Path following and obstacle avoidance for an autonomous UAV using a depth camera

Iacono, Massimiliano; Sgorbissa, Antonio

doi:10.1016/j.robot.2018.04.005

Cited by 67 publications

(28 citation statements)

References 26 publications

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“…Authors in [25] developed an algorithm to build a map of the environment and to generate a collision free path for autonomous UAVs. They used an RGB-D camera with sensors and tested their work in an indoor environment in the presence of obstacles.…”

Section: Related Workmentioning

confidence: 99%

“…They propose an algorithm that plans a coverage path for UAVs over irregular shaped areas using a grid-based method with minimum energy consumption. Authors enhanced the algorithm proposed by Valente et al [25], by substituting the Cost Function with a novel Energy-Cost Function. The novel algorithm comes up with 17% in energy gain compared to the algorithm provided in [25].…”

Section: Related Workmentioning

confidence: 99%

“…Authors enhanced the algorithm proposed by Valente et al [25], by substituting the Cost Function with a novel Energy-Cost Function. The novel algorithm comes up with 17% in energy gain compared to the algorithm provided in [25]. They also included two techniques that significantly decrease the computation Time.…”

Section: Related Workmentioning

confidence: 99%

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EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs

Ghaddar

Merei

2020

Future Internet

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The presence of obstacles like a tree, buildings, or birds along the path of a drone has the ability to endanger and harm the UAV’s flight mission. Avoiding obstacles is one of the critical challenging keys to successfully achieve a UAV’s mission. The path planning needs to be adapted to make intelligent and accurate avoidance online and in time. In this paper, we propose an energy-aware grid based solution for obstacle avoidance (EAOA). Our work is based on two phases: in the first one, a trajectory path is generated offline using the area top-view. The second phase depends on the path obtained in the first phase. A camera captures a frontal view of the scene that contains the obstacle, then the algorithm determines the new position where the drone has to move to, in order to bypass the obstacle. In this paper, the obstacles are static. The results show a gain in energy and completion time using 3D scene information compared to 2D scene information.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs

Ghaddar

Merei

2020

Future Internet

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“…The location is updated using measurements of MIMU while the monocular detection algorithm gives the detection outcome of the element of reference lines. [76] demonstrates the use of AscTec Firefly hardware for indoor navigation, the Firefly hardware is equipped with an RGB-D camera. Collision free paths are generated using sensor output to construct a map of the environment.…”

Section: Path Planningmentioning

confidence: 99%

Aerial and Under-water Dronal Communication: Potentials, Issues and Vulnerabilities

Srinivasan*¹,

Ramaneswaran²,

Subramanian³

et al. 2019

IJITEE

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The use of UAVs has been on the rise since the past decade. These UAVs are very versatile and can be applied to several fields ranging from agriculture to military reconnaissance. Due to the immense functionality of the UAVs, it has now become a very popular topic in Research. With the advent of Deep Learning and Machine Learning the autonomous navigation functionalities have been improved but it still poses a challenge as navigating a drone involves the integration of its software and hardware functionalities. This paper reviews both indoor and outdoor navigation. Topics in navigation such as Control, Hardware, Algorithms, Path-Planning, and communication are touched upon in this paper. The paper also presents a discussion on the Navigation of UAVs using visionbased technologies, this technology encompasses the use of various sensors and cameras and integrate it with the navigation system of the UAV to ensure the success of UAV’s operation. UAVs play a pivotal role in networking, many applications have been developed which have UAVs as an important component in them. Their applications include Urban computing, Internet of Things, Ubiquitous Computing to name a few. Safely operating a UAV as a network node is not a simple task, these nodes are prone to various types of attacks. A new type of attack introduced in this paper is termed as “Coagulation Attack”. This attack gets its name from the coagulation property of fluids wherein fluids clot and their particles settle down. This paper explores the concept, issues, challenges and research aspects of Coagulation attack. This paper also introduces the concept of Underwater Wireless Communication. On a planet where 70% of the surface is covered in water, UWC has a lot of potentials to be exploited. Moreover UWC is critical in many key areas of Maritime research, commerce, and surveillance. UWC is used for experimental observation, data collection and analysis, underwater navigation, disaster prevention and early detection warning of a tsunami. In addition, we summarize emerging technologies in the UWC, future research directions and recommendations using fifth-generation (5G) communication techniques.

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“…Distributed or decentralized coordination control of multiagent systems has received increasing research attention in the control community; it has been widely used in many practical applications, such as unmanned surface vessels, unmanned aerial vehicles, satellite clusters, and military surveillance [1][2][3][4][5][6][7]. The multiagent systems are generally composed of a team of agents, in which all the agents are required to interact with others and then perform various kinds of tasks.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Formation Control Scheme with Obstacle Avoidance for Multiagent Systems

Yang

Fan

2019

Mathematical Problems in Engineering

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This study considers the problem of formation control for second-order multiagent systems. We propose a distributed nonlinear formation controller where the control input of each follower can be expressed as a product of a nonlinear term that relies on the distance errors under the leader–follower structure. In the leader–follower structure, a small number of agents are assumed to be the leaders, and they are responsible for steering a group of agents to the specific destination, while the rest of the agents are called followers. The stability of the proposed control laws is demonstrated by utilizing the Lyapunov function candidate. To solve the obstacle avoidance problem, the artificial potential approach is employed, and the agents can avoid each possible obstacle successfully without getting stuck in any local minimum point. The control problem of multiagent systems in the presence of unknown constant disturbances is also considered. To attenuate such disturbances, the integral term is introduced, and the static error is eliminated through the proposed PI controller, which makes the system stable; the adaptive controller is designed to reduce the effect of time-varying disturbances. Finally, numerical simulation results are presented to support the obtained theoretical results.

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Path following and obstacle avoidance for an autonomous UAV using a depth camera

Cited by 67 publications

References 26 publications

EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs

EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs

Aerial and Under-water Dronal Communication: Potentials, Issues and Vulnerabilities

A Distributed Formation Control Scheme with Obstacle Avoidance for Multiagent Systems

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