Autonomous takeoff and landing of an unmanned aerial vehicle

Masuko, Kouji; Takahashi, Ippei; Ogawa, Shuichi; Wu, Meng-Hung; Oosedo, Atsushi; Matsumoto, Takaaki; Go, Kenta; Sugai, Fumihito; Konno, Atsushi; Uchiyama, Masaru

doi:10.1109/sii.2010.5708333

Cited by 9 publications

(5 citation statements)

References 7 publications

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“…In (13),v j i is the correction parameter,X i is the point i in 3D space,P j is the projection matrix of the jth view andλ j i is a scaling parameter. This method adjusts these parameters such that the difference between re-projected and originally detected points in the image plane can be minimised min…”

Section: Single-cameramentioning

confidence: 99%

“…These questions are solved by interpreting the information retrieved by sensors like global positioning system (GPS), 4-6 lasers [7][8][9][10][11][12] and ultrasonic sensors. [13][14][15][16] Currently, one of the most suitable sensors is the stereo camera because of its small size and low weight, as well as algorithms that permit three-dimensional (3D) scene interpretation that can run in real time on small-sized computers. The position of the MUAV in a map can be estimated from the information retrieved by a camera.…”

Section: Introductionmentioning

confidence: 99%

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A survey on stereo vision-based autonomous navigation for multi-rotor MUAVs

Sanchez-Rodriguez¹,

Aceves-López²

2018

Robotica

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SUMMARYThis paper presents an overview of the most recent vision-based multi-rotor micro unmanned aerial vehicles (MUAVs) intended for autonomous navigation using a stereoscopic camera. Drone operation is difficult because pilots need the expertise to fly the drones. Pilots have a limited field of view, and unfortunate situations, such as loss of line of sight or collision with objects such as wires and branches, can happen. Autonomous navigation is an even more difficult challenge than remote control navigation because the drones must make decisions on their own in real time and simultaneously build maps of their surroundings if none is available. Moreover, MUAVs are limited in terms of useful payload capability and energy consumption. Therefore, a drone must be equipped with small sensors, and it must carry low weight. In addition, a drone requires a sufficiently powerful onboard computer so that it can understand its surroundings and navigate accordingly to achieve its goal safely. A stereoscopic camera is considered a suitable sensor because of its three-dimensional (3D) capabilities. Hence, a drone can perform vision-based navigation through object recognition and self-localise inside a map if one is available; otherwise, its autonomous navigation creates a simultaneous localisation and mapping problem.

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Section: Single-cameramentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A survey on stereo vision-based autonomous navigation for multi-rotor MUAVs

Sanchez-Rodriguez¹,

Aceves-López²

2018

Robotica

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“…The route from the Entry Points to the Waypoints where the aircraft began their descent is the critical part. There are studies for the descent [8,9].…”

Section: Generalizing the Assumptionsmentioning

confidence: 99%

“…If experiments are performed locally for a global requirement, then it is necessary to combine the local confidence levels to obtain the overall confidence level. The necessary result is derived in [9] Theorem: Suppose the parameter θ j has confidence interval [α j, β j] with confidence level 100(1h j) %. Then the vector parameter (θ 1 , θ 2 , … , θ n ) has the vector confidence interval ( [ α 1 , β 1 ] , [ α 2 , β 2 ] , … , [ α n , β n ] ) with an overall confidence level of 100( 1h 1h 2 …h n )% .…”

Section: Safety Analysismentioning

confidence: 99%

An Elementary Algorithm for Autonomous Air Terminal Merging and Interval Management

White

2017

AIAA Modeling and Simulation Technologies Conference

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A central element of air traffic management is the safe merging and spacing of aircraft during the terminal area flight phase. This paper derives and examines an algorithm for the merging and interval managing problem for Standard Terminal Arrival Routes. It describes a factor analysis for performance based on the distribution of arrivals, the operating period of the terminal, and the topology of the arrival routes; then presents results from a performance analysis and from a safety analysis for a realistic topology based on typical routes for a runway at Phoenix International Airport. The heart of the safety analysis is a statistical derivation on how to conduct a safety analysis for a local simulation when the safety requirement is given for the entire airspace I. INTRODUCTION This study develops and examines an algorithm for the merging and interval managing problem for Standard Terminal Arrival Routes (STARS). Once the algorithm is derived, there are two parts to this study. The first part consists of a factor analysis for performance that considers the distribution of arrivals, the operating period of the terminal, and the topology of the arrival routes. The second part conducts a performance analysis and a safety analysis for a realistic topology based on the routes for one of the runways at Phoenix International Airport. The overall objective is to increase understanding of air traffic by creating algorithms and models. The merging and interval management of aircraft in a terminal appears to be a topic that can evolve into other areas. Secondary objectives include:  Simulation for part of or all of the national airspace-such simulations can contribute to more efficient and safer routing and operations, and such simulations depend on accurate and efficient models of terminal operations.  Operation of autonomous aircraftautonomous aircraft can perform routine and boring tasks such as monitoring a forest for fires, and they can perform dangerous tasks such as crop dusting. We are more willing to send an autonomous craft instead of a piloted airplane into hazardous conditions. In the near future, we may see the autonomous operation of cargo carriers.  Layout of arrival routesthere may or may not be two different criteria. One is the efficient and safe conduction of autonomous procedures. The other is to ease the burden of an air traffic controller. For air traffic controllers, our conjecture is that easing the burden leads to more efficient and safer operation.  Extension to traffic outside the terminal areain the material below, when an aircraft arrives at the terminal area, the algorithm computes whether or not it can enter the queue. One possibility is the converse of this where the algorithm computes the range of arrival speeds and arrival times that allow it to enter the queue. This can be applied to aircraft in transit. It can possibly be extended to cover the entire flight including takeoff time.

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“…The landing control of a helicopter [8] can be considered under such situation. If the ground landing target is moving during the aerial vehicle landing, the landing strategy could adopt the gliding down to the landing platform [9,10]. Through minimizing the relative pose and speed between the aerial vehicle and the mobile landing platform, the aerial vehicle can be controlled to approach the landing platform and smoothly landing on it.…”

Section: Introductionmentioning

confidence: 99%

Visual servoing of micro aerial vehicle landing on ground platform

Huang

Hung

2014

2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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This paper presents the visual servoing methodology that lands a micro aerial vehicle on a ground platform in the indoor environments. Since the space in the indoor environment is usually limited, both of the size of micro aerial vehicle and landing platform has to be small. Thus, the visual servoing based on two pin-hole cameras mounted on the front and belly of micro aerial vehicle is suitable for such scenario. The visual feature extraction of the landing platform is proposed to detect the specific image pattern on it. The flying control strategies with the camera switching are developed to search and approach the landing platform. The aligning control during the landing procedure is designed by the fuzzy logic control with the target position error on image. The overall performance of the landing algorithm has been validated in the online experiments.

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Autonomous takeoff and landing of an unmanned aerial vehicle

Cited by 9 publications

References 7 publications

A survey on stereo vision-based autonomous navigation for multi-rotor MUAVs

A survey on stereo vision-based autonomous navigation for multi-rotor MUAVs

An Elementary Algorithm for Autonomous Air Terminal Merging and Interval Management

Visual servoing of micro aerial vehicle landing on ground platform

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