Robust quadcopter control with artificial vector fields

Rezende, Adriano M. C.; Gonçalves, Vinícius Mariano; Nunes, Arthur H. D.; Pimenta, Luciano C. A.

doi:10.1109/icra40945.2020.9196605

Cited by 15 publications

(27 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the Alpha Pilot and AIRR challenges, it is considered the presence of a PID controller to guarantee the desired angular velocities for the quadrotor. In this context, Rezende et al [46] propose a nonlinear pathfollowing method based on artificial vector fields to control a quadrotor on the Acro mode. In the context of robot navigation, an artificial vector field is a function F(p) : R n → R n that is used to guide the robot.…”

Section: Controlmentioning

confidence: 99%

“…The designing methodology proposed by Gonçalves et al [17] requires a representation of the curve as the intersection of zero level sets of two functions α 1 (p) and α 2 (p), a hard task for paths with a generic shape. Here, we incorporate a novel numerical method to compute the surfaces required to compute the field presented by Gonçalves et al [17] and use the strategy by Rezende et al [46] to control the drone.…”

Section: Controlmentioning

confidence: 99%

“…The quadcopter used in the drone racing challenge operates in the Acro mode, i.e., the control inputs are the thrust force and the angular body rates. In this work, a nonlinear model based on the proposed by Rezende et al [46] will be considered. It is given by:…”

Section: Quadcopter Modelmentioning

confidence: 99%

“…The strategy adopted to control the quadcopter along the planned trajectories is based on artificial vector fields [17,46]. In the three-dimensional space, a vector field is a function F(p) : R 3 → R 3 that represents a velocity reference F for each position p. This reference will guide the navigation of the drone.…”

Section: Controlmentioning

confidence: 99%

“…However, the current paper includes other localization strategies using visual perception and odometry and several other systems' technical upgrades. The proposed improvements include: (i) a localization strategy based on gates perception, which allows the drone to improve its pose estimate to pass through the center of the gate fast but safely; (ii) a visual odometry system, which highly improves the localization when the perception block detects no gate; (iii) a new EKF, including some bias states that allows the fusion of pose estimation of two different sources that mutually diverge; (iv) an improved version of the nonlinear controller, which counts with convergence proofs [46]; and (v) a novel method, based on the frames of the curve (as in the Frenet-Serret frames), that allows the computation of the artificial vector field [17] used by the control algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An integrated solution for an autonomous drone racing in indoor environments

Rezende

Miranda

Rezeck

et al. 2021

Intel Serv Robotics

Self Cite

View full text Add to dashboard Cite

The use of drones is becoming more present in modern daily life. One of the most challenging tasks associated with these vehicles is the development of perception and autonomous navigation systems. Competitions such as Artificial Intelligence Robotic Racing (AIRR) and Autonomous Drone Racing were launched to drive the advances in such strategies, requiring the development of integrated systems for autonomous navigation in a racing environment. In this context, this paper presents an improved integrated solution for autonomous drone racing, which focuses on simple, robust, and computationally efficient techniques to enable the application in embedded hardware. The strategy is divided into four modules: (i) A trajectory planner computes a path that passes through the sequence of desired gates; (ii) a perception system that obtains the global pose of the vehicle by using an onboard camera; (iii) a localization system which merges several sensed information to estimate the drone's states; and, (iv) an artificial vector field-based controller in charge of following the plan by using the estimated states. To evaluate the performance of the entire integrated system, we extended the FlightGoggles simulator to use gates similar to those used in the AIRR competition. A computational cost analysis demonstrates the high performance of the proposed perception method running on limited hardware commonly embedded in aerial robots. In addition, we evaluate the localization system by comparing it with ground truth and when there is a disturbance in the location of the gates. Results in a representative race circuit based on the AIRR competition showed the proposed strategy's competing performance, presenting itself as a feasible solution for drone racing systems.

show abstract

Section: Controlmentioning

confidence: 99%

Section: Controlmentioning

confidence: 99%

Section: Quadcopter Modelmentioning

confidence: 99%

Section: Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An integrated solution for an autonomous drone racing in indoor environments

Rezende

Miranda

Rezeck

et al. 2021

Intel Serv Robotics

Self Cite

View full text Add to dashboard Cite

show abstract

Robust distributed control within a curve virtual tube for a robotic swarm under self‐localization drift and precise relative navigation

Gao

Bai

Quan

2023

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

To guide the movement of a robotic swarm in a corridor-like environment, a curve virtual tube with no obstacle inside was designed in our previous work. This article generalizes the controller design to the condition that all robots have self-localization drifts and precise relative navigation, where the flocking algorithm is introduced to reduce the negative impact of the self-localization drift. Similar to the "many wrongs principle," it is shown that the cohesion behavior and the velocity alignment behavior are able to reduce the influence of the position measurement drift and the velocity measurement error, respectively. For convenience in practical use, a modified vector field controller with five control terms is put forward. Finally, the effectiveness of the proposed method is validated by numerical simulations and real experiments.

show abstract