Navigation of Autonomous Swarm of Drones Using Translational Coordinates

Mohamed, Sherif Abdelmonem Sayed; Haghbayan, Mohammad-Hashem; Heikkonen, Jukka; Tenhunen, Hannu; Plosila, Juha

doi:10.1007/978-3-030-49778-1_28

Cited by 17 publications

(10 citation statements)

References 28 publications

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“…Furthermore, further research and development can be directed on the extension and validation of the developed algorithms in 3-dimensional environments with dynamic constraints bringing the simulations closer to real world environments and moving towards the real-time testing. For instance, the 3-D collision avoidance algorithm designed in [95], collision avoidance and navigation using translational coordinates in [116], formation control and collision avoidance in [36], efficiency of the designed controller for countering the environmental disturbances in [133], can be further extended and tested under various realistic scenarios. Since 2018 he is Post-Doc and lecturer in University of Turku, Finland.…”

Section: Discussionmentioning

confidence: 99%

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Unmanned Aerial Vehicles (UAVs): Collision Avoidance Systems and Approaches

et al. 2020

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Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art collision avoidance systems (CAS). A lot of work is being done to make the CAS as safe and reliable as possible, necessitating a comparative study of the recent work in this important area. The paper provides a comprehensive review of collision avoidance strategies used for unmanned vehicles, with the main emphasis on unmanned aerial vehicles (UAV). It is an in-depth survey of different collision avoidance techniques that are categorically explained along with a comparative analysis of the considered approaches w.r.t. different scenarios and technical aspects. This also includes a discussion on the use of different types of sensors for collision avoidance in the context of UAVs. INDEX TERMS autonomous aerial vehicles, autonomous vehicles, collision avoidance, active and passive sensors, optimisation-based, force-field based, sense and avoid, geometry based

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Section: Discussionmentioning

confidence: 99%

“…In this approach, an agent/robot is equipped with different types of sensors such as LiDAR, sonar, and radar. For instance, radar reacts quickly to any object that comes within the detection range of the sensor, even though it cannot see the details of the object [36], [116], [117].…”

Section: Sense and Avoid Methodsmentioning

confidence: 99%

Unmanned Aerial Vehicles (UAVs): Collision Avoidance Systems and Approaches

et al. 2020

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“…Our previous works have focused on: (1) energy-efficient formation morphing by systematic integration of formation control and collision avoidance for formation-collision co-awareness and the use of non-rigid mapping by utilizing a thin-plate splines (TPS) based algorithm to minimize deformation in the swarm [10] ; (2) reducing the energy consumption owing to sensor(s) usage in the swarm by introducing the concepts of translational coordinates based navigation and adaptive consciousness in the agents [23] , [36] ; and (3) dynamic formation reshaping for collision avoidance while passing through the available gaps between the obstacles without slowing down [37] .…”

Section: Problem Formulationmentioning

confidence: 99%

“…Reducing energy consumption to increase mission life is another important research area in swarm robotics, focusing on a diverse set of topics, such as efficient decision making [20] , minimization of traveling distance [21] , energy efficient communication for swarm robot coordination [22] , decreasing the usage of ranging sensors [23] , and autonomous recharging [24] . In this paper, we present a novel approach to avoid congestion that may occur due to the overpopulation in either of the available gaps between the obstacles, resulting in delays and consequently higher energy consumption of the agents as well as the swarm as a whole.…”

Section: Introductionmentioning

confidence: 99%

Swarm formation morphing for congestion-aware collision avoidance

Haghbayan

Yasin

Plosila

2021

Heliyon

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The focus of this work is to present a novel methodology for optimal distribution of a swarm formation on either side of an obstacle, when evading the obstacle, to avoid overpopulation on the sides to reduce the agents' waiting delays, resulting in a reduced overall mission time and lower energy consumption. To handle this, the problem is divided into two main parts: 1) the disturbance phase: how to morph the formation optimally to avoid the obstacle in the least possible time in the situation at hand, and 2) the convergence phase: how to optimally resume the intended formation shape once the threat of potential collision has been eliminated. For the first problem, we develop a methodology which tests different formation morphing combinations and finds the optimal one, by utilizing trajectory, velocity, and coordinate information, to bypass the obstacle. For the second problem, we utilize thin-plate splines (TPS) inspired temperature function minimization method to bring the agents back from the distorted formation into the desired formation in an optimal manner, after collision avoidance has been successfully performed. Experimental results show that, in the considered test scenario, the proposed approach results in substantial energy savings as compared with the traditional methods.

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“…During the flight, they can encounter both stationary and moving obstacles and objects that need to be safely and reliably evaded using the collision avoidance system [23], [24]. Typically, algorithms for collision avoidance can be divided into three generic classes [25], [26]: 1) force-field methods that work on the principle of applying attractive/repulsive electric forces existing amongst charged objects; each drone in a swarm is considered a charged particle, and attractive or repulsive forces between drones and the obstacles are used to generate and choose the routes to be taken [27], [28]; 2) sense-andavoid based methods, where the process of collision avoidance is simplified into individual detection and avoidance of the objects and obstacles, resulting in short response times and reducing the computational power needed [29], [30]; and 3) optimization based methods which focus on providing the optimal or near-optimal solutions for path planning and motion characteristics of each drone with respect to the other drones and obstacles. In order to calculate efficient routes within a finite time horizon, these methods rely on static objects with known locations and dimensions [31], [32].…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Formation Morphing for Collision Avoidance in a Swarm of Drones

et al. 2020

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Two important aspects in dealing with autonomous navigation of a swarm of drones are collision avoidance mechanism and formation control strategy; a possible competition between these two modes of operation may have negative implications for success and efficiency of the mission. This issue is exacerbated in the case of distributed formation control in leader-follower based swarms of drones since nodes concurrently decide and act through individual observation of neighbouring nodes' states and actions. To dynamically handle this duality of control, a plan of action for multi-priority control is required. In this paper, we propose a method for formation-collision co-awareness by adapting the thin-plate splines algorithm to minimize deformation of the swarm's formation while avoiding obstacles. Furthermore, we use a non-rigid mapping function to reduce the lag caused by such maneuvers. Simulation results show that the proposed methodology maintains the desired formation very closely in the presence of obstacles, while the response time and overall energy efficiency of the swarm is significantly improved in comparison with the existing methods where collision avoidance and formation control are only loosely coupled. Another important result of using non-rigid mapping is that the slowing down effect of obstacles on the overall speed of the swarm is significantly reduced, making our approach especially suitable for time critical missions.

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Navigation of Autonomous Swarm of Drones Using Translational Coordinates

Cited by 17 publications

References 28 publications

Unmanned Aerial Vehicles (UAVs): Collision Avoidance Systems and Approaches

Unmanned Aerial Vehicles (UAVs): Collision Avoidance Systems and Approaches

Swarm formation morphing for congestion-aware collision avoidance

Energy-Efficient Formation Morphing for Collision Avoidance in a Swarm of Drones

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