Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms

Abstract

Due to recent booming of UAVs technologies, these are being used in many fields involving complex tasks. Some of them involve a high risk to the vehicle driver, such as fire monitoring and rescue tasks, which make UAVs excellent for avoiding human risks. Mission Planning for UAVs is the process of planning the locations and actions (loading/dropping a load, taking videos/pictures, acquiring information) for the vehicles, typically over a time period. These vehicles are controlled from Ground Control Stations (GCSs) where human operators use rudimentary systems. This paper presents a new Multi-Objective Genetic Algorithm for solving complex Mission Planning Problems (MPP) involving a team of UAVs and a set of GCSs. A hybrid fitness function has been designed using a Constraint Satisfaction Problem (CSP) to check if solutions are valid and Pareto-based measures to look for optimal solutions. The algorithm has been tested on several datasets optimizing different variables of the mission, such as the makespan, the fuel consumption, distance, etc. Experimental results show that the new algorithm is able to obtain good solutions, however as the problem becomes more complex, the optimal solutions also become harder to find.

Fair Task Allocation When Cost of Task Is Multidimensional

“…There are some optimization algorithms that can be used to solve multidimensional cost problem through multi-objective optimization models. The authors of [26] used multi-objective Genetic Algorithms (GA) to solve multi-UAV mission planning. The authors of [27] proposed a methodology with heuristic based on Earliest Available Time algorithm to solve the UAV scheduling problem in an indoor environment incorporated Particle Swarm Optimization (PSO) algorithm, with an objective of minimizing the makespan.…”

Combining an Extended SMAA-2 Method with Integer Linear Programming for Task Assignment of Multi-UCAV under Multiple Uncertainties

“…The constraints expressed in Equations (4)-(6) guarantee a one-to-one relationship between the UCAVs and the targets. The constraints in Equations (8)- (12) define the method to aggregate criteria value matrices.…”

“…They include the mixed integer linear programming (MILP) formulation [4][5][6], the branch and bound tree search algorithm [7,8], and the dynamic programming algorithm [9] among others. Most research to date on the application of heuristic (metaheuristic) algorithms have either extracted some specific search rules based on the properties of the problem to obtain optimal or suboptimal solutions rapidly or introduced some local search mechanism to the basic algorithm framework to improve the solution quality, such as tabu search algorithms [10], auction algorithms [11], genetic algorithms [12][13][14][15], ant colony algorithms [16], and particle swarm optimization [17,18]. The algorithms mentioned above-whether belonging to exact algorithms or heuristic (metaheuristic) algorithms-have demonstrated the ability to provide optimal or suboptimal solutions for task assignment problems of UAVs or UCAVs in various mission scenarios but may become difficult to apply when there are uncertain parameters related to mission scenarios.…”

The UAV flight route optimization by taking into consideration the wind influence

“…In [4], the comparison of Monte Carlo methods, reduction of rows and columns, and averaged coefficients for solving the commercial traveler's task during the planning of the UAV route is carried out. In [5][6] the optimal planning of the complex tasks implementation of several UAVs is considered. The article [7] considers the problem of minimizing the flight time of a small UAV on the route in the presence of a statically changing wind.…”