Path Planning of Quadrotors in a Dynamic Environment Using a Multicriteria Multi-Verse Optimizer

Abstract

Paths planning of Unmanned Aerial Vehicles (UAVs) in a dynamic environment is considered a challenging task in autonomous flight control design. In this work, an efficient method based on a Multi-Objective Multi-Verse Optimization (MOMVO) algorithm is proposed and successfully applied to solve the path planning problem of quadrotors with moving obstacles. Such a path planning task is formulated as a multicriteria optimization problem under operational constraints. The proposed MOMVO-based planning approach aims to lead the drone to traverse the shortest path from the starting point and the target without collision with moving obstacles. The vehicle moves to the next position from its current one such that the line joining minimizes the total path length and allows aligning its direction towards the goal. To choose the best compromise solution among all the non-dominated Pareto ones obtained for compromise objectives, the modified Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is investigated. A set of homologous metaheuristics such as Multiobjective Salp Swarm Algorithm (MSSA), Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Particle Swarm Optimization (MOPSO), and Non-Dominated Genetic Algorithm II (NSGAII) is used as a basis for the performance comparison. Demonstrative results and statistical analyses show the superiority and effectiveness of the proposed MOMVO-based planning method. The obtained results are satisfactory and encouraging for future practical implementation of the path planning strategy.

Parallel Cooperative Coevolutionary Grey Wolf Optimizer for Path Planning Problem of Unmanned Aerial Vehicles

“…The general definition of such a problem is the generation of a path that guides the drone from a starting point A : (x 1 , y 1 , z 1 ) to a predefined destination B : (x n , y n , z n ). To ensure this, an environmental modeling is required [13,14,16,41]. The x-axis range (x 1 , x n ) is divided into n − 1 equal segments delimited by geometric perpendicular hyper-planes passing through the corresponding points {x 1 , x 2 , .…”

“…The connection of the different waypoints forming such a flight sequence leads to generating the complete flyable path. In this manner, the path planning problem can be reformulated as an optimization problem that consists in determining the optimal flight waypoints' sequences minimizing a previously defined performance criterion, i.e., shorter, collision-free and smoother flyable paths [14,41]. In this formulation, the decision variables of such a constrained optimization problem are defined as the vector of coordinates of the waypoints X = (y 2 , y 3 , .…”

“…Therefore, a shorter path remains desirable in all planning problems. To well formulate such a design goal, the corresponding objective function to be minimized is chosen as follows [14,41]:…”

“…Indeed, to ensure that the planned path is safe, the UAV drone must avoid all obstacles. On the other hand, to avoid the risk of being detected by the radars or missiles, a UAV cannot pass through the dangerous regions and/or fly over them [13,14,16,41]. Thus, such an obstacles' avoidance constraint is modeled by the following expression:…”

“…To handle the operational constraints ( 2) and (3) of the optimization problem (4), a static penalty function-based technique is used as follows [41]:…”