Landing Site Searching and Selection Algorithm Development Using Vision System and its Application to Quadrotor

Park, Jong-Ho; Kim, Youdan; Kim, Sung‐Wan

doi:10.1109/tcst.2014.2341214

Cited by 29 publications

(3 citation statements)

References 41 publications

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“…When a quadrotor drone performs a desired trajectory, accurate tracking is highly necessary. In trajectory tracking control, feedback linearization (FL) has been widely used [7,8]. This control method works well under the assumption of known system dynamics.…”

Section: Introductionmentioning

confidence: 99%

Iterative Learning Sliding Mode Control for UAV Trajectory Tracking

Nguyen

Phung

2021

Electronics

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This paper presents a novel iterative learning sliding mode controller (ILSMC) that can be applied to the trajectory tracking of quadrotor unmanned aerial vehicles (UAVs) subject to model uncertainties and external disturbances. Here, the proposed ILSMC is integrated in the outer loop of a controlled system. The control development, conducted in the discrete-time domain, does not require a priori information of the disturbance bound as with conventional SMC techniques. It only involves an equivalent control term for the desired dynamics in the closed loop and an iterative learning term to drive the system state toward the sliding surface to maintain robust performance. By learning from previous iterations, the ILSMC can yield very accurate tracking performance when a sliding mode is induced without control chattering. The design is then applied to the attitude control of a 3DR Solo UAV with a built-in PID controller. The simulation results and experimental validation with real-time data demonstrate the advantages of the proposed control scheme over existing techniques.

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Section: Introductionmentioning

confidence: 99%

Iterative Learning Sliding Mode Control for UAV Trajectory Tracking

Nguyen

Phung

2021

Electronics

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“…The quadrotor [1,2,3], as a branch of unmanned aerial vehicles (UAVs), is highly favored in both military and civilian applications given its vertical take-off and landing ability, insensibility to varying environments, high mobility and stability, and easy operation modes. The quadrotor system is highly-coupled, under-actuated, and inherently non-linear, which challenges the system stability involving the microprocessor, the sensor, the mechanism, and the navigation and control algorithm.…”

Section: Introductionmentioning

confidence: 99%

Novel Fuzzy PID-Type Iterative Learning Control for Quadrotor UAV

Dong

2018

Sensors

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Due to the under-actuated and strong coupling characteristics of quadrotor aircraft, traditional trajectory tracking methods have low control precision, and poor anti-interference ability. A novel fuzzy proportional-interactive-derivative (PID)-type iterative learning control (ILC) was designed for a quadrotor unmanned aerial vehicle (UAV). The control method combined PID-ILC control and fuzzy control, so it inherited the robustness to disturbances and system model uncertainties of the ILC control. A new control law based on the PID-ILC algorithm was introduced to solve the problem of chattering caused by an external disturbance in the ILC control alone. Fuzzy control was used to set the PID parameters of three learning gain matrices to restrain the influence of uncertain factors on the system and improve the control precision. The system stability with the new design was verified using Lyapunov stability theory. The Gazebo simulation showed that the proposed design method creates effective ILC controllers for quadrotor aircraft.

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“…Simulation: In this kind of trial, the proposed solution is validated by means of simulation tests, such as hardware-in-the-loop simulations (HILS), image-in-loop simulations (IILS), virtual reality environments, and numerical simulations. In all these cases, the main feature is that the flight of a model or virtual aircraft is simulated (e.g., [79][80][81][82]). With respect to the camera employed, it is a real device (physical), a virtual camera in a 3D environment or even a model that represents its behavior by providing theoretical measurements that would be obtained with computer vision.…”

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confidence: 99%

Computer Vision in Autonomous Unmanned Aerial Vehicles—A Systematic Mapping Study

2019

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Personal assistant robots provide novel technological solutions in order to monitor people’s activities, helping them in their daily lives. In this sense, unmanned aerial vehicles (UAVs) can also bring forward a present and future model of assistant robots. To develop aerial assistants, it is necessary to address the issue of autonomous navigation based on visual cues. Indeed, navigating autonomously is still a challenge in which computer vision technologies tend to play an outstanding role. Thus, the design of vision systems and algorithms for autonomous UAV navigation and flight control has become a prominent research field in the last few years. In this paper, a systematic mapping study is carried out in order to obtain a general view of this subject. The study provides an extensive analysis of papers that address computer vision as regards the following autonomous UAV vision-based tasks: (1) navigation, (2) control, (3) tracking or guidance, and (4) sense-and-avoid. The works considered in the mapping study—a total of 144 papers from an initial set of 2081—have been classified under the four categories above. Moreover, type of UAV, features of the vision systems employed and validation procedures are also analyzed. The results obtained make it possible to draw conclusions about the research focuses, which UAV platforms are mostly used in each category, which vision systems are most frequently employed, and which types of tests are usually performed to validate the proposed solutions. The results of this systematic mapping study demonstrate the scientific community’s growing interest in the development of vision-based solutions for autonomous UAVs. Moreover, they will make it possible to study the feasibility and characteristics of future UAVs taking the role of personal assistants.

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Landing Site Searching and Selection Algorithm Development Using Vision System and its Application to Quadrotor

Cited by 29 publications

References 41 publications

Iterative Learning Sliding Mode Control for UAV Trajectory Tracking

Iterative Learning Sliding Mode Control for UAV Trajectory Tracking

Novel Fuzzy PID-Type Iterative Learning Control for Quadrotor UAV

Computer Vision in Autonomous Unmanned Aerial Vehicles—A Systematic Mapping Study

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