Cooperative Optimization of UAVs Formation Visual Tracking

Lissandrini, Nicola; Michieletto, Giulia; Oboe, Roberto; Galvan, Marta; Franco, Alberto; Cenedese, Angelo

doi:10.3390/robotics8030052

Cited by 14 publications

(9 citation statements)

References 45 publications

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“…The technological advancements in UAV mechanisms, wireless sensing, and AI-based computing have put forth new opportunities for the deployment of possible real-time intelligent inspection of outdoor electrical insulators. Surveying, based on UAV imaging, has been successfully applied to diverse applications, such as agriculture [13], military [14], industrial plants [15], etc. Visual monitoring of the power transmission systems can also be efficiently carried out using UAVs, owing to their high mobility, low operating cost, and ability to access obscure, intricate, and remote areas.…”

Section: Methodsmentioning

confidence: 99%

Multi-Modal Data Fusion Using Deep Neural Network for Condition Monitoring of High Voltage Insulator

et al. 2020

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A novel Fusion Convolutional Network (FCN) is proposed in this research for potential realtime monitoring of insulators using unmanned aerial vehicle (UAV) edge devices. Precise airborne imaging of outdoor objects, such as high voltage insulators, suffers from varied object resolution, cluttered backgrounds, unclear or contaminated surfaces, and illumination conditions. Accurate information about the insulator surface condition is essential and is of a high priority since insulator breakdown is a leading cause of electrical failure. A multi-modal information fusion (MMIF) system is developed during this research to analyze and classify possible contaminations present on the electrical insulators. A novel system, referred to as FCN, consists of a Convolutional Neural Network (CNN) and a binary Multilayer Neural Network (MNN) sub-classifier. While constructing the MMIF dataset for training and testing the novel FCN, the image classification output of the CNN is combined with the leakage current values (LCV) obtained as the classification output of MNN. Each sample of the MMIF dataset is, therefore, represented as a series of fusions. Later, sub-classifiers, of the FCN, are trained to identify the contamination types in the fusion series by implementing a voting system of sub-classifiers which is trained to identify a given class. As a result of the implementation of the proposed FCN, the classification accuracy increased by 8.4%, i.e., from 92% to 99.76%. To compare and benchmark the performance of proposed FCN, conventional classification algorithms are also implemented on the fusion of features that are extracted employing the wavelet transform and PCA methods. State-of-the-art CNN architectures are also discussed on account of their time consumption and memory usage. The conceptualization of a potential hardware implementation of the proposed FCN, on emerging edge devices, is also provided for completeness of the discussion. Pertinent outcomes of this research can be further extended to other potential applications of airborne imaging. INDEX TERMS Convolutional Neural Network, multi-modal information fusion, electrical insulators, unmanned aerial vehicle.

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Section: Methodsmentioning

confidence: 99%

Multi-Modal Data Fusion Using Deep Neural Network for Condition Monitoring of High Voltage Insulator

et al. 2020

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“…Given these premises, the main contribution of this paper is the design and the implementation of an MPC-based leader-follower scheme that performs local trajectory planning for each robotic agent while also performing obstacle avoidance and maintaining grasping of the transported load. In particular, the proposed method improves the standard leader-follower approach, as in [5], by accounting for the feasibility of the trajectory imposed by the leader to the follower. Specifically, the approach relies on a direct MPC formulation based on the convexification of the optimization space with linear constraints that are easily customizable depending on loads shapes.…”

Section: Contributionmentioning

confidence: 99%

“…Thanks to these features, in the last decade, the MRS architecture has emerged as an effective technology in military, civil, and industrial contexts, where it is employed in a wide range of application fields from the more traditional surveillance, monitoring, and tracking tasks with both fixed and mobile robotic agents [3][4][5], to the modern physical interaction tasks involving loads cooperative grasping, transportation, and manipulation [6,7]. Nonetheless, the MRS scenario yields also new methodological challenges that need to be addressed and solved.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control for Cooperative Transportation with Feasibility-Aware Policy

et al. 2021

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The transportation of large payloads can be made possible with Multi-Robot Systems (MRS) implementing cooperative strategies. In this work, we focus on the coordinated MRS trajectory planning task exploiting a Model Predictive Control (MPC) framework addressing both the acting robots and the transported load. In this context, the main challenge is the possible occurrence of a temporary mismatch among agents’ actions with consequent formation errors that can cause severe damage to the carried load. To mitigate this risk, the coordination scheme may leverage a leader–follower approach, in which a hierarchical strategy is in place to trade-off between the task accomplishment and the dynamics and environment constraints. Nonetheless, particularly in narrow spaces or cluttered environments, the leader’s optimal choice may lead to trajectories that are infeasible for the follower and the load. To this aim, we propose a feasibility-aware leader–follower strategy, where the leader computes a reference trajectory, and the follower accounts for its own and the load constraints; moreover, the follower is able to communicate the trajectory infeasibility to the leader, which reacts by temporarily switching to a conservative policy. The consistent MRS co-design is allowed by the MPC formulation, for both the leader and the follower: here, the prediction capability of MPC is key to guarantee a correct and efficient execution of the leader–follower coordinated action. The approach is formally stated and discussed, and a numerical campaign is conducted to validate and assess the proposed scheme, with respect to different scenarios with growing complexity.

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“…2) Task Constraints: There are no task constraints implemented currently, however we may easily envisage constraints ensuring that all observed quadrotors remain in the field of view of the observing quadrotor as in [30], which require the persistent observation of a target outside the formation [31], or ensuring collision avoidance with environmental obstacles [32].…”

Section: Constraintsmentioning

confidence: 99%

Model Predictive Control for Dynamic Quadrotor Bearing Formations

Erskine

Balderas-Hill

Fantoni

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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Formation control of multi-agent systems deals with groups of robots forming specific spatial geometries. Combined with the advancements of unmanned aerial vehicles (UAVs) in the past decade, formation control may potentially be applied to tasks such as search-and-rescue, surveillance, even collaborative manipulation. A key challenge is the decentralization of formation control, where each agent behaves independently using onboard sensors and computation, improving the scaleability and robustness of the system.This paper proposes a decentralized controller based on model predictive control (MPC), for the control of formations of quadrotor UAVs defined by inter-agent bearings. The use of MPC allows the controller to account for attitude kinematics, improving upon the results of existing bearing formation control methods based on rigidity and visual servoing approaches, which typically only consider the quadrotor as a single or double integrator. Furthermore the near-optimality of MPC permits a more optimal use of the quadrotors dynamic capabilities for faster maneuvering. Extensive simulations are performed to demonstrate the improved transient formation convergence and fast maneuvering permitted by this controller. Experiments show that it is indeed a real-time feasible solution for bearing formation control.

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Cooperative Optimization of UAVs Formation Visual Tracking

Cited by 14 publications

References 45 publications

Multi-Modal Data Fusion Using Deep Neural Network for Condition Monitoring of High Voltage Insulator

Multi-Modal Data Fusion Using Deep Neural Network for Condition Monitoring of High Voltage Insulator

Model Predictive Control for Cooperative Transportation with Feasibility-Aware Policy

Model Predictive Control for Dynamic Quadrotor Bearing Formations

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