Path planning and formation control via potential function for UAV Quadrotor

Rizqi, Ahmad Ataka Awwalur; Cahyadi, Adha Imam; Adji, Teguh Bharata

doi:10.1109/aris.2014.6871517

Cited by 26 publications

(20 citation statements)

References 13 publications

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“…Path planning may be considered together with team work and formation control [7]. Some approaches have been adapted to quadcopters [8], as well. Similarly to other path planning algorithms, the drones have a common goal, i.e., a location or coverage of an area.…”

Section: Related Workmentioning

confidence: 99%

Geocaching-inspired Resilient Path Planning for Drone Swarms

Barbeau

García-Alfaro

Kranakis

2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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A path planning algorithm for drone swarms is presented. From the outset, none of the drones knows the path and final destination. Together, they collectively determine and unravel step-by-step the waypoints and final destination, resolving a localization problem at each step. It is a sharedinformation path planning algorithm. The algorithm is faulttolerant and resilient to drones falling victim of attacks to their positioning system. It is shown that correctly functioning drones navigate the path provided that the number of faulty drones is less than n−d 2 , where n is the total number of drones and d, equal to two or three, is the dimension of the space navigated by the drones. We validate the algorithm with appropriate simulations, implemented over OMNeT++ and GNSSim, which allow building network simulations including GPS attacks (e.g., jamming and spoofing attacks). The OMNeT++ models and GNSSim functions are linked together.

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Section: Related Workmentioning

confidence: 99%

Geocaching-inspired Resilient Path Planning for Drone Swarms

Barbeau

García-Alfaro

Kranakis

2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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“…This topic has been studied in [18], [19] and [20]. The reactive behavior is designed to find a safe way, even if new obstacles have appeared in the environment as the result of an updated Octomap.…”

Section: B Navigation Systemmentioning

confidence: 99%

A flexible hardware-in-the-loop architecture for UAVs

Lepej

Santamaria-Navarro²,

Solà³

2017

2017 International Conference on Unmanned Aircraft Systems (ICUAS)

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Abstract-As robotic technology matures, fully autonomous robots become a realistic possibility, but demand very complex solutions to be rapidly engineered. In order to be able to quickly set up a working autonomous system, and to reduce the gap between simulated and real experiments, we propose a modular, upgradeable and flexible hardware-in-theloop (HIL) architecture, which hybridizes the simulated and real settings. We take as use case the autonomous exploration of dense forests with UAVs, with the aim of creating useful maps for forest inspection, cataloging, or to compute other metrics such as total wood volume. As the first step in the development of the full system, in this paper we implement a fraction of this architecture, comprising assisted localization, and automatic methods for mapping, planning and motion execution. Specifically we are able to simulate the use of a 3D LIDAR endowed below an actual UAV autonomously navigating among simulated obstacles, thus the platform safety is not compromised. The full system is modular and takes profit of pieces either publicly available or easily programmed. We highlight the flexibility of the proposed HIL architecture to rapidly configure different experimental setups with a UAV in challenging terrain. Moreover, it can be extended to other robotic fields without further design. The HIL system uses the multi-platform ROS capabilities and only needs a motion capture system as external extra hardware, which is becoming standard equipment in all research labs dealing with mobile robots.

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“…The value of is 0 1. shows the coordinates of the agentand shows the point of the obstacle. Many researchers have modified the potential field algorithm such as Rizqi et al [23] whohas modified repulsive potential field. Ren et al [24] has modified potential field by using Newton's laws and applied it to mobile agents.…”

Section: Potential Field Algorithmmentioning

confidence: 99%

Path Planning Based on Fuzzy Decision Trees and Potential Field

Iswanto

Wahyunggoro

Cahyadi

2016

IJECE

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The fuzzy logic algorithm is an artificial intelligence algorithm that uses mathematical logic to solve to by the data value inputs which are not precise in order to reach an accurate conclusion. In this work, Fuzzy decision tree (FDT) has been designed to solve the path planning problem by considering all available information and make the most appropriate decision given by the inputs. The FDT is often used to make a path planning decision in graph theory. It has been applied in the previous researches in the field of robotics, but it still shows drawbacks in that the robot will stop at the local minima and is not able to find the shortest path. Hence, this paper combines the FDT algorithm with the potential field algorithm. The potential field algorithm provides weight to the FDT algorithm which enables the robot to successfully avoid the local minima and find the shortest path.

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Path planning and formation control via potential function for UAV Quadrotor

Cited by 26 publications

References 13 publications

Geocaching-inspired Resilient Path Planning for Drone Swarms

Geocaching-inspired Resilient Path Planning for Drone Swarms

A flexible hardware-in-the-loop architecture for UAVs

Path Planning Based on Fuzzy Decision Trees and Potential Field

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