainties are incorporated into the models used to evaluate how probable is that each HAPS is at a mission area at a given time, which affects the outcome of the objective and constraint values.…”

Reactive Planning-Driven Approach to Online UAVs Mission Rerouting and Rescheduling

“…In the considered research domain, two directions can be distinguished. The first focuses on planning methods, taking into account weather uncertainty (e.g., investigating the manner in which energy consumption in UAV deliveries is affected by windy environmental conditions), and searching for route planning methods resistant to ad hoc changes in procurement conditions (e.g., studying the impact of changes in order terms and volumes to complete the planned delivery mission) [6,7,11]. In turn, the second research direction deals with methods of design and utilising networks of autonomous UAVs with complementary sensing and actuation equipment to collectively perform complicated tasks, i.e., the flying ad-hoc network (FANET) [30].…”

“…In this paper, we consider the planning of the fleet mission problem for UAVs with highly dynamic and unpredictable environment constraints [1][2][3][4][5]. Typical disruptions in deliveries by UAVs may be caused by ad hoc changes to the deliveries ordered or changing weather conditions, which affect the energy consumption of UAVs and result in their shorter range due to the depletion of batteries [6,7]. For this reason, routing and scheduling a UAV fleet in partially known and unpredictable environments should take into account both projected changes in orders and weather conditions and selected categories of interference not included in previous forecasts.…”

“…Routes determined in proactive planning guarantee the achievement of the planned mission's goal in terms of environmental parameters, which change at predetermined intervals. Scenarios corresponding to planned reactive rules do not guarantee reactive end-to-end paths employed in the routing process while adapting to changes in the environment during the execution of a mission [7,[9][10][11][12][13][14][15][16]. In particular, the reactive routing strategies linking "route discovery" (proactive route planning) and "route maintenance" (adopting reactive rules) concepts [17] are responsible for the robustness of UAVs in terms of the changes in weather conditions.…”

UAV Swarm Intelligence: Recent Advances and Future Trends

“…For solving such problems, Ramirez-Atencia C uses an improved multi-objective genetic algorithm to deal with the mission planning problem [41]. Amila Thibbotuwawa studied a declarative approach to cope with the weather uncertainty during task execution [42]. Based on combined optimization mode, the method based on the direction graph and a new meta-heuristic optimization algorithm, namely the modified two-part wolf pack search algorithm (MTWPS), can be used to solve the problem, which can reduce the large number of UAV targets Simulation time [43].…”