Multi-Objective Cooperated Path Planning of Multiple Unmanned Aerial Vehicles Based on Revisit Time

Abstract

This paper investigates multi-objective optimization of coordinated patrolling flight of multiple unmanned aerial vehicles in the vicinity of terrain, while respecting their performance parameters. A new efficient modified A-star (A*) algorithm with a novel defined criterion known as individual revisit time cell value is introduced and extended to the whole area of the 3D mountainous environment. As a contribution to solving trade-offs in the optimization problem, revisit time is conjugated with other contrary costs effective in flight planning through Pareto analysis. By introducing the revisit time and applying a specific setup to mitigate computational complexity, the proposed algorithm efficiently revisits the desired zones, which are more important to be revisited during the patrolling mission. The results of the introduced modified A* algorithm are compared in various scenarios with two different algorithms including a complete and optimal algorithm known as Dijkstra, and an evolutionary algorithm known as the genetic algorithm. Simulation results demonstrate that the proposed algorithm generates faster and more efficient trajectories in complex multiagent scenarios due to the introduced cell selection method and dynamic-based simplifications applied in this research.

Performance-based emergency landing trajectory planning applying meta-heuristic and Dubins paths

“…𝑋 𝑖+1 = { 𝑋 𝑖 ′ Pr i ≥ 𝑝 0 𝑋 𝑖 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 (28) where 𝑇 𝑘 is the temperature at the kth step of going through with the degraded solution ∆𝑓 𝑖 ≥ 0 that enables the solution to avoid the local minimum relates. It also allows for analysis of the whole part of the solution space affecting the cost value control parameter.…”

“…Dubins twodimensional (2D) shortest paths have been previously applied to generate the shortest paths for an aircraft in a threedimensional (3D) environment. Atkins [25] and some other researchers [26,12] Several other methods such as grid base, Dijkstra, numerical Hamilton Jacobi, Eikonal equation, and metaheuristic methods have been applied for path planning of moving vehicles with dynamic constraints in presence of obstacles [27][28][29]. The applicability of the aforementioned methods to aerial vehicles, especially in emergency conditions respecting high speeds of aerial vehicles and required safety, is still challenging.…”

Humanoid path planning on even and uneven terrains using an efficient memory-based gravitational search algorithm and evolutionary learning strategy

“…Vikas et al 17 further modified the existing GSA model to improve the diversity at the later stages. Of the different path planning approaches, Haghighi et al 38 implemented the modified A-star approach with individual revisit time cell value for multi-objective optimization of patrolling flights. The proposed approach was compared with others in different scenarios for its effectiveness.…”

Unmanned aerial vehicle formation obstacle avoidance control based on light transmission model and improved artificial potential field

“…In the same time, the proposed algorithm provides outstanding following performance and inherent collision avoidance pattern due to prioritized tracking. Haghighi et al (2021) proposed a new efficient modified A* algorithm with a novel defined criterion based on the three-dimensional mountainous environment, which can solve the cooperated multi-agent patrolling with online mapping and dynamic situation of cooperated agents. However, as the number of UAVs in the formation increases, the information that needs to be coordinated between UAVs in Haghighi et al (2021) will increase, which seems to affect the computational complexity of the algorithm.…”

“…Haghighi et al (2021) proposed a new efficient modified A* algorithm with a novel defined criterion based on the three-dimensional mountainous environment, which can solve the cooperated multi-agent patrolling with online mapping and dynamic situation of cooperated agents. However, as the number of UAVs in the formation increases, the information that needs to be coordinated between UAVs in Haghighi et al (2021) will increase, which seems to affect the computational complexity of the algorithm. A novel control function was proposed in Park and Yoo (2021) for a robust leader–follower formation tracking, which can guarantee the connectivity between the leader and the followers in the case of avoiding an obstacle.…”