Multi-UAV Distributed Control for Load Transportation in Precision Agriculture

Hegde, Aditya; Ghose, Debasish

doi:10.2514/6.2020-2068

Cited by 12 publications

(8 citation statements)

References 14 publications

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“…S1: Transform the position p i into the {B} frame to obtain p B i = [ xi , ȳi , zi ] T by applying (13). S2: Compute the projection of p B i onto B defined in the {B} frame by setting zi = 0 as…”

Section: Online Computation Of Centroidal Voronoi Configurationsmentioning

confidence: 99%

“…Unmanned aerial vehicles (UAVs) have become a popular choice in this area to form MWSNs especially in places inaccessible by ground vehicles. In general, multi-UAV systems have been emerging in various applications such as precision agriculture [9][10][11][12][13], aerial manipulation and transportation [14][15][16][17][18], search and rescue [14,19,20], firefighting [21], surveillance and monitoring [22,23], mapping and exploration [24][25][26], etc.…”

Section: Introductionmentioning

confidence: 99%

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Computationally-Efficient Distributed Algorithms of Navigation of Teams of Autonomous UAVs for 3D Coverage and Flocking

Elmokadem

Savkin

2021

Drones

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This paper proposes novel distributed control methods to address coverage and flocking problems in three-dimensional (3D) environments using multiple unmanned aerial vehicles (UAVs). Two classes of coverage problems are considered in this work, namely barrier and sweep problems. Additionally, the approach is also applied to general 3D flocking problems for advanced swarm behavior. The proposed control strategies adopt a region-based control approach based on Voronoi partitions to ensure collision-free self-deployment and coordinated movement of all vehicles within a 3D region. It provides robustness for the multi-vehicle system against vehicles’ failure. It is also computationally-efficient to ensure scalability, and it handles obstacle avoidance on a higher level to avoid conflicts in control with the inter-vehicle collision avoidance objective. The problem formulation is rather general considering mobile robots navigating in 3D spaces, which makes the proposed approach applicable to different UAV types and autonomous underwater vehicles (AUVs). However, implementation details have also been shown considering quadrotor-type UAVs for an example application in precision agriculture. Validation of the proposed methods have been performed using several simulations considering different simulation platforms such as MATLAB and Gazebo. Software-in-the-loop simulations were carried out to asses the real-time computational performance of the methods showing the actual implementation with quadrotors using C++ and the Robot Operating System (ROS) framework. Good results were obtained validating the performance of the suggested methods for coverage and flocking scenarios in 3D using systems with different sizes up to 100 vehicles. Some scenarios considering obstacle avoidance and robustness against vehicles’ failure were also used.

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Section: Online Computation Of Centroidal Voronoi Configurationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computationally-Efficient Distributed Algorithms of Navigation of Teams of Autonomous UAVs for 3D Coverage and Flocking

Elmokadem

Savkin

2021

Drones

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“…Liao et al developed a distributed cascade robust feedback controller for formation that takes into account the dynamic constraints of the UAV [11]. The paper by Hegde et al covered the applicability of multiagent systems to agriculture and the design of distance-based control algorithms to maintain a specific formation of UAVs [12]. Zhang et al investigated the control problem of highorder linear systems in the presence of time delays [13].…”

Section: Introductionmentioning

confidence: 99%

Design of a Multi-Constraint Formation Controller Based on Improved MPC and Consensus for Quadrotors

2022

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The formation flight of quadrotor unmanned aerial vehicles (UAVs) is a complex multi-constraint process. When designing a formation controller, the dynamic model of the UAV itself has modeling errors and uncertainties. Model predictive control (MPC) is one of the best control methods for solving the constrained problem. First, a mathematical model of the quadrotor considering disturbance and uncertainty is established using the Lagrange–Euler formulation and is divided into a rotational subsystem (RS) and a translational subsystem (TS). Here, an improved MPC (IMPC) strategy based on an error model is introduced for the control of UAVs. The tracking errors caused by synthesis disturbance can be eliminated because of the integrator embedded in the augmented model. In addition, by modifying the parameters of the cost function, not only can the degree of stability of the closed-loop subsystem be specified, but also numerical problems in the MPC calculation can be improved. The simulation results demonstrate the stability of the designed controller in formation maintenance and its robustness to external disturbances and uncertainties.

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“…Unmanned aerial vehicles (UAVs) are widely used in photogrammetry, 1 agriculture, 2,3 search and rescue operations, 4 payload transportation, [5][6][7] and target-tracking applications. Recently, UAV-based target-tracking applications have received significant attention, and relevant work has been done to track moving objects visually.…”

Section: Introductionmentioning

confidence: 99%

Bounded distance control for Multi-UAV formation safety and preservation in target-tracking applications

Hegde

Jerry

Ghose

2022

Proceedings of the Institution of Mechanical Engineers, Part G:

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The notion of safety in multi-agent systems assumes great significance in many emerging collaborative multi-robot applications. In this paper, we present a multi-UAV collaborative target-tracking application by defining bounded inter-UAV distances in the formation in order to ensure safe operation. In doing so, we address the problem of prioritizing specific objectives over others in a multi-objective control framework. We propose a barrier Lyapunov function-based distributed control law to enforce the bounds on the distances and assess its Lyapunov stability using a kinematic model. The theoretical analysis is supported by numerical results, which account for measurement noise and moving targets. Straight-line and circular motion of the target are considered, and results for quadratic Lyapunov function-based control, often used in multi-agent multi-objective problems, are also presented. A comparison of the two control approaches elucidates the advantages of our proposed safe-control in bounding the inter-agent distances in a formation. A concluding evaluation using Robot Operating System simulations illustrates the practical applicability of the proposed control to a pair of multi-rotors visually estimating and maintaining their mutual separation within specified bounds, as they track a moving target.

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Multi-UAV Distributed Control for Load Transportation in Precision Agriculture

Cited by 12 publications

References 14 publications

Computationally-Efficient Distributed Algorithms of Navigation of Teams of Autonomous UAVs for 3D Coverage and Flocking

Computationally-Efficient Distributed Algorithms of Navigation of Teams of Autonomous UAVs for 3D Coverage and Flocking

Design of a Multi-Constraint Formation Controller Based on Improved MPC and Consensus for Quadrotors

Bounded distance control for Multi-UAV formation safety and preservation in target-tracking applications

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