Asymmetrical Artificial Potential Field as Framework of Nonlinear PID Loop to Control Position Tracking by Nonholonomic UAVs

Kownacki, Cezary; Ambroziak, Leszek

doi:10.3390/s22155474

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Violent heading changes near the tracked position do not cause a rapid turn with a minimal radius. The local and asymmetrical potential function is also a framework for future research on nonlinear PID control loops [ 8 ], where the definition of LA-PF is extended with the integral and derivative terms of PID control. Therefore, position-tracking control becomes more resistant to disturbances like wind gusts, and steady-state tracking errors can be minimized.…”

Section: Introductionmentioning

confidence: 99%

Artificial Potential Field Based Trajectory Tracking for Quadcopter UAV Moving Targets

Kownacki

2024

Sensors

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The trajectory or moving-target tracking feature is desirable, because it can be used in various applications where the usefulness of UAVs is already proven. Tracking moving targets can also be applied in scenarios of cooperation between mobile ground-based and flying robots, where mobile ground-based robots could play the role of mobile landing pads. This article presents a novel proposition of an approach to position-tracking problems utilizing artificial potential fields (APF) for quadcopter UAVs, which, in contrast to well-known APF-based path planning methods, is a dynamic problem and must be carried out online while keeping the tracking error as low as possible. Also, a new flight control is proposed, which uses roll, pitch, and yaw angle control based on the velocity vector. This method not only allows the UAV to track a point where the potential function reaches its minimum but also enables the alignment of the course and velocity to the direction and speed given by the velocity vector from the APF. Simulation results present the possibilities of applying the APF method to holonomic UAVs such as quadcopters and show that such UAVs controlled on the basis of an APF behave as non-holonomic UAVs during 90° turns. This allows them and the onboard camera to be oriented toward the tracked target. In simulations, the AR Drone 2.0 model of the Parrot quadcopter is used, which will make it possible to easily verify the method in real flights in future research.

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Section: Introductionmentioning

confidence: 99%

Artificial Potential Field Based Trajectory Tracking for Quadcopter UAV Moving Targets

Kownacki

2024

Sensors

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“…In the paper [11], the authors used GWO and golden eagle optimizer (GEO) and their modifications to plan the unmanned aerial vehicle (UAV) path carrying out inspection works. When cooperation of several robots is required to process or manufacture a given element, artificial potential fields may be used to maintain proper structure and distances between the platforms [12], [13], [14].…”

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confidence: 99%

Trajectory optimization using learning from demonstration with meta-heuristic grey wolf algorithm

Pawlowski

Romaniuk

Kulesza

et al. 2022

IJRA

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<span>Nowadays, most robotic systems perform their tasks in an environment that is generally known. Thus, robot's trajectory can be planned in advance depending on a given task. However, as a part of modern manufacturing systems which are faced with the requirements to produce high product variety, mobile robots should be flexible to adapt to changing and diverse environments and needs. In such scenarios, a modification of the task or a change in the environment, forces the operator to modify robot's trajectory. Such modification is usually expensive and time-consuming, as experienced engineers must be involved to program robot's movements. The current paper presents a solution to this problem by simplifying the process of teaching the robot a new trajectory. The proposed method generates a trajectory based on an initial raw demonstration of its shape. The new trajectory is generated in such a way that the errors between the actual and target end positions and orientations of the robot are minimized. To minimize those errors, the grey wolf optimization (GWO) algorithm is applied. The proposed approach is demonstrated for a two-wheeled mobile robot. Simulation and experimental results confirm high accuracy of generated trajectories.</span>

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Planar curved path following controller for a small fixed-wing unmanned aerial vehicle with constrained parameters optimized by nonlinear model predictive control

Chen,

Zeng,

Wang

et al. 2024

International Journal of Advanced Robotic Systems

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Path following presents a pivotal challenge within the realm of small fixed-wing unmanned aerial vehicles. Firstly, a Lyapunov-stable path guidance law was formulated to follow specific planar curved paths. To ensure differentiability of the guidance law, a modified, smooth saturation function was derived. Secondly, an analysis was conducted to ascertain the interrelationship between control parameters and input constraints, thereby identifying the relevant parameter domains. Thirdly, the nonlinear model predictive control technique was harnessed to optimize both guidance law parameters, enhancing the unmanned aerial vehicle’s capacity to achieve optimal performance in both straight-line and circular path following, hereafter referred to as PFC_NMPC. By leveraging Lyapunov stability arguments for switched systems, the stability of the corresponding nonlinear switched system was guaranteed. In this study, square and circular paths were generated to assess the path-following control of a simulated fixed-wing unmanned aerial vehicle. The performance of various guidance laws, including those with fixed parameters (PFC), those with parameters tuned using fuzzy logic (PFC_FL), PFC_NMPC, vector field, and pure pursuit with line-of-sight, was compared. Notably, the proposed PFC_NMPC method exhibited the ability to expedite the unmanned aerial vehicle’s convergence to the desired path while maximizing the effective flight path length.

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Asymmetrical Artificial Potential Field as Framework of Nonlinear PID Loop to Control Position Tracking by Nonholonomic UAVs

Cited by 4 publications

References 26 publications

Artificial Potential Field Based Trajectory Tracking for Quadcopter UAV Moving Targets

Artificial Potential Field Based Trajectory Tracking for Quadcopter UAV Moving Targets

Trajectory optimization using learning from demonstration with meta-heuristic grey wolf algorithm

Planar curved path following controller for a small fixed-wing unmanned aerial vehicle with constrained parameters optimized by nonlinear model predictive control

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