###### Abstract  
Collision-free distributed path planning for the swarm of unmanned aerial vehicles (UAVs) in a stochastic and dynamic environment is an emerging and challenging subject for research in the field of a communication system. Monitoring the methods and approaches for multi-UAVs with full area surveillance is needed in both military and civilian applications, in order to protect human beings and infrastructure, as well as their social security. To perform the path planning for multiple unmanned aerial vehicles, we propose a trajectory planner based on Particle Swarm Optimization (PSO) algorithm to derive a distributed full coverage optimal path planning, and a trajectory planner is developed using a dynamic fitness function. In this paper, to obtain dynamic fitness, we implemented the PSO algorithm independently in each UAV, by maximizing the fitness function and minimizing the cost function. Simulation results show that the proposed distributed path planning algorithm generates feasible optimal trajectories and update maps for the swarm of UAVs to surveil the entire area of interest. 3D trajectory planning; unmanned aerial vehicle; distributed path planning; PSO; area surveillance; dynamic fitness function +
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environment. There are various path planning strategies regarding both 2-D and 3-D environments. As we are working with UAVs, which are related to the 3-D environment, we have focused mainly on path planning in the 3-D environment. There are so many path planning algorithms regarding the 3-D environment, which can be categorized into five categories [5]. These include, (i) Sampling-Based Algorithms, like, visibility graph, corridor map [6], Rapidly-exploring Random Tree (RRT) [7], 3-D Voronoi [8] (ii) Node Based Optimal Algorithms, such as, Dijkstra's Algorithm [9], A* algorithm [10], D* algorithm [11], (iii) Mathematics Model-Based Algorithms, like, Linear Programming and Optimal Control [12], Binary Linear Programming [13], Nonlinear Programming [14; 15], (iv) Bio-inspired Algorithms, which can be divided into two types, Evolutionary Algorithm [16], and Neural Network [17], and (v) Multi-fusion Based Algorithms [18]. There are various types of Evolutionary Algorithm, such as, Genetic Algorithm [19], Memetic Algorithm [20], Particle Swarm Optimization (PSO) [21], and Ant Colony Optimization (ACO) [22], which are notable for the path planning of UAVs in the 3-D environment. Evolutionary Algorithms are the algorithms that update the results in iteration-by-iterations. In our proposed 3-D path planning methodology, we used Particle Swarm Optimization (PSO), due to its benefits like the advantages of easy implementation, simple parameter settings, fast convergence speed, and for which the PSO algorithm has been widely used in various fields, such as, functions optimization, neural networks training, and fuzzy logic system control are notable. However, it has some limitations too, like premature convergence, route self-crossing. The authors in [23] suggest that this problem can be solved by using the following techniques, (1) adjust important parameters iteratively; (2) random grouping inversion strategy for avoiding premature convergence. In terms of area coverage, various techniques have been suggested by various authors, such as the authors in [24] decomposed the concave region into a convex sub-region, then the flight direction was determined based on the width of the convex polygon. Some adjacent sub-areas were merged to avoid unnecessary repetitive movement. In [25], the authors proposed a new approach relative to UAVs' capabilities assessment by using hierarchical fuzzy inference and established a cost model for UAVs' mission execution. The authors in [26], believed that a rectangle can be circumscribed by any polygon. The idea of polygon region segmentation was adopted by the authors in [27]. The sweeping technique was used for area decomposition by the authors in [28], to minimize the number of UAV, turns inside the subareas by generating the optimal number of lanes.