Cooperative rendezvous of ground vehicle and aerial vehicle using model predictive control

Persson, Linnéa; Muskardin, Tin; Wahlberg, Bo

doi:10.1109/cdc.2017.8264069

Cited by 21 publications

(7 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Not only is this a chicken-and-egg problem, where the relative position of the arrest system, at the time of recovery, is needed to calculate the time it takes to fly to it (which again is needed to predict the relative position of the arrest system at the time of recovery), but it is also highly dependent on the dynamics of the arrest system. The arrest system can either have accurate, actively controlled motion, such as [8][9][10], which calls for synchronization between the UAV and arrest system, or be passively attached to a moving platform without accurate control, such as a moving ship [4]. Particularly for recovery in smaller arrest systems in space-restricted environments, using less agile UAVs with smaller error margins, good predictions of the arrest system motion will be more important, but the prediction quality naturally depends on how well the arrest system dynamics can be modeled.…”

Section: Motion Predictionmentioning

confidence: 99%

See 1 more Smart Citation

Control System Architecture for Automatic Recovery of Fixed-Wing Unmanned Aerial Vehicles in a Moving Arrest System

Gryte

Sollie

Johansen

2021

J Intell Robot Syst

View full text Add to dashboard Cite

Automatic recovery is an important step in enabling fully autonomous missions using fixed-wing unmanned aerial vehicles (UAVs) operating from ships or other moving platforms. However, automatic recovery in moving arrest systems is only briefly studied in the research literature, and is not yet an option when using low-cost, commercial off-the-shelf (COTS) autopilots. Acknowledging the reliability and low cost of COTS avionics, this paper adds recovery functionality as a modular extension based on non-intrusive additions to an autopilot with very general assumptions on its interface. This is achieved by line-of-sight guidance, which sends an augmented desired position to the autopilot, to ensure line-following along a virtual runway that guides the UAV into the arrest system. The translation and rotation of this line is determined by the pose of the arrest system, determined using two Global Navigation Satellite System (GNSS) receivers, where one is configured as a Real-Time Kinematic (RTK) base station. The relative position of the UAV and arrest system is also precisely estimated using RTK GNSS. Through extensive field testing, on two different fixed-wing UAVs, the system has shown its performance and reliability; 43 recovery attempts in a stationary net hit 0.01 ± 0.25m to the right and 0.07 ± 0.20m below the target in calm conditions. Further, 15 recoveries in a barge-mounted, ship-towed net hit 0.06 ± 0.53m to the right and 0.98 ± 0.27m below the target in winds up to 4 m/s. The remaining error is largely systematic, caused by communication delays, and could be reduced with more integral effect or through direct compensation.

show abstract

Section: Motion Predictionmentioning

confidence: 99%

“…Net recovery flying into a tensioned, fixed net that absorbs the kinetic energy of the impact either vertically [5][6][7], horizontally mounted on the roof of a moving car [8,9], or suspended between two multirotor UAVs [10].…”

Section: Introductionmentioning

confidence: 99%

Control System Architecture for Automatic Recovery of Fixed-Wing Unmanned Aerial Vehicles in a Moving Arrest System

Gryte

Sollie

Johansen

2021

J Intell Robot Syst

View full text Add to dashboard Cite

show abstract

“…This because the mathematical model of a multirotor requires fewer unknown parameters and is easier to work with when compared to the fixed-wing aircraft. [36][37][38] The details of mathematical model of the fixed-wing will be discussed further in the upcoming section. The simulation work using a MPC on a blended-fixed-wing body aircraft is presented in Reference 39.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the literature reports the simulation based work on MPC for UAVs, especially multi‐rotors. This because the mathematical model of a multirotor requires fewer unknown parameters and is easier to work with when compared to the fixed‐wing aircraft 36‐38 . The details of mathematical model of the fixed‐wing will be discussed further in the upcoming section.…”

Section: Introductionmentioning

confidence: 99%

Disturbance rejection enhancement using predictive control for the fixed‐wing UAV with multiple ailerons

Sattar

Wang

Ansari

et al. 2023

Adaptive Control & Signal

View full text Add to dashboard Cite

Summary The performance of small fixed‐wing unmanned aerial vehicles (UAVs) is easily degraded by exogenous disturbances. In an attempt to improve the performance, the structural change is made in conventional small fixed‐wing UAV through segmentation of each aileron control surface into multiples. Detailed system identification experiments are performed on each aileron pair in the wind tunnel to acquire linear dynamic models for the roll attitude of the UAV. These experiments have provided the transfer functions for the aileron control surfaces based on corresponding frequency response. Three distinctive model predictive controllers are designed and deployed in real‐time hardware to achieve the roll attitude control. The roll attitude control experiments are validated in wind tunnel under both normal and turbulent environments. The results show that multiple input and single output control system provides significant improvement in the roll attitude and disturbance rejection performance. The collective actuation of multiple control surfaces improves roll stability by 10.1%$$ 10.1\% $$ to 67.87%$$ 67.87\% $$ when compared to the single aileron pair based conventional control in the presence of turbulent flight conditions.

show abstract

“…The fluctuating water surface causes unpredictable changes in the position and attitude of the unmanned vessel. Landing the UAV on the UGV can give the UGV system the ability to work in a three-dimensional space, and carrying the charging module on the UGV can solve the limitations of payload and flight time on the working range of the UAV, but it places high requirements on the accuracy of the drone landing [16][17][18][19]. The position and attitude of a UAV are estimated during the landing process, but the position and attitude of today's UAVs are typically measured by inertial measurement units (IMUs) and Global Positioning System (GPS) [20].…”

Section: Introductionmentioning

confidence: 99%

An Autonomous Tracking and Landing Method for Unmanned Aerial Vehicles Based on Visual Navigation

Wang,

Ma,

Zhu

et al. 2023

Drones

View full text Add to dashboard Cite

In this paper, we examine potential methods for autonomously tracking and landing multi-rotor unmanned aerial vehicles (UAVs), a complex yet essential problem. Autonomous tracking and landing control technology utilizes visual navigation, relying solely on vision and landmarks to track targets and achieve autonomous landing. This technology improves the UAV’s environment perception and autonomous flight capabilities in GPS-free scenarios. In particular, we are researching tracking and landing as a cohesive unit, devising a switching plan for various UAV tracking and landing modes, and creating a flight controller that has an inner and outer loop structure based on relative position estimation. The inner and outer nested markers aid in the autonomous tracking and landing of UAVs. Optimal parameters are determined via optimized experiments on the measurements of the inner and outer markers. An indoor experimental platform for tracking and landing UAVs was established. Tracking performance was verified by tracking three trajectories of an unmanned ground vehicle (UGV) at varying speeds, and landing accuracy was confirmed through static and dynamic landing experiments. The experimental results show that the proposed scheme has good dynamic tracking and landing performance.

show abstract

Cooperative rendezvous of ground vehicle and aerial vehicle using model predictive control

Cited by 21 publications

References 14 publications

Control System Architecture for Automatic Recovery of Fixed-Wing Unmanned Aerial Vehicles in a Moving Arrest System

Control System Architecture for Automatic Recovery of Fixed-Wing Unmanned Aerial Vehicles in a Moving Arrest System

Disturbance rejection enhancement using predictive control for the fixed‐wing UAV with multiple ailerons

An Autonomous Tracking and Landing Method for Unmanned Aerial Vehicles Based on Visual Navigation

Contact Info

Product

Resources

About