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.
Highlights
Path planning consists of defining a sequence of movements from a starting point aiming to reach a desired destination while avoiding collisions with objects in the workspace [1].Considering the significant advances of robotics and control technologies, this research topic has been extensively studied in the literature in the context of several practical applications
Implementation of a modified artificial potential fields (APFs) algorithm with a vortex field that spins over three directions aiming to avoid local minima, collisions, as well as oscillations in narrow passages and in the influence threshold associated with the obstacles; development of a technique in which each aircraft analyzes its position in relation to the obstacles and the target, individually determining the best direction of rotation for the vortex field generated by each obstacle aiming at safe motion in the workspace
In order to evaluate the performance of the proposed algorithm, the obtained path is compared with those generated by the conventional APF algorithm and left turning APF algorithm (LTAPF)
Summary
Path planning consists of defining a sequence of movements from a starting point aiming to reach a desired destination while avoiding collisions with objects in the workspace [1].Considering the significant advances of robotics and control technologies, this research topic has been extensively studied in the literature in the context of several practical applications. It has become of particular interest to unmanned aerial vehicles (UAVs), which must rely on the accurate and safe movement for accomplishing distinct tasks [2]. Path planning often involves significant complexity owing to constraints associated with the differential speed and acceleration [3], the atmospheric turbulence that makes it difficult to follow a given route accurately [4], the three-dimensional (3D) workspace, and little information about the environment considering limitations of the sensing system [5]. One of the main aspects associated with path planning is the uncertainty with respect to the presence of unknown or unexpected obstacles in the workspace, which demands the continuous monitoring during operation. The adaptation and/or recalculation of a given path is essential to ensure the safe operation of vehicles. In the context of global path planning, it requires the previous knowledge of the whole workspace, allowing the path to be planned offline, but without the possibility to perform changes in the scenario
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