Phenox: Zynq 7000 based quadcopter robot

Konomura, Ryo; Hori, Koichi

doi:10.1109/reconfig.2014.7032546

Cited by 13 publications

(6 citation statements)

References 7 publications

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“…Inner loop control is used to control the speed at body coordinates. The method used in the inner loop is pole placement state feedback using the model equation in (3). To obtain the gain K, a target pole is needed which is the desired performance.…”

Section: A Inner Loop Control Using Pole Placement State Feedbackmentioning

confidence: 99%

“…Robots, which initially consisted of only mechanical systems, are now developing into three components: electronics, mechanics, and programming or brainware. Robots have various types based on how they move, using legs (humanoid) [2], wheels or using propellers to fly [3]. One type of robot that will be proposed in this manuscript is the omni wheels robot.…”

Section: Introductionmentioning

confidence: 99%

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Movement Control of Three Omni-Wheels Robot using Pole Placement State Feedback and PID Control

Swadexi Istiqphara,

Ulya Darajat,

Fahmizal

et al. 2023

JFSC

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Omni wheels robot have high efficiency in robot movement, but also have its own challenges in controlling this robot, especially robots with three omni wheels (3WD). This paper proposes a robot control system using the pole placement state feedback method to control the inner loop which controls the body reference speed, and PID method is used to control the outer loop which controls the robot's position in world coordinates. The results obtained using computer simulations show that the proposed method is able to control the robot's position in world coordinates even though there is an overshoot of 5- 10% on X and yaw axis, however the Y axis performance is overshoot more than 100% because of coupled effect thats still cannot be eliminated.

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Section: A Inner Loop Control Using Pole Placement State Feedbackmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Movement Control of Three Omni-Wheels Robot using Pole Placement State Feedback and PID Control

Swadexi Istiqphara,

Ulya Darajat,

Fahmizal

et al. 2023

JFSC

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“…Thus, it provides a feasible solution for control and processing systems of small-scale autonomous mobile robots. 28 However, as far as we know, few studies were made to fully tap its potential in robotic vision. Focusing on the application problem of the amphibious spherical robot, a Zynq-7000 SoC was used to construct the robotic tracking system in this article.…”

Section: Low-power Real-time Visual Tracking Systemsmentioning

confidence: 99%

A system on chip-based real-time tracking system for amphibious spherical robots

Guo

Pan

et al. 2017

International Journal of Advanced Robotic Systems

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Aiming at vision applications of our amphibious spherical robot, a real-time detection and tracking system adopting Gaussian background model and compressive tracking algorithm was designed and implemented in this article. Considering the narrow load space, the limited power resource and the specialized application scenarios of the robot, a heterogeneous computing architecture combining advanced Reduced Instruction-Set Computer (RISC) machine and field programmable gate array was proposed on the basis of Zynq-7000 system on chip.Under the architecture, main parts of the vision algorithms were implemented as software programs running on the advanced RISC machine-Linux subsystem. And customized image accelerators were deployed on the field programmable gate array subsystem to speed up the timeconsuming processes of visual algorithms. Moreover, dynamic reconfiguration was used to switch accelerators online for reducing resource consumption and improving system adaptability. The word length of accelerators was optimized with simulated annealing algorithm to make a compromise between calculation accuracy and resource consumption. Experimental results confirmed the feasibility of the proposed architecture. The single board tracking system was able to provide an image processing rate of up to 89.2 frames per second at the resolution of 320 Â 240, which could meet future demands of our robot in biological monitoring and multi-target tracking.

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“…The Phenox 2 (Phenox Lab, referred to as MAV in this article) MAVs were selected because they are light-weight, they have high computing performance for vision-based self-localization, and they can be extended by attaching odor sensors and enabling control program flexibility [13,16]. Figure 1 shows the Phenox 2, which flies using four electric motors and has a diameter of 176 mm.…”

Section: Micro Air Vehiclesmentioning

confidence: 99%

“…The front camera is unused for controlling the MAV. The FPGA conducts image processing of feature point detection based on the FAST algorithm [13,20] and feature point description based on the BRIEF algorithm [21]. CPU1 calculates the current position based on the feature points for self-localization, and it notifies CPU0 of the current position.…”

Section: Micro Air Vehiclesmentioning

confidence: 99%

Autonomous Micro-Air-Vehicle Control Based on Visual Sensing for Odor Source Localization

Kurotsuchi¹,

Tai²,

Takahashi³

2017

Adv. sci. technol. eng. syst. j.

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Phenox: Zynq 7000 based quadcopter robot

Cited by 13 publications

References 7 publications

Movement Control of Three Omni-Wheels Robot using Pole Placement State Feedback and PID Control

Movement Control of Three Omni-Wheels Robot using Pole Placement State Feedback and PID Control

A system on chip-based real-time tracking system for amphibious spherical robots

Autonomous Micro-Air-Vehicle Control Based on Visual Sensing for Odor Source Localization

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