Modified Artificial Potential Field for the Path Planning of Aircraft Swarms in Three-Dimensional Environments

Abstract

Path planning techniques are of major importance for the motion of autonomous systems. In addition, the chosen path, safety, and computational burden are essential for ensuring the successful application of such strategies in the presence of obstacles. In this context, this work introduces a modified potential field method that is capable of providing obstacle avoidance, as well as eliminating local minima problems and oscillations in the influence threshold of repulsive fields. A three-dimensional (3D) vortex field is introduced for this purpose so that each robot can choose the best direction of the vortex field rotation automatically and independently according to its position with respect to each object in the workspace. A scenario that addresses swarm flight with sequential cooperation and the pursuit of moving targets in dynamic environments is proposed. Experimental results are presented and thoroughly discussed using a Crazyflie 2.0 aircraft associated with the loco positioning system for state estimation. It is effectively demonstrated that the proposed algorithm can generate feasible paths while taking into account the aforementioned problems in real-time applications.

Distributed Robust Learning Based Formation Control of Mobile Robots Based on Bioinspired Neural Dynamics

“…There have been many studies related to this formation protocol with different approaches such as virtual structure [4], [5], behavior based [6], [7], artificial potential field [8], [9], and leader-follower based approach [10]- [12]. Among all these formation approaches, it is noticed that the leaderfollower approach has been the dominant method in realworld applications due to its relatively easy implementation, real-time performance, and good flexibility.…”

Two-level vehicle path planning model for multi-warehouse robots with conflict solution strategies and improved ACO

“…For comparison and analysis, these path planning algorithms are organized in Table 1. Classical methods include artificial potential field algorithms (Chen et al, 2022;Hao et al, 2022;Ma et al, 2022;Souza et al, 2022;Zhao et al, 2023), graph search algorithms (Jin et al, 2023;Li et al, 2022c), and sampling algorithms (Dian et al, 2022;Ding et al, 2023;Ma et al, 2023). With the development of computational techniques, metaheuristic optimization algorithms have emerged as an efficient path planning method for highdimensional complex problems based on genetic evolution mechanisms and cluster foraging migration mechanisms theories.…”

Real-Time Interval Type-2 Fuzzy Control of an Unmanned Aerial Vehicle with Flexible Cable-Connected Payload

“…For indoor experimental research, nano or mini UAVs have proven their efficiency concerning easy access, small sizes and weights [18]. As a result, researchers have used several commercial UAVs, such as Bitcraze Crazyflie 2.0 [8,[19][20][21][22], Parrot Mambo Minidrone [9,10,12] and DJI Tello [23][24][25].…”