Toward indoor flying robots

Nicoud, Jean-Daniel; Zufferey, Jean-Christophe

doi:10.1109/irds.2002.1041486

Cited by 43 publications

(23 citation statements)

References 7 publications

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“…For instance, position and orientation of simple vision sensors could be left to evolutionary control [Cliff and Miller, 1996]. Eventually, our goal is to apply this approach to winged microflyers [Nicoud and Zufferey, 2002 instead of airships.…”

Section: Discussionmentioning

confidence: 99%

Flying over the reality gap: From simulated to real indoor airships

et al. 2006

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Because of their ability to naturally float in the air, indoor airships (often called blimps) constitute an appealing platform for research in aerial robotics. However, when confronted to long lasting experiments such as those involving learning or evolutionary techniques, blimps present the disadvantage that they cannot be linked to external power sources and tend to have little mechanical resistance due to their low weight budget. One solution to this problem is to use a realistic flight simulator, which can also significantly reduce experimental duration by running faster than real time. This requires an efficient physical dynamic modelling and parameter identification procedure, which are complicated to develop and usually rely on costly facilities such as wind tunnels. In this paper, we present a simple and efficient physics-based dynamic modelling of indoor airships including a pragmatic methodology for parameter identification without the need for complex or costly test facilities. Our approach is tested with an existing blimp in a vision-based navigation task. Neuronal controllers are evolved in simulation to map visual input into motor commands in order to steer the flying robot forward as fast as possible while avoiding collisions. After evolution, the best individuals are successfully transferred to the physical blimp, which experimentally demonstrates the efficiency of the proposed approach.

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Section: Discussionmentioning

confidence: 99%

Flying over the reality gap: From simulated to real indoor airships

et al. 2006

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“…Finding the highest thrust to weight ratio is one of the most important challenges in micro VTOL design (Nicoud and Zufferey, 2002). Our approach was firstly to specify the application requirements in terms of thrust, energy and overload allowed.…”

Section: Propulsion Group Evaluation and Design Procedurementioning

confidence: 99%

Towards Autonomous Indoor Micro VTOL

2005

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Abstract. Recent progress in sensor technology, data processing and integrated actuators has made the development of miniature flying robots fully possible. Micro VTOL 1 systems represent a useful class of flying robots because of their strong capabilities for small-area monitoring, building exploration and intervention in hostile environments. In this paper, we emphasize the importance of the VTOL vehicle as a candidate for the high-mobility system emergence. In addition, we describe the approach that our lab 2 has taken to micro VTOL evolving towards autonomy and present the mechanical design, dynamic modelling, sensing, and control of our indoor VTOL autonomous robot OS4.3

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“…Utilization of remote sensing in multirotor with low power and light weight has started to be developed [3]. Including the development of a dual rotor helicopter with omnidirectional camera [4] the use of visuals to land a helicopter while avoiding the obstacle is also being developed [5].…”

Section: Introductionmentioning

confidence: 99%

The use of video processing for quadrotor flight stability control monitoring

Dharmawan¹,

Ashari²,

Putra³

et al. 2016

AIP Conference Proceedings

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Abstract. The quadrotor is one kind of Unmanned Aerial Vehicles (UAV). Quadrotor has the ability to hover with minimal translational velocity approaching a stationary state. This capability is supported by the four rotors. These rotors are used to lift the Quadrotor to fly. These rotors are placed on all four sides of the tip of Quadrotor. In order to fly with good stability, we can use an IMU sensor (Inertial Measurement Unit). Imu sensor consists of some DOF (degrees of freedom) sensors, such as 3-axis accelerometer sensor, 3-axis gyroscope sensor, 3-axis magnetometer sensor, and so on in accordance with the needs of flight. To test the stability of Quadrotor can be done by utilizing the video and image processing methods. This processing act as the 'eyes' of Quadrotor. Sobel method as one of the image processing algorithms can be used to read the edges of the object. This method can measure the level of stability fly. But before reading the results of the edge must first be converted to black and white format. Otsu method can be used to perform the conversion. Then we find the center point of the result of the conversion of the object being viewed. This point can be used to read the movement of Quadrotor. It is used to determine the position of the quadrotor movement on vertical and horizontal axes. The position can be used as input to control the quadrotor flight stability.

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Toward indoor flying robots

Cited by 43 publications

References 7 publications

Flying over the reality gap: From simulated to real indoor airships

Flying over the reality gap: From simulated to real indoor airships

Towards Autonomous Indoor Micro VTOL

The use of video processing for quadrotor flight stability control monitoring

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