Drones

Research

27 pages, 7044 KiB

Open AccessArticle

Modeling and Simulation of an Octorotor UAV with Manipulator Arm

by Edmundo Javier Ollervides-Vazquez, Pablo A. Tellez-Belkotosky, Victor Santibañez, Erik G. Rojo-Rodriguez, Luis A. Reyes-Osorio and Octavio Garcia-Salazar

Drones 2023, 7(3), 168; https://doi.org/10.3390/drones7030168 - 28 Feb 2023

Cited by 3 | Viewed by 2166

Abstract

In this paper, the conceptual design, modeling, and simulation are proposed for an octorotor UAV with a manipulator arm. The conceptual design of the octorotor UAV with a manipulator arm is developed, and for the study and analysis, the design is implemented and [...] Read more.

In this paper, the conceptual design, modeling, and simulation are proposed for an octorotor UAV with a manipulator arm. The conceptual design of the octorotor UAV with a manipulator arm is developed, and for the study and analysis, the design is implemented and validated in Matlab (Simulink-SimMechanics) software. The kinematics and dynamics models of the octorotor UAV with a manipulator arm are obtained using the classical Denavit–Hartenberg convention and the recursive Newton–Euler method, respectively. In this sense, a cascade PID controller for the attitude and navigation of the UAV and a simple PID controller for the manipulator arm are proposed and simulated in a closed-loop system in order to highlight the performance of the proposed design. Finally, simulations show the feasibility and behavior of the mathematical model and the flight controller in a closed-loop system. Full article

(This article belongs to the Special Issue Multi-UAVs Control)

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29 pages, 8735 KiB

Open AccessArticle

Trajectory Planning for Multiple UAVs and Hierarchical Collision Avoidance Based on Nonlinear Kalman Filters

by Warunyu Hematulin, Patcharin Kamsing, Peerapong Torteeka, Thanaporn Somjit, Thaweerath Phisannupawong and Tanatthep Jarawan

Drones 2023, 7(2), 142; https://doi.org/10.3390/drones7020142 - 18 Feb 2023

Cited by 1 | Viewed by 2124

Abstract

Fully autonomous trajectory planning for multiple unmanned aerial vehicles (UAVs) is significant for building the next generation of the logistics industry without human control. This paper presents a method to enable multiple UAVs to fly in the same trajectory without collision. It benefits [...] Read more.

Fully autonomous trajectory planning for multiple unmanned aerial vehicles (UAVs) is significant for building the next generation of the logistics industry without human control. This paper presents a method to enable multiple UAVs to fly in the same trajectory without collision. It benefits several applications, such as smart cities and transfer goods, during the COVID-19 pandemic. Different types of nonlinear state estimation are deployed to test the position estimation of drones by treating the information from AirSim as offline dynamic data. The obtained global positioning system sensor data and magnetometer sensor data are determined as the measurement model. The experiment in the simulation is separated into (1) the localization state, (2) the rendezvous state, in which the proposed rendezvous strategy is presented by using the relation between velocity and displacement through the setting area, and (3) the full mission state, which combines both the localization and rendezvous states. The localization state results show the best RMSE in the case of full GPS available at 0.21477 m and 0.25842 m in the case of a GPS outage during a period of time by implementing the ensemble Kalman filter. Similarly, the ensemble Kalman filter performs well with an RMSE of 0.5112414 m in the rendezvous state and demonstrates exceptional performance in the full mission state. Moreover, the experiment is implemented in a real-world situation with some basic drone kits as proof that the proposed rendezvous strategy can truly operate. Full article

(This article belongs to the Special Issue Multi-UAVs Control)

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15 pages, 790 KiB

Open AccessArticle

Genetic Fuzzy Methodology for Decentralized Cooperative UAVs to Transport a Shared Payload

by Anoop Sathyan, Ou Ma and Kelly Cohen

Drones 2023, 7(2), 103; https://doi.org/10.3390/drones7020103 - 3 Feb 2023

Cited by 3 | Viewed by 1503

Abstract

In this work, we train controllers (models) using Genetic Fuzzy Methodology (GFM) for learning cooperative behavior in a team of decentralized UAVs to transport a shared slung payload. The training is done in a reinforcement learning fashion where the models learn strategies based [...] Read more.

