A Tailless Flapping Wing MAV Performing Monocular Visual Servoing Tasks

Olejnik, Diana A.; Duisterhof, Bardienus P.; Karásek, Matěj; Scheper, Kirk Y. W.; Dijk, Tom van; Croon, Guido C. H. E. de

doi:10.1142/s2301385020500235

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…However, fixed wing MAVs also have limited agility in comparison to quadrotors or flapping wing MAVs. The latter, for instance, can have a very high agility (Karásek et al, 2018 ), but also comes with more limited endurance and payload constraints (Olejnik et al, 2019 ). The MAV design impacts the number and type of sensors that can be taken on-board.…”

Section: Mav Designmentioning

confidence: 99%

“…The capabilities of a vision algorithm will depend on the resolution of the on-board cameras, the number of the on-board cameras, as well as the processing power on-board. On very lightweight MAVs, such as flapping wings, even carrying a small stereo-camera can be challenging (Olejnik et al, 2019 ). A further known disadvantage of vision is the limited Field of View (FOV) of cameras.…”

Section: Local Ego-state Estimation and Controlmentioning

confidence: 99%

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A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints

et al. 2020

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This work presents a review and discussion of the challenges that must be solved in order to successfully develop swarms of Micro Air Vehicles (MAVs) for real world operations. From the discussion, we extract constraints and links that relate the local level MAV capabilities to the global operations of the swarm. These should be taken into account when designing swarm behaviors in order to maximize the utility of the group. At the lowest level, each MAV should operate safely. Robustness is often hailed as a pillar of swarm robotics, and a minimum level of local reliability is needed for it to propagate to the global level. An MAV must be capable of autonomous navigation within an environment with sufficient trustworthiness before the system can be scaled up. Once the operations of the single MAV are sufficiently secured for a task, the subsequent challenge is to allow the MAVs to sense one another within a neighborhood of interest. Relative localization of neighbors is a fundamental part of self-organizing robotic systems, enabling behaviors ranging from basic relative collision avoidance to higher level coordination. This ability, at times taken for granted, also must be sufficiently reliable. Moreover, herein lies a constraint: the design choice of the relative localization sensor has a direct link to the behaviors that the swarm can (and should) perform. Vision-based systems, for instance, force MAVs to fly within the field of view of their camera. Range or communication-based solutions, alternatively, provide omni-directional relative localization, yet can be victim to unobservable conditions under certain flight behaviors, such as parallel flight, and require constant relative excitation. At the swarm level, the final outcome is thus intrinsically influenced by the on-board abilities and sensors of the individual. The real-world behavior and operations of an MAV swarm intrinsically follow in a bottom-up fashion as a result of the local level limitations in cognition, relative knowledge, communication, power, and safety. Taking these local limitations into account when designing a global swarm behavior is key in order to take full advantage of the system, enabling local limitations to become true strengths of the swarm.

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Section: Mav Designmentioning

confidence: 99%

Section: Local Ego-state Estimation and Controlmentioning

confidence: 99%

A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints

et al. 2020

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“…For decades, MAV has received considerable critical attention and has synchronously developed in two fields, including microaircrafts based on bionic design and cyborg insects whose locomotion are controlled by artificial stimulation modules. Due to inheriting the senor organs and athletic ability of the insect carriers, cyborg insects remedy the technical limitations of bionic aircrafts in terms of deficient sensing equipment [6,7], poor adaptability in an unstructured environment [8], and difficulty in continuous power supply [9]. Moreover, cyborg insects have incomparable application potential in biodiversity protection.…”

Section: Introductionmentioning

confidence: 99%

Experimental Verification on Steering Flight of Honeybee by Electrical Stimulation

Zhao

et al. 2022

Cyborg Bionic Syst

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The artificial locomotion control strategy is the fundamental technique to ensure the accomplishment of the preset assignments for cyborg insects. The existing research has recognized that the electrical stimulation applied to the optic lobes was an appropriate flight control strategy for small insects represented by honeybee. This control technique has been confirmed to be effective for honeybee flight initiation and cessation. However, its regulation effect on steering locomotion has not been fully verified. Here, we investigated the steering control effect of honeybee by applying electrical stimulation signals with different duty cycles and frequencies on the unilateral optic lobes and screened the stimulus parameters with the highest response successful rate. Moreover, we confirmed the effectiveness of steering control by verifying the presence of rotation torque on tethered honeybees and the body orientation change of crawling honeybees. Our study will contribute some reliable parameter references to the motion control of cyborg honeybees.

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“…Dougherty et al (2014) calculated the relative position by combining laser sensor data with vision sensor data and he designed a landing trajectory-tracking controller to guide the quadrotor. Instead of calculating the 3D position information from the images, the pixel deviation is directly used in the image-based method (Lee et al, 2012; Wynn and McLain, 2019; Olejnik et al, 2020). Lee et al (2012) calculate the image Jacobian matrix using the camera pinhole model.…”

Section: Introductionmentioning

confidence: 99%

Autonomous deck landing of a vertical take-off and landing unmanned aerial vehicle based on the tau theory

Wang

Lin

Tang

et al. 2022

Transactions of the Institute of Measurement and Control

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The research on autonomous landing of vertical take-off and landing (VTOL) unmanned aerial vehicles (UAVs) is well established. However, the research on autonomous deck landing using visual methods is relatively not so mature and many of them require the support of ground infrastructures. In order to reduce such dependencies, a ship landing guidance strategy based on on-board vision is studied. Considering the characteristics of the ship landing issue, we propose a three-phase landing scheme and a decision-making method to ensure landing safety is also studied. For improving the traditional two-dimensional (2D) optical-flow method, a three-dimensional (3D) velocity vector estimation method using image spherical optical flow is studied. Furthermore, a guidance law based on the tau theory is employed by only using the visual information of the line of sight to the target. In this phase, a trajectory-tracking controller is applied to generate the velocity commands of the UAV. Finally, the whole algorithm is validated by simulation in different wave conditions developed with Unity3D. Compared with traditional trajectory planning methods, our method does not require complex optimization iterations and can meet both the real-time and accuracy requirements of deck landing. The average tracking error of our method maintains in 0.2 m. Moreover, the whole algorithm runs efficiently at around 30 fps on a Raspberry-Pi 3B+ microcomputer which meets the real-time requirements.

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A Tailless Flapping Wing MAV Performing Monocular Visual Servoing Tasks

Cited by 7 publications

References 14 publications

A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints

A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints

Experimental Verification on Steering Flight of Honeybee by Electrical Stimulation

Autonomous deck landing of a vertical take-off and landing unmanned aerial vehicle based on the tau theory

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