Stereo Vision Based Obstacle Avoidance on Flapping Wing MAVs

Tijmons, Sjoerd; Croon, Guido de; Remes, B. D. W.; Wagter, Christophe De; Ruijsink, R.; Kampen, Erik-Jan van; Chu, Q.P.

doi:10.1007/978-3-642-38253-6_28

Cited by 10 publications

(8 citation statements)

References 15 publications

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“…In small confined spaces this is a problem as it limits the set of possible trajectories which can be safely undertaken and complicates the required obstacle avoidance strategies (cf. [5]). If an FWMAV has the ability to minimize the forward velocity or even hover, the navigation in small confined spaces would be significantly improved.…”

Section: Introductionmentioning

confidence: 93%

Attitude and altitude estimation and control on board a Flapping Wing Micro Air Vehicle

Verboom

Tijmons

Wagter

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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The autonomous capabilities of light-weight Flapping Wing Micro Air Vehicles (FWMAVs) have much to gain from onboard state estimation and attitude control. In this article, we present the first FWMAV with robust onboard state estimation and attitude control. The tailed FWMAV DelFly II was used, with the main goal to achieve active stabilization in the (passively unstable) hover condition. The attitude is estimated using an Inertial Measurement Unit with a gyroscope, accelerometer and magnetometer and the altitude is estimated using a barometer. A major challenge lies in the disturbance of the accelerometer measurements by the flapping motion of the wings. We propose a mechanical damping mechanism and flapcycle based filtering to resolve this issue. The pitch estimates have a mean error of 1.5 • with respect to the ground-truth measurement from a motion capture system. Using the onboard pitch estimate we can control the attitude of the FWMAV in the forward flight regime with a 30% lower standard deviation than in a trimmed flight. With a different set of gains, the FWMAV is able to perform a hovering flight -showing that a tailed FWMAV has enough control authority for this task. In a fully autonomous hover experiment, the DelFly II stays within a sphere of 0.75 m radius.

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Section: Introductionmentioning

confidence: 93%

Attitude and altitude estimation and control on board a Flapping Wing Micro Air Vehicle

Verboom

Tijmons

Wagter

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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“…1. This platform has a wingspan of 28 cm and a weight of 20 g. The Explorer version of the DelFly has a few important differences compared to its design used in previous studies ( [19], [32]). First, it has a cumston-made 1.0 g autopilot board which includes all electronics required for flight control: an electronic speed controller for its brushless motor, a transceiver for two-way communication with a remote station, and an ATmega328P 20 MHz microcontroller for onboard processing.…”

Section: System Descriptionmentioning

confidence: 99%

Obstacle Avoidance Strategy using Onboard Stereo Vision on a Flapping Wing MAV

et al. 2017

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The development of autonomous lightweight MAVs, capable of navigating in unknown indoor environments, is one of the major challenges in robotics. The complexity of this challenge comes from constraints on weight and power consumption of onboard sensing and processing devices. In this paper we propose the "Droplet" strategy, an avoidance strategy based on stereo vision inputs that outperforms reactive avoidance strategies by allowing constant speed maneuvers while being computationally extremely efficient, and which does not need to store previous images or maps. The strategy deals with nonholonomic motion constraints of most fixed and flapping wing platforms, and with the limited field-of-view of stereo camera systems. It guarantees obstacle-free flight in the absence of sensor and motor noise. We first analyze the strategy in simulation, and then show its robustness in real-world conditions by implementing it on a 20gram flapping wing MAV.

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“…Unwittingly, the bidirectional wall avoidance in a square room can result in the DelFly getting caught in and crashing into corners. There are available methods to correct for this behaviour [50,55] but as this is a conceptual error typical for human designed systems, we will keep this behaviour as is. Figure 4 shows the path of successful and failed flight realisations of DelFly with the user-defined behaviour.…”

Section: User Designed Behaviour Treementioning

confidence: 99%

Behavior Trees for Evolutionary Robotics

Scheper¹,

Tijmons²,

Visser³

et al. 2016

Artificial Life

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Abstract-Evolutionary Robotics allows robots with limited sensors and processing to tackle complex tasks by means of sensory-motor coordination. In this paper we show the first application of the Behaviour Tree framework to a real robotic platform using the Evolutionary Robotics methodology. This framework is used to improve the intelligibility of the emergent robotic behaviour as compared to the traditional Neural Network formulation. As a result, the behaviour is easier to comprehend and manually adapt when crossing the reality gap from simulation to reality. This functionality is shown by performing real-world flight tests with the 20-gram DelFly Explorer flapping wing Micro Air Vehicle equipped with a 4-gram onboard stereo vision system. The experiments show that the DelFly can fully autonomously search for and fly through a window with only its onboard sensors and processing. The success rate of the optimised behaviour in simulation is 88% and the corresponding real-world performance is 54% after user adaptation. Although this leaves room for improvement, it is higher than the 46% success rate from a tuned user-defined controller.

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Stereo Vision Based Obstacle Avoidance on Flapping Wing MAVs

Cited by 10 publications

References 15 publications

Attitude and altitude estimation and control on board a Flapping Wing Micro Air Vehicle

Attitude and altitude estimation and control on board a Flapping Wing Micro Air Vehicle

Obstacle Avoidance Strategy using Onboard Stereo Vision on a Flapping Wing MAV

Behavior Trees for Evolutionary Robotics

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