In this work, we train controllers (models) using Genetic Fuzzy Methodology (GFM) for learning cooperative behavior in a team of decentralized UAVs to transport a shared slung payload. The training is done in a reinforcement learning fashion where the models learn strategies based on feedback received from the environment. The controllers in the UAVs are modeled as fuzzy systems. Genetic Algorithm is used to evolve the models to achieve the overall goal of bringing the payload to the desired locations while satisfying the physical and operational constraints. The UAVs do not explicitly communicate with one another, and each UAV makes its own decisions, thus making it a decentralized system. However, during the training, the cost function is defined such that it is a representation of the team’s effectiveness in achieving the overall goal of bringing the shared payload to the target. By including a penalization term for any constraint violation during the training, the UAVs learn strategies that do not require explicit communication to achieve efficient transportation of payload while satisfying all constraints. We also present the performance metrics by testing the trained UAVs on new scenarios with different target locations and with different number of UAVs in the team. Full article

(This article belongs to the Special Issue Multi-UAVs Control)

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16 pages, 2699 KiB

Open AccessArticle

Distributed Motion Planning for Multiple Quadrotors in Presence of Wind Gusts

by Pramod Abichandani, Deepan Lobo, Meghna Muralidharan, Nathan Runk, William McIntyre, Donald Bucci and Hande Benson

Drones 2023, 7(1), 58; https://doi.org/10.3390/drones7010058 - 13 Jan 2023

Cited by 3 | Viewed by 2046

Abstract

This work demonstrates distributed motion planning for multi-rotor unmanned aerial vehicle in a windy outdoor environment. The motion planning is modeled as a receding horizon mixed integer nonlinear programming (RH-MINLP) problem. Each quadrotor solves an RH-MINLP to generate its time-optimal speed profile along [...] Read more.

This work demonstrates distributed motion planning for multi-rotor unmanned aerial vehicle in a windy outdoor environment. The motion planning is modeled as a receding horizon mixed integer nonlinear programming (RH-MINLP) problem. Each quadrotor solves an RH-MINLP to generate its time-optimal speed profile along a minimum snap spline path while satisfying constraints on kinematics, dynamics, communication connectivity, and collision avoidance. The presence of wind disturbances causes the motion planner to continuously regenerate new motion plans, thereby significantly increasing the computational time and possibly leading to safety violations. Control Barrier Functions (CBFs) are used for assist in collision avoidance in the face of wind disturbances while alleviating the need to recalculate the motion plans continually. The RH-MINLPs are solved using a novel combination of heuristic and optimal methods, namely Simulated Annealing and interior-point methods, respectively, to handle discrete variables and nonlinearities in real-time feasibly. The framework is validated in simulations featuring up to 50 quadrotors and Hardware-in-the-loop (HWIL) experiments, followed by outdoor field tests featuring up to 6 DJI M100 quadrotors. Results demonstrate (1) fast online motion planning for outdoor communication-centric multi-quadrotor operations and (2) the utility of CBFs in providing effective motion plans. Full article

(This article belongs to the Special Issue Multi-UAVs Control)

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18 pages, 1476 KiB

Open AccessArticle

Reference Generator for a System of Multiple Tethered Unmanned Aerial Vehicles

by Carlos G. Valerio, Néstor Aguillón, Eduardo S. Espinoza and Rogelio Lozano

Drones 2022, 6(12), 390; https://doi.org/10.3390/drones6120390 - 1 Dec 2022

Cited by 3 | Viewed by 1806

Abstract

This paper deals with the references generation for a team of unmanned aerial vehicles tethered to a ground station for inspection applications. In order to deploy the team of vehicles in a suitable location to cover the largest area, each vehicle is commanded [...] Read more.

This paper deals with the references generation for a team of unmanned aerial vehicles tethered to a ground station for inspection applications. In order to deploy the team of vehicles in a suitable location to cover the largest area, each vehicle is commanded to securely navigate in an area of interest while it is tethered to another vehicle or to a ground station. To generate the corresponding reference for each vehicle, we used a model predictive controller, which optimizes the desired trajectory based on the mission-defined constraints. To validate the effectiveness of the proposed strategy, we conducted a simulation and experimental tests with a team of consumer unmanned aerial vehicles tethered to a ground station. Full article

(This article belongs to the Special Issue Multi-UAVs Control)

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18 pages, 1653 KiB

Open AccessArticle

Prescribed Performance Rotating Formation Control of Multi-Spacecraft Systems with Uncertainties

by Yan Liu, Kaiyu Qin, Weihao Li, Mengji Shi, Boxian Lin and Lu Cao

Drones 2022, 6(11), 348; https://doi.org/10.3390/drones6110348 - 9 Nov 2022

Cited by 2 | Viewed by 1494

Abstract

This paper investigates the problem of rotating formation control for multi-spacecraft systems with prescribed performance in the presence of model uncertainties. Firstly, The spacecraft dynamics containing unmodelled parts is described in a polar coordinate system, which is to solve the problem of the [...] Read more.

