Unsupervised learning‐based flexible framework for surveillance planning with aerial vehicles

Faigl, Jan; Váňa, Petr; Pěnička, Robert; Saska, Martin

doi:10.1002/rob.21823

Cited by 30 publications

(11 citation statements)

References 59 publications

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“…Decoupling methods and transformation methods share the similarity that an efficient TSP solver like the LKH can be used to provide a high-quality solution [38]. In the unsupervised learning methods, the solution of the sequencing part of the problem is combined with the online sampling of the suitable positions and headings [39], [40]. The unsupervised learning method can be regarded as a kind of constructive algorithm, which can find a solution with high efficiency.…”

Section: A Related Workmentioning

confidence: 99%

Data Collection Task Planning of a Fixed-Wing Unmanned Aerial Vehicle in Forest Fire Monitoring

et al. 2021

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This article studies the data collection task planning for a fixed-wing unmanned aerial vehicle (UAV) in forest fire monitoring. Multiple wireless-based detection nodes (DNs) are distributed in highrisk areas of the forest to monitor the surrounding environment. The task of UAV is to circularly fly to them and collect the environmental data. Because of the kinematic constraints of UAV and the effective communication range between UAV and DN, this problem can be generally regarded as a Dubins traveling salesman problem with neighborhood (DTSPN). A bi-level hybridization-based metaheuristic algorithm (BLHMA) is proposed for solving this problem. At the first level, differential evolution (DE) optimizes the continuous-valued communication positions and UAV headings by the population-based search. For the asymmetric traveling salesman problem (ATSP) corresponding to the combination of the positions and headings generated by DE, a constructive heuristic based on self-organized multi-agent competition (SOMAC) is proposed to determine the discrete collection sequence. By competitive iterations in such a cooperative way in DE, a high-quality data collection tour can be generated. At the second level, a local search based on multistage approximate gradient is proposed to further refine the positions and headings, which accelerates the convergence of the BLHMA. Referring to a real-world scene of forest fire mornitoring, the simulation experiments are designed, and comparative results show that BLHMA can find significantly shorter data collection tours in most cases over three state-of-the-art algorithms. The proposed UAV data collection planning algorithm is conducive to the efficient execution of the forest fire monitoring data collection mission and the energy saving of UAV.

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Section: A Related Workmentioning

confidence: 99%

Data Collection Task Planning of a Fixed-Wing Unmanned Aerial Vehicle in Forest Fire Monitoring

et al. 2021

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“…Another reason is that the AI community addresses this problem from the Multi-Agent perspective and the robotics community approaches this problem from the Multi-Robot perspective. For example, Multi-robot (task) Assignment Problem for Grouped Tasks (MAP-GT) [54], Target Assignment and Path Finding (TAPF) [23], and Multi-Vehicle Multi-Goal Planning [55]. All these problems include assignment or allocation of tasks/goals to robots.…”

Section: Robotics/artificial Intelligencementioning

confidence: 99%

“…Similarly, the Set-cover problems are termed as coverage planning problem of drones/robots and also termed as surveillance planning problems. For example, Faigl et al [55] treated our problem as a Multi-Vehicle Dubin's Traveling Salesman Problem with Neighborhoods. The authors studied the MRR-MM problem with two additional requirements: One to ensure that the task assignment respects Dubin's curvature and one that allows the robots to visit a small neighbourhood around the tasks/goals instead of a specific task location.…”

Section: Robotics/artificial Intelligencementioning

confidence: 99%

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Multi-Robot Routing Problem with Min–Max Objective

David

Rögnvaldsson

2021

Robotics

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In this paper, we study the “Multi-Robot Routing problem” with min–max objective (MRR-MM) in detail. It involves the assignment of sequentially ordered tasks to robots such that the maximum cost of the slowest robot is minimized. The problem description, the different types of formulations, and the methods used across various research communities are discussed in this paper. We propose a new problem formulation by treating this problem as a permutation matrix. A comparative study is done between three methods: Stochastic simulated annealing, deterministic mean-field annealing, and a heuristic-based graph search method. Each method is investigated in detail with several data sets (simulation and real-world), and the results are analysed and compared with respect to scalability, computational complexity, optimality, and its application to real-world scenarios. The paper shows that the heuristic method produces results very quickly with good scalability. However, the solution quality is sub-optimal. On the other hand, when optimal or near-optimal results are required with considerable computational resources, the simulated annealing method proves to be more efficient. However, the results show that the optimal choice of algorithm depends on the dataset size and the available computational budget. The contribution of the paper is three-fold: We study the MRR-MM problem in detail across various research communities. This study also shows the lack of inter-research terminology that has led to different names for the same problem. Secondly, formulating the task allocation problem as a permutation matrix formulation has opened up new approaches to solve this problem. Thirdly, we applied our problem formulation to three different methods and conducted a detailed comparative study using real-world and simulation data.

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“…The plat- 27 UVDAR, http://github.com/ctu-mrs/uvdar form provided real-world verification and showed the feasibility of the proposed approaches. Coverage optimization for multi-UAV cooperative surveillance was tackled in [49,50]. Complex maneuvers and cooperative load-carrying by multiple UAVs were reported on in [51,52].…”

Section: Uav Motion Planningmentioning

confidence: 99%

The MRS UAV System: Pushing the Frontiers of Reproducible Research, Real-world Deployment, and Education with Autonomous Unmanned Aerial Vehicles

Baca,

Petrlik,

Vrba

et al. 2020

Preprint

Self Cite

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We present a multirotor Unmanned Aerial Vehicle (UAV) control and estimation system for supporting replicable research through realistic simulations and real-world experiments. We propose a unique multi-frame localization paradigm for estimating the states of a UAV in various frames of reference using multiple sensors simultaneously. The system enables complex missions in GNSS and GNSS-denied environments, including outdoor-indoor transitions and the execution of redundant estimators for backing up unreliable localization sources. Two feedback control designs are presented: one for precise and aggressive maneuvers, and the other for stable and smooth flight with a noisy state estimate. The proposed control and estimation pipeline are constructed without using the Euler/Tait-Bryan angle representation of orientation in 3D. Instead, we rely on rotation matrices and a novel heading-based convention to represent the one free rotational degree-of-freedom in 3D of a standard multirotor helicopter. We provide an actively maintained and well-documented open-source implementation (http://github.com/ctu-mrs/mrs_uav_system), including realistic simulation of UAVs, sensors, and localization systems. The proposed system is the product of years of applied research on multi-robot systems, aerial swarms, aerial manipulation, motion planning, and remote sensing. All our results have been supported by real-world system deployment that subsequently shaped the system into the form presented here. In addition, the system was utilized during the participation of our team from the Czech Technical University in Prague in the prestigious MBZIRC 2017 and 2020 robotics competitions, and also in the DARPA Subterranean challenge. Each time, our team was able to secure top places among the best competitors from all over the world. On each occasion, the competitions and challenges has motivated the team to improve the system and to gain a great amount of high-quality experience within tight deadlines.

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Unsupervised learning‐based flexible framework for surveillance planning with aerial vehicles

Cited by 30 publications

References 59 publications

Data Collection Task Planning of a Fixed-Wing Unmanned Aerial Vehicle in Forest Fire Monitoring

Data Collection Task Planning of a Fixed-Wing Unmanned Aerial Vehicle in Forest Fire Monitoring

Multi-Robot Routing Problem with Min–Max Objective

The MRS UAV System: Pushing the Frontiers of Reproducible Research, Real-world Deployment, and Education with Autonomous Unmanned Aerial Vehicles

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