Collision free 4D path planning for multiple UAVs based on spatial refined voting mechanism and PSO approach

For large-scale Unmanned Aerial Vehicle (UAV), it is a challenging problem to automatically generate an optimal path considering the multi-constraints. In order to improve the efficiency and optimality of conventional A * algorithm, Bi-directional adaptive A * algorithm is proposed in this paper. Regarding to the defect of expansion efficiency for conventional A * algorithm based on multi-direction, directional search strategy is adopted to improve the efficiency of expansion process and guarantee the smooth of the path. Adaptive step strategy and adaptive weight strategy are used to improve the exploration speed and the accuracy of the proposed algorithm. By adopting bi-directional search strategy, the exploration ability of the algorithm can be improved in some complex environment. Finally, rewiring process is introduced to optimize the path length. The simulation for UAV path planning under the multi-constraints conditions is carried out and the simulation results show that Bi-directional adaptive A * algorithm has the superiority in run time and path quality. INDEX TERMS A * algorithm, adaptive strategy, unmanned aerial vehicle, path planning.

“…If nodes A, B, and C satisfy Equ. (11) and Equ. (12), the original path A − B − C will turns into A − C, and the redundant node B will be removed.…”

Section: G Rewiring Processmentioning

confidence: 98%

“…years [8], [9]. Common swarm intelligence optimization algorithms include Ant Colony Optimization (ACO) [10], Particle Swarm Optimization (PSO) [11], and Genetic algorithm(GA) [12]. Swarm intelligence optimization algorithms can efficiently solve the path planning problem with good robustness.…”

Section: Introductionmentioning

confidence: 99%

Bi-Directional Adaptive A* Algorithm Toward Optimal Path Planning for Large-Scale UAV Under Multi-Constraints

Zhen

et al. 2020

“…Supposing that the start state S and the target state T are 0 ph and 1 n ph  , the specific calculation formula of path length Length( ) PH can be expressed as follows: ph and all obstacles. In order to evaluate the degree of collision of the path segment, scholars often calculate the distance between the path segment and the center of the obstacle [24]. The degree of collision is related to the cost of path avoiding collision.…”

Section: B Fitness Function and Evaluation Of Degree Of Collisionmentioning

confidence: 99%

A Firefly Algorithm With Self-Adaptive Population Size for Global Path Planning of Mobile Robot

Fan

Hou

2020

In the simulation experiment of path planning of mobile robot based on firefly algorithm, it is found that the matching relationship between the number of fireflies and obstacles in the iterative process has significant conflict impacts on exploration ability and computational complexity of the algorithm. In order to solve the above problem, an optimal method of path planning based on firefly algorithm with selfadaptive population size is proposed. Firstly, the evaluation of degree of collision is established at the cost of avoiding collision. Based on the degree of collision of the population, two nonlinear functions are proposed to determine the population size. Then, individuals are added or deleted for the firefly population. Individuals are added randomly. The feasible solution and the infeasible solution are distinguished in firefly population, and delete the fireflies in the infeasible solution first when performing the eliminating operation. Finally, on the basis of the existing methods for dealing with infeasible paths, a coefficient that is adaptively adjusted according to the population size is introduced to control the degree to which the infeasible path approaches the feasible area. Compared with fixed population size firefly algorithm, the proposed algorithm has better performance in terms of solution stability, convergence speed and running time. INDEX TERMS path planning, improved firefly algorithm, self-adaptive population size.

“…Liu at al. propose a four-dimensional coordinated path planning algorithm for multiple UAVs in [14]. They focus on the plan of a collision free path by taking time variable into consideration.…”

Section: A Uav Path Planningmentioning

confidence: 99%

UAV Path Planning With QoS Constraint in Device-to-Device 5G Networks Using Particle Swarm Optimization

Shi

2020

Unmanned Ariel Vehicles (UAVs) are tasked to collect sensory data which are typically retrieved after the flight. The emergence of 5G and Device-to-Device (D2D) networks enables high speed network communication for UAVs to transfer data during a flight mission instead of post flight. UAVs are now subject to constraints of area coverage, battery capacity and network quality of service, making their path planning more challenging. In this paper, we formulate the issue as a combinatorial optimization problem which minimizes the flight cost of multiple UAVs covering the entire area. We show this problem is NP-hard, therefore we propose a Particle Swarm Optimization heuristic along with path encoding and local search techniques to solve the problem. Our numerical simulations demonstrate the effectiveness of the approach and how the size of the area and D2D link affect the number of UAVs needed and their flight time.