This paper investigates the problem of rotating formation control for multi-spacecraft systems with prescribed performance in the presence of model uncertainties. Firstly, The spacecraft dynamics containing unmodelled parts is described in a polar coordinate system, which is to solve the problem of the controllable angular velocity of rotating formation. Then, the prescribed performance control method is improved by developing new prescribed performance functions. Based on the improved prescribed performance control method, the distributed controller is designed for multi-spacecraft systems to achieve rotating formations with prescribed performance, i.e., the formations error converges to a predefined arbitrarily small residual set, with convergence time no less than a prespecified value. And an RBF neural network is used to fit the unmodelled components of the spacecraft dynamics. Compared with the existing works of literature, this paper not only solves the robust prescribed performance rotating formation control of multi-spacecraft system, but also acheives rotating formation with adjustable angular velocity. Finally, the Lyapunov approach is employed for convergence analysis, and simulation results are provided to illustrate the effectiveness of the theoretical results. Full article

(This article belongs to the Special Issue Multi-UAVs Control)

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22 pages, 1028 KiB

Open AccessArticle

Robust Neural Network Consensus for Multiagent UASs Based on Weights’ Estimation Error

by Alejandro Morfin-Santana, Filiberto Muñoz, Sergio Salazar and José Manuel Valdovinos

Drones 2022, 6(10), 300; https://doi.org/10.3390/drones6100300 - 13 Oct 2022

Cited by 1 | Viewed by 1537

Abstract

We propose a neural network consensus strategy to solve the leader–follower problem for multiple-rotorcraft unmanned aircraft systems (UASs), where the goal of this work was to improve the learning based on a set of auxiliary variables and first-order filters to obtain the estimation [...] Read more.

We propose a neural network consensus strategy to solve the leader–follower problem for multiple-rotorcraft unmanned aircraft systems (UASs), where the goal of this work was to improve the learning based on a set of auxiliary variables and first-order filters to obtain the estimation error of the neural weights and to introduce this error information in the update laws. The stability proof was conducted based on Lyapunov’s theory, where we concluded that the formation errors and neural weights’ estimation error were uniformly ultimately bounded. A set of simulation results were conducted in the Gazebo environment to show the efficacy of the novel update laws for the altitude and translational dynamics of a group of UASs. The results showed the benefits and insights into the coordinated control for multiagent systems that considered the weights’ error information compared with the consensus strategy based on classical

σ

-modification. A comparative study with the performance index ITAE and ITSE showed that the tracking error was reduced by around 45%. Full article

(This article belongs to the Special Issue Multi-UAVs Control)

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22 pages, 2636 KiB

Open AccessArticle

A Virtual Point-Oriented Control for Distance-Based Directed Formation and Its Application to Small Fixed-Wing UAVs

by Jiarun Yan, Yangguang Yu, Yinbo Xu and Xiangke Wang

Drones 2022, 6(10), 298; https://doi.org/10.3390/drones6100298 - 12 Oct 2022

Cited by 1 | Viewed by 1352

Abstract

This paper proposes a new algorithm to solve the control problem for a special class of distance-based directed formations, namely directed-triangulated Laman graphs. The central idea of the algorithm is to construct a virtual point for the agents who have more than two [...] Read more.

This paper proposes a new algorithm to solve the control problem for a special class of distance-based directed formations, namely directed-triangulated Laman graphs. The central idea of the algorithm is to construct a virtual point for the agents who have more than two neighbors by employing the information of the desired formation. Compared with the existing methods, the proposed algorithm can make the distance error between the agents converge faster and the path consumption is less. Furthermore, the proposed algorithm is modified to be operable for the small fixed-wing UAV model with nonholonomic and input constraints. Finally, the effectiveness of the proposed method is verified by a series of simulation experiments. Full article

(This article belongs to the Special Issue Multi-UAVs Control)

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19 pages, 10153 KiB

Open AccessCommunication

Entropy-Based Distributed Behavior Modeling for Multi-Agent UAVs

by Luke Fina, Douglas Shane Smith, Jr., Jason Carnahan and Hakki Erhan Sevil

Drones 2022, 6(7), 164; https://doi.org/10.3390/drones6070164 - 29 Jun 2022

Cited by 3 | Viewed by 2166

Abstract

This study presents a novel distributed behavior model for multi-agent unmanned aerial vehicles (UAVs) based on the entropy of the system. In the developed distributed behavior model, when the entropy of the system is high, the UAVs get closer to reduce the overall [...] Read more.

This study presents a novel distributed behavior model for multi-agent unmanned aerial vehicles (UAVs) based on the entropy of the system. In the developed distributed behavior model, when the entropy of the system is high, the UAVs get closer to reduce the overall entropy; this is called the grouping phase. If the entropy is less than the predefined threshold, then the UAVs switch to the mission phase and proceed to a global goal. Computer simulations are performed in AirSim, an open-source, cross-platform simulator. Comprehensive parameter analysis is performed, and parameters with the best results are implemented in multiple-waypoint navigation experiments. The results show the feasibility of the concept and the effectiveness of the distributed behavior model for multi-agent UAVs. Full article

(This article belongs to the Special Issue Multi-UAVs Control)

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